{"paper_id":"44ea87ce-5f48-4bfa-987a-79b22b377b9b","body_text":"Association of dynamic changes of hemoglobin-to-red blood cell distribution width ratio with all-cause mortality in patients with intracerebral hemorrhage | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of dynamic changes of hemoglobin-to-red blood cell distribution width ratio with all-cause mortality in patients with intracerebral hemorrhage Yanqun Huang, Hui Liang, Senhu Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6634211/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aimed to evaluate the time-dependent prognostic value of HRR for all-cause mortality in ICH patients. We included 2,447 ICH patients from the MIMIC-IV 3.1 database. Cox regression assessed HRR-mortality associations, while restricted cubic spline model evaluated non-linear relationships. Serial HRR trends were analyzed using temporal Pearson correlation analyses and ROC curves, with the optimal cutoff identified via surv_cutpoint. Results demonstrated a dynamic inverse association with all-cause mortality in ICH patients, with higher baseline HRR independently linked to an 88.5% reduced mortality risk. Both survivors and non-survivors exhibited progressive HRR declines during hospitalization, though non-survivors showed a steeper 14-day trajectory (0.835 to 0.553 vs. 0.919 to 0.710 in survivors, P < 0.001 for trend) and a daily decrease rate of -0.014 (r = -0.971). Consistent HRR declines across all subgroups. Daily HRR levels inversely correlated with mortality risk throughout hospitalization (adjusted HRs <1.0 at all time points, P < 0.05), with discharge HRR achieving peak discriminative accuracy (AUC = 0.763). A baseline HRR cutoff ≤0.74 identified high-risk patients with 25.14% mortality. HRR may serve as a dynamic prognostic indicator for ICH mortality risk stratification. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Neurology/Neurological disorders/Cerebrovascular disorders Hemoglobin-to-red blood cell distribution width ratio (HRR) Dynamic changes Intracerebral hemorrhage Hospital mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Intracerebral hemorrhage (ICH), caused by ruptured intracranial vessels leading to blood extravasation into brain tissue 1 , 2 ‌, represents the most devastating stroke subtype, accounting for 15–20% of all strokes and driving disproportionately high rates of acute mortality, long-term disability and socioeconomic burden 2 . Over recent decades, shifting vascular risk profiles‌ have contributed to rising ICH incidence 3,4 ,and while fewer than 50% of patients survive beyond one year, with survivors often facing permanent functional impairments and catastrophic healthcare costs 1 . This dire prognosis underscores the critical need for dynamic biomarker monitoring during hospitalization to enable real-time risk stratification and targeted interventions‌. Recent research has focused on the identification of readily measurable biological markers capable of reliably assessing patient prognosis 5, 6 . Their routine availability, low cost, and strong prognostic value make them valuable tools in both clinical and public health settings. Some studies aimed to discover prognostic biomarkers for stratifying high-risk ICH cohorts and guiding clinical decisions. 7 , 8 . Routine blood markers are gaining clinical relevance due to easy availability and their ability to reflect various health and disease states, especially hemoglobin (Hb) and red cell distribution width (RDW), which been proven to be two key blood parameters for ICH patients 9 , 10 . Hb serves as a quantitative indicator of erythrocytic oxygen-carrying capacity. Hb depletion demonstrates significant correlation not merely with anemia progression but also functions as an independent prognostic indicator across multisystem chronic pathologies. Studies have shown that low Hb levels have been associated with adverse outcomes in cardiovascular and cerebrovascular diseases, such as acute coronary syndrome 11 , heart failure 12 , and ischemic stroke 13 . Particularly, while elevated Hb is associated with protective effects in spontaneous ICH, low levels may worsen tissue damage after bleeding 9 . RDW measures red blood cell size variation, with higher values suggesting inflammation or oxidative stress. Elevated RDW has been linked to a greater incidence of various type of stroke, including ischemic stroke 14 , 15 and ICH 10 , 16 . RDW independently predicted long-term mortality and median RDW levels within the first month after admission were better predictors of long-term mortality than RDW levels on admission 17 . Furthermore, complex interactions between Hb and RDW have been documented 18 – 20 , suggesting their interdependent relationship. The hemoglobin-to-red cell distribution width ratio (HRR) has emerged as an innovative combined indicator, which offers the key advantage of evaluating both blood oxygen transport efficiency and associated erythropoietic stress or impairment 21 , influencing the progression of various diseases 20 , 22 . A study demonstrated a significant inverse correlation between HRR and stroke incidence, with each unit increment in HRR corresponding to a 58% reduction in stroke risk 23 . Besides, previous research has shown that a lower HRR was significantly associated with an increased risk of mortality in various cardiovascular and cerebrovascular diseases, including heart failure 24 , 25 and acute ischemic stroke 26 , 27 . Despite preliminary evidence linking HRR to stroke risks and clinical outcomes, the critical temporal association between dynamic HRR trajectories during hospitalization and mortality risk in ICH patients remains unclear. Real-time monitoring and analysis of HRR fluctuations may provide critical prognostic insights, as these variations reflect metabolic instability and disease severity. Such monitoring enables timely interventions to prevent acute complications, thereby potentially reducing ICH-related mortality rates. Therefore, this study aimed to investigate the association between HRR and in-hospital mortality of ICH utilizing data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV, version 3.1) database. Furthermore, we explored the temporal changes in HRR during hospitalization to evaluate its potential as a predictive clinical indicator. Methods Cohort selection This retrospective study analyzed data from MIMIC-IV 3.1, which is a publicly available critical care database maintained by the Massachusetts Institute of Technology. The MIMIC-IV 3.1 database comprises more than 220 thousand patients’ demographics, vital signs, laboratory indicators, and diagnoses using International Classification of Diseases and Ninth Revision (ICD-9) and Tenth Revision (ICD-10) codes. One author (Yanqun Huang) obtained database access following Institutional Review Board approval (certification number: 39674606). We identified ICH patients using ICD-9 codes (430–432) and ICD-10 codes (I60-I62). Among 223,452 adult patients in the MIMIC-IV 3.1 database, 217,300 without ICH were excluded first. After excluding 121 patients with a hospital stay duration of less than 2 days, we further excluded 3,524 patients with missing Hb and RDW data on the first day of admission, followed by 60 patients with fewer than 2 HRR measurements during hospitalization. Finally, a total of 2,447 patients with ICH were included in the cohort and divided into four groups based on quartiles of baseline HRR values at admission (Fig. 1 ). Feature selection Baseline characteristics at admission included demographics (age, sex, marital status and race), hospitalization details (admission type, length of stay), 16 comorbidities (prevalence ≥ 300 patients; e.g., hypertension, diabetes, cerebral infarction, cerebral cysts, dementia, paralysis, peripheral vascular disease [PVD], myocardial infarction, heart failure, other heart disease, respiratory failure, kidney failure, metabolic disorders, anemia, cancer, chronic obstructive pulmonary diseases [COPD]), and 24 baseline laboratory indicators (< 50% missing values; e.g., RDW, Hb, red blood cells [RBC], white blood cells [WBC], hematocrit, platelet count [PLT], neutrophils, monocytes, basophils, eosinophils, lymphocytes, glucose, creatinine, blood urea nitrogen [BUN], bicarbonate, phosphate, total calcium, chloride, potassium, magnesium, anion gap, prothrombin time [PT], activated partial thromboplastin time [APTT] and international normalized ratio [INR]). Missing baseline laboratory indicators were imputed via the Random Forest algorithm, with all non-missing variables as predictors. Time-varying RDW and Hb values (recorded daily for each patient) were retained. For each patient, HRR was calculated as Hb (g/dL) divided by RDW (%) and rounded to two decimal places. The outcome was all-cause mortality during hospitalization. Statistical analysis For baseline characteristics, continuous variables were summarized as mean ± standard deviation (normally distributed) or median with interquartile range (non-normal), and compared using Student’s t-test or Mann-Whitney U test, respectively. Categorical variables were expressed as counts (percentages) and analyzed with Chi-square test or Fisher’s exact test. ‌Kaplan-Meier survival analysis was employed to compare mortality risk across HRR quartiles, with group differences assessed using log-rank tests. Cox proportional hazards models‌ estimated hazard ratios (HR) and 95% confidence interval (CI) for the association between baseline HRR (modeled as continuous or ordinal variables, with the first quartile as reference) and all-cause mortality. Clinically relevant variables were included in univariate Cox regression analysis. Three hierarchical models were then constructed: Model 1 contained only baseline HRR without adjustment; Model 2 was adjusted for demographics; and ‌Model 3 was further adjusted for demographics and covariates selected via univariate analysis. Additionally, a restricted cubic spline (RCS) regression model with four knots analyzed the nonlinear association between baseline HRR and mortality, and P values for nonlinear trends were calculated. To assess the consistency of the association between HRR and mortality within the general population and to identify specific population characteristics, we performed subgroup analyses and interaction tests based on age (≤ 65 and > 65 years), sex, hypertension, diabetes, cerebral infarction, cerebral cysts, dementia, paralysis, PVD, respiratory failure, metabolic disorders, anemia and COPD. The interactions between HRR and stratification variables were examined using likelihood ratio tests. A two-tailed P < 0.05 was regarded as statistically significant. For temporal analyses, we employed Pearson correlation analysis to examine correlations between daily HRR and mortality. Longitudinal trends of HRR during hospitalization were analyzed using linear regression. Receiver operating characteristic (ROC) curves were constructed to evaluate HRR's time-specific predictive performance for mortality in ICH patients, with predictive accuracy quantified by the area under the ROC curve (AUC). Cox proportional hazards regression was conducted to analyze the dynamic association between time-dependent daily HRR and mortality, adjusted for multiple confounders. To assess the impact of HRR fluctuations relative to admission values, we calculated daily deviations from baseline HRR and analyzed their association with mortality using logistic regression. Besides, we identified the optimal baseline HRR cut-off value using the ‘surv_cutpoint’ function (survminer package) in R 4.3.1, then categorized patients into high- and low-HRR groups via ‘surv_categorize’‌ function. Kaplan-Meier survival curves with Log-rank tests were employed to compare mortality between these predefined groups‌. All statistical analyses were performed using R 4.3.1 and Python 3.7.13. Results Baseline characteristics Table 1 presents the baseline characteristics of 2,447 ICH patients stratified into quartiles based on HRR (Q1: 0.16–0.77; Q2: 0.77–0.93; Q3: 0.93–1.05; Q4: 1.05–1.64). The cohort demonstrated a median age of 68 years (interquartile range [IQR]: 56–79), with male predominance (1,482 [60.56%]). The median HRR was 0.93 (IQR: 0.77–1.05). The median hospital length of stay was 8 days (IQR 4–14), with an observed all-cause hospital mortality rate of 15.57% (381 patients). The median HRR (IQR) for each quartile was 0.64 (0.53–0.71), 0.86 (0.82–0.90), 0.99 (0.96–1.02), and 1.14 (1.09–1.20), respectively. Patients in Q4 were the youngest (median age 61 years, IQR 51–71), while Q2 patients were the oldest (72 years, IQR 60–82). Mortality rates progressively decreased across ascending HRR quartiles (Q1 to Q4: 23.73–9.65%; P < 0.001). Higher HRR quartiles demonstrated inverse associations with most comorbidities (all P < 0.05 except PVD, myocardial infarction, and metabolic disorders). Patients in Q4 exhibited significantly higher levels of Hb, RBC, WBC, hematocrit, PLT and total calcium (all P < 0.001), alongside lower levels of RDW, BUN, phosphate, and PT (all P < 0.001). Table 1 Baseline characteristics of participants categorized by HRR quartiles Variable Overall (n = 2447) Q1 (n = 611) Q2 (n = 595) Q3 (n = 619) Q4 (n = 622) P-value Demographic characters Age, years 68.00 (56.00–79.00) 69.00 (60.00–80.00) 72.00 (60.00–82.00) 68.00 (55.00–79.00) 61.00 (51.00–71.00) < 0.001 Sex (males) 1482 (60.56) 331 (54.17) 318 (53.45) 355 (57.35) 478 (76.85) < 0.001 Marital status (Married) 1646 (67.27) 372 (60.88) 375 (63.03) 445 (71.89) 454 (72.99) < 0.001 Race (white) 831 (33.96) 225 (36.82) 192 (32.27) 199 (32.15) 215 (34.57) 0.261 Hospitalization information Admission type (emergency) 465 (19.00) 123 (20.13) 117 (19.66) 104 (16.80) 121 (19.45) 0.439 Length of stay, days 8.00 (4.00–14.00) 8.00 (5.00–16.00) 7.00 (4.00–13.00) 7.00 (4.00–13.00) 8.00 (4.00–14.00) 0.037 Comorbidities Hypertension 1525 (62.32) 438 (71.69) 384 (64.54) 363 (58.64) 340 (54.66) < 0.001 Diabetes 896 (36.62) 287 (46.97) 236 (39.66) 197 (31.83) 176 (28.30) < 0.001 Cerebral infarction 459 (18.76) 151 (24.71) 105 (17.65) 108 (17.45) 95 (15.27) < 0.001 Cerebral cysts 545 (22.27) 165 (27.00) 136 (22.86) 132 (21.32) 112 (18.01) 0.002 Dementia 307 (12.55) 156 (25.53) 63 (10.59) 54 (8.72) 34 (5.47) < 0.001 Paralysis 469 (19.17) 172 (28.15) 115 (19.33) 98 (15.83) 84 (13.50) < 0.001 Peripheral vascular disease 531 (21.70) 125 (20.46) 153 (25.71) 128 (20.68) 125 (20.10) 0.057 Myocardial infarction 724 (29.59) 166 (27.17) 182 (30.59) 197 (31.83) 179 (28.78) 0.297 Heart failure 365 (14.92) 169 (27.66) 71 (11.93) 68 (10.99) 57 (9.16) < 0.001 Other heart disease 597 (24.40) 202 (33.06) 159 (26.72) 133 (21.49) 103 (16.56) < 0.001 Respiratory failure 570 (23.29) 176 (28.81) 152 (25.55) 145 (23.42) 97 (15.59) < 0.001 Kidney failure 440 (17.98) 199 (32.57) 108 (18.15) 78 (12.60) 55 (8.84) < 0.001 Metabolic disorders 1070 (43.73) 298 (48.77) 239 (40.17) 273 (44.10) 260 (41.80) 0.016 Anemia 885 (36.17) 202 (33.06) 217 (36.47) 227 (36.67) 239 (38.42) 0.259 Cancer 511 (20.88) 273 (44.68) 120 (20.17) 80 (12.92) 38 (6.11) < 0.001 COPD 1746 (71.35) 465 (76.10) 438 (73.61) 440 (71.08) 403 (64.79) < 0.001 Laboratory tests HRR 0.93 (0.77–1.05) 0.64 (0.53–0.71) 0.86 (0.82–0.90) 0.99 (0.96–1.02) 1.14 (1.09–1.20) < 0.001 RDW, % 13.70 (13.00-14.80) 16.00 (14.80–17.70) 14.00 (13.40–14.70) 13.40 (13.00-13.90) 12.90 (12.40–13.40) < 0.001 Hemoglobin, g/dL 12.70 (11.30–14.00) 10.10 (8.90–10.90) 12.00 (11.50–12.60) 13.20 (12.70–13.80) 14.80 (14.10–15.60) < 0.001 RBC, m/uL 4.21 (3.75–4.65) 3.43 (2.99–3.89) 3.98 (3.74–4.31) 4.32 (4.10–4.58) 4.82 (4.49–5.11) < 0.001 WBC, K/uL 9.30 (7.10–12.50) 8.30 (6.00-11.80) 9.20 (7.00-12.15) 9.60 (7.50–12.60) 10.10 (7.80–13.50) < 0.001 Hematocrit, % 38.10 (34.30–41.70) 31.10 (27.80–34.10) 36.20 (34.50–38.40) 39.30 (37.70–41.20) 43.40 (41.30–45.80) < 0.001 PLT, K/uL 217.00 (167.00-273.00) 194.00 (126.00-277.50) 217.00 (166.25-275.75) 222.00 (178.50–273.00) 227.00 (187.00-271.00) < 0.001 Neu, % 77.80 (67.17–85.80) 75.55 (64.83–84.50) 79.10 (67.85–85.88) 78.65 (68.95–86.60) 78.50 (67.47–85.90) 0.004 Mono, % 5.30 (3.60–7.40) 5.90 (3.80–8.78) 5.30 (3.73–7.40) 5.00 (3.30–6.80) 5.30 (3.68–7.12) < 0.001 Baso, % 0.40 (0.20–0.60) 0.30 (0.10–0.50) 0.30 (0.20–0.50) 0.40 (0.20–0.60) 0.40 (0.20–0.60) < 0.001 Eos, % 0.70 (0.20–1.70) 1.00 (0.10–2.10) 0.70 (0.20–1.60) 0.70 (0.20–1.62) 0.60 (0.20–1.40) 0.033 Lym, % 13.80 (8.00-21.88) 14.00 (8.22–21.82) 13.00 (7.50-21.35) 13.85 (7.70-21.73) 14.30 (8.70–22.40) 0.358 Glucose, mg/dL 124.00 (104.00-157.00) 118.00 (99.00-155.00) 125.00 (104.00-158.25) 126.50 (107.00-157.25) 124.00 (106.00-156.00) 0.001 Creatinine, mg/dL 0.90 (0.70–1.10) 1.00 (0.70–1.50) 0.90 (0.70–1.10) 0.80 (0.70-1.00) 0.90 (0.80–1.10) < 0.001 BUN, mg/dL 17.00 (13.00–23.00) 21.00 (14.00–33.00) 17.00 (13.00–24.00) 16.00 (13.00–20.00) 15.00 (12.00–20.00) < 0.001 Bicarbonate, mEq/L 24.00 (22.00–26.00) 24.00 (21.00–26.00) 24.00 (22.00–26.00) 24.00 (22.00–26.00) 24.00 (22.00–27.00) 0.12 Phosphate, mg/dL 3.30 (2.80–3.80) 3.50 (2.90–4.20) 3.30 (2.80–3.75) 3.20 (2.80–3.70) 3.10 (2.70–3.60) < 0.001 Total calcium, mg/dL 9.00 (8.50–9.40) 8.80 (8.40–9.20) 8.90 (8.40–9.30) 9.00 (8.60–9.40) 9.20 (8.70–9.50) < 0.001 Chloride, mEq/L 102.00 (99.00-105.00) 102.00 (98.00-105.00) 103.00 (100.00-105.00) 103.00 (100.00-105.00) 102.00 (99.00-105.00) 0.033 Serum sodium, mEq/L 139.00 (137.00-141.00) 138.00 (136.00-141.00) 139.00 (136.00-141.00) 139.00 (137.00-141.00) 139.00 (137.00-141.00) < 0.001 Serum potassium, mEq/L 4.10 (3.70–4.40) 4.10 (3.80–4.60) 4.00 (3.70–4.50) 4.00 (3.70–4.40) 4.00 (3.70–4.40) 0.013 Serum magnesium, mg/dL 1.90 (1.80–2.10) 2.00 (1.70–2.10) 1.90 (1.70–2.10) 2.00 (1.80–2.10) 2.00 (1.80–2.10) 0.074 Anion Gap, mEq/L 15.00 (13.00–17.00) 15.00 (13.00–17.00) 15.00 (13.00–17.00) 15.00 (13.00–17.00) 15.00 (13.00–17.00) 0.183 PT, sec 12.40 (11.40-14.03) 13.50 (11.90–16.80) 12.50 (11.60–14.20) 12.30 (11.30–13.40) 11.90 (11.10-13.05) < 0.001 APTT, sec 28.10 (25.30–31.50) 29.50 (25.90-35.02) 28.10 (25.50-31.48) 27.45 (25.00-30.60) 27.80 (25.15–30.50) < 0.001 INR 1.10 (1.00-1.30) 1.20 (1.10–1.50) 1.10 (1.00-1.30) 1.10 (1.00-1.20) 1.10 (1.00-1.20) < 0.001 Outcome Hospital mortality 381 (15.57) 145 (23.73) 93 (15.63) 83 (13.41) 60 (9.65) < 0.001 Baseline characteristics between survivors and non-survivors are presented in Table 2 . Non-survivors were older, and exhibited a higher prevalence of hypertension, diabetes, cerebral cysts, dementia, paralysis, PVD, respiratory failure, kidney failure, metabolic disorders, cancer, and COPD (all P < 0.05). Median HRR was significantly lower in non-survivors compared to survivors (0.86 vs. 0.94; P < 0.001). Most laboratory indicators differed significantly between non-survivors and survivors, with lower Hb, RBC, hematocrit, and PLT in non-survivors (all P < 0.001) Table 2 Baseline characteristic of survivors and non-survivors Variable Overall (n = 2447) Survivors (n = 2066) Non-survivors (n = 381) P-value Demographic characters Age, years 68.00 (56.00–79.00) 67.00 (55.00–78.00) 70.00 (60.00–81.00) 0.002 Sex (males) 1482 (60.56) 1256 (60.79) 226 (59.32) 0.588 Marital status (Married) 1646 (67.27) 1317 (63.75) 329 (86.35) < 0.001 Race (white) 831 (33.96) 652 (31.56) 179 (46.98) < 0.001 Hospitalization information Admission type (emergency) 465 (19.00) 416 (20.14) 49 (12.86) < 0.001 Length of stay, days 8.00 (4.00–14.00) 8.00 (4.00–15.00) 7.00 (3.00–13.00) < 0.001 Comorbidities Hypertension 1525 (62.32) 1253 (60.65) 272 (71.39) < 0.001 Diabetes 896 (36.62) 708 (34.27) 188 (49.34) < 0.001 Cerebral infarction 459 (18.76) 377 (18.25) 82 (21.52) 0.132 Cerebral cysts 545 (22.27) 339 (16.41) 206 (54.07) < 0.001 Dementia 307 (12.55) 233 (11.28) 74 (19.42) < 0.001 Paralysis 469 (19.17) 378 (18.30) 91 (23.88) 0.011 Peripheral vascular disease 531 (21.70) 433 (20.96) 98 (25.72) 0.038 Myocardial infarction 724 (29.59) 599 (28.99) 125 (32.81) 0.134 Heart failure 365 (14.92) 305 (14.76) 60 (15.75) 0.62 Other heart disease 597 (24.40) 498 (24.10) 99 (25.98) 0.432 Respiratory failure 570 (23.29) 441 (21.35) 129 (33.86) < 0.001 Kidney failure 440 (17.98) 317 (15.34) 123 (32.28) < 0.001 Metabolic disorders 1070 (43.73) 815 (39.45) 255 (66.93) < 0.001 Anemia 885 (36.17) 748 (36.21) 137 (35.96) 0.926 Cancer 511 (20.88) 394 (19.07) 117 (30.71) < 0.001 COPD 1746 (71.35) 1449 (70.14) 297 (77.95) 0.002 Laboratory tests HRR 0.93 (0.77–1.05) 0.94 (0.79–1.06) 0.86 (0.68-1.00) < 0.001 RDW, % 13.70 (13.00-14.80) 13.60 (13.00-14.60) 14.20 (13.30–15.90) < 0.001 Hemoglobin, g/dL 12.70 (11.30–14.00) 12.80 (11.40–14.10) 12.30 (10.50–13.50) < 0.001 RBC, m/uL 4.21 (3.75–4.65) 4.23 (3.80–4.67) 4.06 (3.46–4.57) < 0.001 WBC, K/uL 9.30 (7.10–12.50) 9.20 (7.00-12.17) 10.90 (7.70–15.20) < 0.001 Hematocrit, % 38.10 (34.30–41.70) 38.30 (34.70-41.88) 36.90 (32.70–40.90) < 0.001 PLT, K/uL 217.00 (167.00-273.00) 222.00 (173.00-276.00) 192.50 (136.75–252.00) < 0.001 Neu, % 77.80 (67.17–85.80) 77.20 (66.62–85.20) 83.25 (71.43–88.25) < 0.001 Mono, % 5.30 (3.60–7.40) 5.40 (3.70–7.40) 5.05 (3.42–7.38) 0.258 Baso, % 0.40 (0.20–0.60) 0.40 (0.20–0.60) 0.25 (0.10–0.40) < 0.001 Eos, % 0.70 (0.20–1.70) 0.70 (0.20–1.80) 0.30 (0.00–1.00) < 0.001 Lym, % 13.80 (8.00-21.88) 14.55 (8.70–22.40) 9.45 (5.93–16.98) < 0.001 Glucose, mg/dL 124.00 (104.00-157.00) 121.00 (103.00-152.00) 146.00 (116.00-184.00) < 0.001 Creatinine, mg/dL 0.90 (0.70–1.10) 0.90 (0.70–1.10) 1.00 (0.70–1.45) < 0.001 BUN, mg/dL 17.00 (13.00–23.00) 16.00 (13.00–22.00) 20.00 (15.00-29.50) < 0.001 Bicarbonate, mEq/L 24.00 (22.00–26.00) 24.00 (22.00–26.00) 23.00 (20.00–26.00) < 0.001 Phosphate, mg/dL 3.30 (2.80–3.80) 3.30 (2.80–3.80) 3.30 (2.80–4.07) 0.102 Total calcium, mg/dL 9.00 (8.50–9.40) 9.00 (8.60–9.40) 8.70 (8.20–9.20) < 0.001 Chloride, mEq/L 102.00 (99.00-105.00) 103.00 (100.00-105.00) 101.00 (98.00-105.00) 0.002 Serum sodium, mEq/L 139.00 (137.00-141.00) 139.00 (137.00-141.00) 139.00 (136.00-141.00) 0.113 Serum potassium, mEq/L 4.10 (3.70–4.40) 4.10 (3.70–4.40) 4.10 (3.70–4.60) 0.095 Serum magnesium, mg/dL 1.90 (1.80–2.10) 1.90 (1.80–2.10) 1.90 (1.70–2.10) 0.176 Anion Gap, mEq/L 15.00 (13.00–17.00) 15.00 (13.00–17.00) 16.00 (14.00–19.00) < 0.001 PT, sec 12.40 (11.40-14.03) 12.30 (11.40–13.90) 12.90 (11.70–15.40) < 0.001 APTT, sec 28.10 (25.30–31.50) 28.10 (25.30–31.40) 28.20 (25.10-32.52) 0.493 INR 1.10 (1.00-1.30) 1.10 (1.00-1.20) 1.10 (1.00-1.40) < 0.001 Association between baseline HRR and mortality Univariate analysis (Table S1 ) indicated that higher HRR was associated with reduced mortality risk in ICH patients. Significant mortality factors (P < 0.05) of mortality included HRR, age, admission type, race, marital status, diabetes, cerebral cysts, dementia, paralysis, kidney failure, metabolic disorders, anemia, COPD, RDW, Hb, RBC, hematocrit, PLT, basophil, glucose, creatinine, BUN, bicarbonate, phosphate, total calcium, chloride, anion gap, PT, and INR. Both unadjusted and adjusted models demonstrated a negative relationship between HRR and mortality (Table 3 ). When modeled continuously, lower HRR values were independently associated with elevated mortality risk across all models: unadjusted (HR: 0.346; 95% CI: 0.226–0.528; P < 0.001), partially adjusted (0.346; 0.223–0.537; P < 0.001), and fully adjusted models (0.115, 0.048–0.280; P < 0.001). When analyzed as ordinal quartiles, higher HRR categories showed graded mortality reductions. In unadjusted analysis, the highest quartile (Q4) exhibited a 56.1% risk reduction compared to Q1 (HR: 0.439; 95% CI: 0.323–0.597; P < 0.001). This association persisted after adjustment, with Q4 showing 53.1% and 70.7% risk reductions in partially (HR: 0.469; 95% CI: 0.342–0.642; P < 0.001) and fully adjusted models (HR: 0.293; 95% CI: 0.186–0.464; P < 0.001), respectively. Table 3 Cox proportional hazard (HR) for all-cause mortality Categories Model1 Model2 Model3 HR (95% CI) P-value P for trend HR (95% CI) P-value P for trend HR (95% CI) P-value P for trend Continuous variable per unit 0.346 (0.226–0.528) < 0.001 0.346 (0.223–0.537) < 0.001 0.115 (0.048–0.280) < 0.001 Quartile < 0.001 < 0.001 < 0.001 Q1 (N = 615) ref ref ref Q2 (N = 613) 0.751 (0.579–0.975) 0.032 0.724 (0.558–0.940) 0.015 0.657 (0.483–0.893) 0.007 Q3 (N = 610) 0.682 (0.521–0.891) 0.005 0.644 (0.492–0.844) 0.001 0.500 (0.350–0.713) < 0.001 Q4 (N = 609) 0.439 (0.323–0.597) < 0.001 0.469 (0.342–0.642) < 0.001 0.293 (0.186–0.464) < 0.001 Kaplan-Meier curves demonstrated a stepwise reduction in all-cause mortality across ascending HRR quartiles (Q1: 23.73%, Q2: 15.63%, Q3: 13.41%, Q4: 9.65%; log-rank P < 0.001) (Fig. 2 a). Patients in higher HRR quartiles exhibited progressively lower mortality risk compared to lower quartiles (Fig. 2 b). RCS analysis (Fig. 2 c) indicated that the relationship between HRR and hospital mortality risk was more likely to be linear (P for non-linearity = 0.213). Subgroup analyses (Fig. 3 ) indicated a consistent inverse association between elevated HRR and lower mortality risk across most subgroups (P < 0.05). However, the association was not statistically significant in three subgroups: patients with PVD, anemia, and non-COPD cases. No significant interaction effects were detected across subgroups for any confounder (all P for interaction > 0.05). Temporal trends of HRR Figure 4 a-c depicted the temporal trends in HRR, Hb, and RDW during the 14-day post-admission period for ICH patients. Both survivors and non-survivors exhibited progressive declines in HRR and Hb levels, while RDW levels increased gradually. From admission to day 14, mean HRR decreased significantly in non-survivors (0.835 to 0.553) and survivors (0.919 to 0.710) (both P for trend < 0.001). Linear regression analyses revealed temporal associations for each biomarker: HRR declined at a rate of -0.014 per day (r = -0.971), Hb at -0.157 per day (r = -0.963), and RDW increased at 0.054 per day (r = 0.994) (all P < 0.001). Temporal trajectories during the whole hospitalization of HRR, Hb, and RDW were visualized in Fig. S1 . At each time point during the 14-day post-admission period, non-survivors exhibited significantly lower HRR (mean difference: 0.128) and Hb (mean difference: 1.074), along with higher RDW (mean difference: 1.303), compared to the survivors (all P < 0.001 by Mann-Whitney U test at each time point). Longitudinal analysis revealed progressively widening 95% CIs for HRR, Hb and RDW, reflecting increased variability over time (Fig. 4 a–c). This variability likely stems from the diminishing cohort size, as fewer patients underwent repeated laboratory assessments during hospitalization (Fig. 4 d). Despite greater variability resulting from reduced sample size, the directional trends remained statistically robust. Subgroup analyses by HRR temporal trends across demographic and clinical factors (Fig. 5 ) revealed consistent downward trajectories in all subgroups (all P for trend < 0.001), though decline rates varied. Males maintained higher HRR levels throughout hospitalization compared to females (mean difference: 0.078, P = 0.003). Age-stratified analysis showed comparable HRR declines between older (> 65 years) and younger patients (≤ 65 years, P = 0.448). Patients without chronic comorbidities (e.g., hypertension, diabetes, cerebral cysts, dementia, paralysis, and major organ failure) maintained significantly higher HRR levels than those with (all P < 0.05). The most pronounced differences were observed in patients with cancer (mean difference: 0.196, P < 0.001), kidney failure (0.124, P < 0.001), dementia (0.109, P < 0.001), cerebral cysts (0.095, P < 0.001), and heart failure (0.081, P = 0.002). Associations between dynamic HRR and mortality Figure 6 illustrated Pearson correlation coefficients between HRR, Hb, RDW, and mortality risk across post-admission time points. Most time points demonstrated negative correlations between HRR, Hb and mortality, alongside positive RDW-mortality associations. These correlations strengthened as discharge neared, with HRR-mortality and Hb-mortality correlations becoming progressively more negative (HRR: from − 0.141 to -0.281; Hb: from − 0.101 to -0.238) and RDW-mortality correlations increasing from 0.155 to 0.261 (all P for trend < 0.05). Notably, the HRR-mortality correlation intensified sharply within the first four days post-admission, particularly in baseline HRR quartiles 3 (-0.004 to -0.238) and 4 (0.083 to -0.223) (Fig. 6 a). Adjacent time points exhibited a higher correlation in HRR values, suggesting a closer relationship within short time intervals (Fig. 6 b) and similar phenomena were observed in Hb and RDW (Fig. 6 c and d). Daily HRR levels during the 14-day post-admission period demonstrated a consistent inverse association with mortality risk, with adjusted hazard ratios (HRs) < 1.0 and P < 0.05 at all time points (Fig. 7 ). Day-to-day HRR variability, quantified as deviations from admission-day baseline, showed a significant independent association with mortality from day 3 to day 14 (OR range: 0.027 to 0.107, P < 0.05). Time-varying Cox proportional hazards models and logistic regression analyses confirmed that both sustained HRR elevation and reduced acute fluctuations were independently linked to the mortality in hospitalized patients. To evaluate the temporal predictive utility of HRR for mortality, we developed logistic regression models incorporating repeated HRR measurements. As demonstrated in Fig. 8 , predictive performance improved progressively as measurements were taken closer to discharge, with AUCs increasing from 0.631 (Day 1) to 0.731 (Day 7) and 0.763 (the last day). HRR-based models consistently outperformed those using Hb or RDW alone at all time points, achieving the highest discriminative ability (AUC 0.763 vs. 0.736 for Hb and 0.705 for RDW). Association between HRR cut-off and mortality The optimal baseline HRR cutoff for mortality risk stratification was identified as 0.74 (Fig. 9 a). Patients with baseline HRR > 0.74 maintained higher HRR levels throughout hospitalization compared to those with baseline HRR ≤ 0.74 (Fig. 9 b). Median survival time was significantly longer in the HRR > 0.74 group (79 days) than in the HRR ≤ 0.74 group (41 days, P < 0.001).‌ Patients with baseline HRR > 0.74 exhibited greater declines in serial HRR measurements over 25 days post-admission (Fig. 9 b). As shown in Table 4 , patients with HRR ≤ 0.74 were older, had longer hospital stays, and exhibited significantly higher comorbidity burdens. Compared to the HRR > 0.74 group, this cohort showed higher prevalence of hypertension, diabetes, cerebral infarction, cerebral cysts, dementia, paralysis, heart failure, respiratory failure, kidney failure, metabolic disorders, cancer, and COPD. Notably, in-hospital mortality was substantially higher in the HRR ≤ 0.74 group (25.14% vs. 12.93%, P < 0.001). Table 4 Comparison of baseline characteristics of patients stratified by baseline HRR cut-off. Variable HRR ≤ 0.74 (n = 529) HRR > 0.74 (n = 1918) P-value Demographic characters Age, years 69.00(60.00–80.00) 67.00(55.00–78.00) < 0.001 Sex (males) 291(55.01) 1191(62.10) 0.003 Marital status (Married) 323(61.06) 1323(68.98) < 0.001 Race (white) 196(37.05) 635(33.11) 0.09 Hospitalization information Admission type (emergency) 107(20.23) 358(18.67) 0.418 Length of stay, days 8.00(5.00–17.00) 7.00(4.00–14.00) 0.002 Comorbidities Hypertension 382(72.21) 1143(59.59) < 0.001 Diabetes 258(48.77) 638(33.26) < 0.001 Cerebral infarction 135(25.52) 324(16.89) < 0.001 Cerebral cysts 149(28.17) 396(20.65) < 0.001 Dementia 143(27.03) 164(8.55) < 0.001 Paralysis 149(28.17) 320(16.68) < 0.001 Peripheral vascular disease 102(19.28) 429(22.37) 0.127 Myocardial infarction 144(27.22) 580(30.24) 0.178 Heart failure 150(28.36) 215(11.21) < 0.001 Other heart disease 174(32.89) 423(22.05) < 0.001 Respiratory failure 158(29.87) 412(21.48) < 0.001 Kidney failure 184(34.78) 256(13.35) < 0.001 Metabolic disorders 266(50.28) 804(41.92) < 0.001 Anemia 174(32.89) 711(37.07) 0.077 Cancer 255(48.20) 256(13.35) < 0.001 COPD 402(75.99) 1344(70.07) 0.008 Laboratory tests HRR 0.62(0.51–0.69) 0.98(0.89–1.09) < 0.001 RDW, % 16.30(15.00–18.00) 13.40(12.90–14.10) < 0.001 Hemoglobin, g/dL 9.90(8.80–10.70) 13.20(12.30-14.38) < 0.001 RBC, m/uL 3.35(2.92–3.82) 4.36(4.00-4.77) < 0.001 WBC, K/uL 8.40(5.90–11.90) 9.60(7.40–12.60) < 0.001 Hematocrit, % 30.60(27.30–33.40) 39.40(36.70–42.60) < 0.001 Platelet, K/uL 191.00(121.00-277.00) 222.00(177.00-273.00) < 0.001 Neu, % 75.40(64.60–84.50) 78.70(68.00–86.00) < 0.001 Mono, % 5.90(3.80–8.80) 5.20(3.60–7.15) < 0.001 Baso, % 0.30(0.10–0.50) 0.40(0.20–0.60) < 0.001 Eos, % 0.90(0.10–2.10) 0.60(0.20–1.60) 0.085 Lym, % 13.90(8.00-21.30) 13.80(7.90–22.00) 0.434 Glucose, mg/dL 119.00(99.00-155.00) 125.00(105.00-158.00) 0.001 Creatinine, mg/dL 1.10(0.80–1.60) 0.90(0.70–1.10) < 0.001 BUN, mg/dL 21.50(15.00–34.00) 16.00(13.00–21.00) < 0.001 Bicarbonate, mEq/L 24.00(21.00–26.00) 24.00(22.00–26.00) 0.035 Phosphate, mg/dL 3.60(3.00-4.20) 3.20(2.80–3.70) < 0.001 Total calcium, mg/dL 8.80(8.38–9.20) 9.00(8.50–9.40) < 0.001 Chloride, mEq/L 102.00(98.00-105.00) 103.00(100.00-105.00) 0.008 Serum sodium, mEq/L 138.00(135.25–141.00) 139.00(137.00-141.00) < 0.001 Serum potassium, mEq/L 4.10(3.80–4.60) 4.00(3.70–4.40) < 0.001 Serum magnesium, mg/dL 2.00(1.70–2.10) 1.90(1.80–2.10) 0.555 Anion Gap, mEq/L 15.00(13.00–18.00) 15.00(13.00–17.00) 0.967 PT, sec 13.60(12.00-16.80) 12.20(11.30–13.50) < 0.001 APTT, sec 29.70(26.10–35.60) 27.90(25.20-30.88) < 0.001 INR 1.20(1.10–1.50) 1.10(1.00-1.20) < 0.001 Outcome Hospital mortality 133(25.14) 248(12.93) < 0.001 Discussions This study assessed the association between the dynamic HRR during hospitalization and all-cause mortality in ICH patients, revealing that lower HRR was associated with higher mortality risk. Throughout hospitalization, HRR progressively declined, with non-survivors exhibiting persistently lower levels than survivors. While all subgroups demonstrated significant temporal declines, males and patients with comorbidities maintained consistently lower HRR values across all time points. The inverse relationship between HRR and mortality strengthened progressively over time, peaking near discharge, where final-day HRR alone achieved a peak predictive accuracy (AUC = 0.763) in temporal models. A baseline HRR threshold of 0.74 effectively stratified mortality risk, with HRR ≤ 0.74 patients showing significantly shorter survival (25.14% vs. 12.93% mortality, P < 0.001) compared to those with HRR > 0.74. These findings underscore HRR as both a time-sensitive prognostic biomarker and a potential target for early interventions to improve outcomes in high-risk ICH populations. HRR has recently emerged as a crucial prognostic marker for severe conditions such as coronary heart disease 24 , 25 , 28 , 29 , cancer 21 , 30 , 31 , sepsis 32 , and ischemic stroke 19 , 23 , 26 , 33 . Evidence highlighted its inverse relationship with mortality, with higher HRR levels correspond to 53% lower all-cause mortality, 49% lower cancer-specific mortality, and 57% reduced cardiovascular mortality 21 . Notably, acute ischemic stroke patients with suboptimal post-thrombectomy outcomes exhibited low HRR levels 33 , and those with HRR ≤ 0.76 faced nearly tripled mortality risks alongside heightened susceptibility to pneumonia and septicemia 26 . This study confirms this inverse HRR-mortality link in ICH patients, demonstrating its persistence throughout hospitalization even after adjusting for age, comorbidities, and key laboratory confounders. The prognostic value of HRR likely stems from its dual-pathway integration of hemoglobin (Hb) and red cell distribution width (RDW) - two hematologic parameters reflecting oxygen transport capacity and inflammatory status. As a composite biomarker, HRR bridges erythrocyte heterogeneity (RDW) and oxygen-carrying function (Hb), encapsulating the interplay between hematologic stress and tissue vulnerability. Mechanistically, elevated HRR may mitigate chronic inflammation through synergistic effects: enhancing Hb's capacity to capture inflammatory molecules 34 while reducing RDW-associated inflammatory responses 35 . This dual regulation improves oxygen delivery to hypoxia-sensitive neural tissues and reduces systemic inflammation, ultimately decreasing cerebral hemorrhage mortality risk. From a clinical perspective, higher HRR levels indicate optimized erythrocyte functionality - better oxygen transport efficiency coupled with reduced inflammatory burden, collectively contributing to improved outcomes in hemorrhagic stroke and other critical illnesses. These pathophysiological insights position HRR as an applicable biomarker reflecting both inflammatory-hematologic crosstalk and tissue oxygen homeostasis. Our study enhances understanding of HRR as a dynamic prognostic biomarker in ICH by analyzing longitudinal in-hospital trajectories, contrasting with prior static baseline assessments. While previous investigations, including studies by Lin et al. 36 , Xiong et al. 23 , and Feng et al. 33 , primarily examined single-time-point HRR measurements at admission across diverse stroke populations, our analysis focuses on short-term HRR evolution during hospitalization and its direct association with ICH-specific mortality. We demonstrate that the inverse HRR-mortality relationship not only persists but strengthens over time, with HRR levels progressively declining post-admission and the Pearson correlation coefficient between HRR and mortality risk escalating from 0.140 (Day 1) to 0.238 (Day 14). Survivors consistently maintained higher HRR trajectories compared to non-survivors, whose HRR declined more rapidly. Logistic regression confirmed temporal HRR changes as independent mortality predictors, emphasizing HRR’s utility as a time-sensitive prognostic indicator. Unlike a previous population-based longitudinal study documenting gradual HRR attenuation over decades 21 , this study highlighted the clinical relevance of serial in-hospital HRR monitoring for identifying deteriorating patients in real time, with a critical advantage over static baseline assessments. These temporal findings provide actionable insights into evolving risk profiles, enabling timely intervention strategies to mitigate mortality in high-risk ICH cohorts. Moreover, subgroup analyses revealed sustained HRR declines across demographic and comorbid populations, with notable variations in trajectory and magnitude. Throughout hospitalization, females demonstrated consistently higher HRR levels compared to males, while patients with severe comorbidities such as cancer, renal failure, heart failure, and cerebral cysts exhibited notably lower HRR than those without. These findings imply that HRR may be as a sensitive, stratification-ready marker, with comorbidity-driven divergence in trajectories reflecting differential pathophysiological burdens. In addition, our findings confirm HRR's independent prognostic value for ICH mortality, with predictive performance progressively improving from admission (AUC 0.631) to discharge (AUC 0.763). This aligns with prior evidence establishing HRR as a robust prognostic marker across diverse clinical populations, including heart failure 28 , cancer 32 , and stroke patients 29 , 33 , with reported AUC ranges spanning 0.548 to 0.790. Particularly in ischemic stroke, HRR achieved clinically significant discrimination for functional outcomes (AUC 0.790) and mortality (AUC 0.771) 33 . Critically, HRR demonstrated superior predictive capacity compared to Hb and RDW at all time points in our cohort, reaching optimal discrimination (AUC 0.763) versus Hb (0.736) and RDW (0.705). Consistent with Qu et al.'s findings in patients with coronary heart disease (HRR AUC 0.652 vs. Hb 0.618/RDW 0.650) 29 and Yang et al.'s traumatic brain injury cohort (HRR AUC 0.713 vs. Hb 0.688/RDW 0.680 for 120-day mortality) 37 , our results reinforce HRR's prognostic superiority over conventional hematologic markers. Our threshold analysis identified 0.74 as a critical baseline HRR cutoff for mortality risk stratification in ICH patients, with those exhibiting HRR ≤ 0.74 demonstrating a 2.97-fold increased mortality risk, shorter survival duration, and higher comorbidity burdens compared to their HRR > 0.74 counterparts. While existing literature reported variable HRR thresholds across clinical contexts, including HRR < 9.50 predicting in-hospital mortality in non-traumatic subarachnoid hemorrhage 38 and HRR < 5.877 associated with septic atrial fibrillation mortality 32 , these studies collectively affirm HRR's prognostic utility. Notably, prior ICH-specific research revealed protective effects of elevated HRR against 28-day (HRR > 0.92) and 90-day (HRR > 0.93) mortality in older adults, underscoring its subtype-specific relevance 36 . Although optimal thresholds differ across populations, these findings consistently validate HRR's capacity to stratify mortality risk. Future studies could explore dynamic threshold adjustment during hospitalization to enhance predictive accuracy, particularly for patients with fluctuating HRR trajectories. Limitations This study has several limitations. First, the retrospective single-center design using publicly available data restricted analyses to routinely collected clinical information, resulting in incomplete laboratory parameters for some patients (e.g., due to early discharge/mortality), which may introduce selection bias. Second, the modest sample size limited statistical power for detecting subtle HRR interactions. Third, the absence of external validation restricts generalizability. Fourth, while adjusting for multiple confounders, residual bias persists from unmeasured variables, including genetic predispositions, socioeconomic determinants, and neuroimaging-based parameters (e.g., ICH subtype, hematoma location/volume). These factors may influence ICH outcomes and independently modulate physiological reserve/recovery trajectories, potentially confounding the HRR-mortality relationship. Future multi-center prospective studies with larger cohorts should incorporate standardized neuroimaging protocols and external validation to confirm HRR’s utility across ICH phenotypes, while exploring subgroup analyses to assess robustness. Conclusions This study established HRR as a dynamic, time-dependent prognostic biomarker for mortality risk stratification in ICH, outperforming Hb and RDW individually. Lower baseline HRR values independently predicted higher all-cause mortality, while serial measurements demonstrated a time-intensifying association with survival risk—peaking near discharge. Notably, a baseline HRR cutoff of 0.74 effectively identified high-risk patients. This routine, cost-effective metric holds significant potential for clinical risk assessment, particularly during early hospitalization when intervention windows are critical. Future research could validate HRR-guided therapeutic strategies and elucidate the pathophysiological pathways linking HRR dynamics to adverse outcomes, thereby strengthening its translational applicability in acute neurocritical care. Abbreviations ICH Intracerebral hemorrhage HRR Hemoglobin-to-red blood cell distribution width ratio RDW Red blood cell distribution width Hb Hemoglobin PVD Peripheral vascular disease RCS Restricted cubic spline COPD Chronic obstructive pulmonary disease RBC Red blood cells WBC White blood cells PLT Platelet count BUN Blood urea nitrogen PT Prothrombin time APTT Activated partial thromboplastin time INR International normalized ratio Declarations Competing interests All authors declare no competing interests. Funding This study was supported by the Outstanding Young Doctoral Program Research Initiation Fund of the First Affiliated Hospital of Guangxi Medical University (grant 202302). Author Contribution Y.H. was responsible for conceptualization, data curation, methodology, and writing the original draft. H.L. ensured data integrity and analysis accuracy. S.T. conducted a comprehensive review and validation. All authors actively participated and approved the final manuscript. Data availability All survey data utilized in this research are available in the Medical Information Mart for Intensive Care-IV (MIMIC-IV, version 3.1) database, accessible at: https://physionet.org/content/mimiciv/3.1/ . References Puy, L. et al. Intracerebral haemorrhage. Nat. reviews Disease primers . 9 , 14. https://doi.org/10.1038/s41572-023-00424-7 (2023). Hilkens, N. A., Casolla, B., Leung, T. W., de Leeuw, F. E. & Stroke Lancet (London England) 403 , 2820–2836, https://doi.org/10.1016/s0140-6736(24)00642-1 (2024). Rosand, J. Preserving brain health after intracerebral haemorrhage. Lancet Neurol. 20 , 879–880. https://doi.org/10.1016/s1474-4422(21)00339-2 (2021). Collaborators, G. D. & a. I. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet (London England) . 396 , 1204–1222. https://doi.org/10.1016/s0140-6736(20)30925-9 (2020). Cai, W. et al. Association between triglyceride-glucose index and all-cause mortality in critically ill patients with ischemic stroke: analysis of the MIMIC-IV database. Cardiovasc. Diabetol. 22 , 138. https://doi.org/10.1186/s12933-023-01864-x (2023). Chen, S. et al. A Longitudinal Dynamic Change in LMR Can Be a Biomarker for Recurrence in Fusobacterium Nucleatum-Positive Colorectal Cancer Patients. J. Inflamm. Res. 17 , 11587–11604. https://doi.org/10.2147/jir.S489432 (2024). Pereira, M., Batista, R., Marreiros, A. & Nzwalo, H. Neutrophil-to-leukocyte ratio and admission glycemia as predictors of short-term death in very old elderlies with lobar intracerebral hemorrhage. Brain circulation . 9 , 94–98. https://doi.org/10.4103/bc.bc_5_23 (2023). Yang, J., Duan, C., Zhu, X., Shen, J. & Ji, Q. The clinical value of triglyceride to high-density lipoprotein cholesterol ratio for predicting stroke-associated pneumonia after spontaneous intracerebral hemorrhage. BMC Neurol. 25 , 148. https://doi.org/10.1186/s12883-025-04154-z (2025). Acosta, J. N. et al. Admission Hemoglobin Levels Are Associated With Functional Outcome in Spontaneous Intracerebral Hemorrhage. Crit. Care Med. 49 , 828–837. https://doi.org/10.1097/ccm.0000000000004891 (2021). Pinho, J. et al. Red Cell Distribution Width is Associated with 30-day Mortality in Patients with Spontaneous Intracerebral Hemorrhage. Neurocrit. 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Association between red cell distribution width level and risk of stroke: A systematic review and meta-analysis of prospective studies. Medicine 99 , e19691. https://doi.org/10.1097/md.0000000000019691 (2020). Xu, J. et al. Prognostic Value of Red Blood Cell Distribution Width and Hemoglobin in Patients with Spontaneous Intracerebral Hemorrhage. Curr. Neurovasc. Res. 20 , 390–398. https://doi.org/10.2174/1567202620666230731111836 (2023). He, J. et al. Association Between Red Blood Cell Distribution width and Long-Term Mortality in Patients with Intracerebral Hemorrhage. Neurocrit. Care . 40 , 1059–1069. https://doi.org/10.1007/s12028-023-01875-2 (2024). Eyiol, A. & Ertekin, B. The relationship between hemoglobin-to-red cell distribution width (RDW) ratio (HRR) and mortality in stroke patients. Eur. Rev. Med. Pharmacol. Sci. 28 , 1504–1512. https://doi.org/10.26355/eurrev_202402_35480 (2024). Qin, Z. et al. Relationship Between the Hemoglobin-to-Red Cell Distribution Width Ratio and All-Cause Mortality in Ischemic Stroke Patients with Atrial Fibrillation: An Analysis from the MIMIC-IV Database. Neuropsychiatr. Dis. Treat. 18 , 341–354. https://doi.org/10.2147/ndt.S350588 (2022). Chi, G., Lee, J. J., Montazerin, S. M. & Marszalek, J. Prognostic value of hemoglobin-to-red cell distribution width ratio in cancer: a systematic review and meta-analysis. Biomark. Med. 16 , 473–482. https://doi.org/10.2217/bmm-2021-0577 (2022). Lai, T., Liang, Y., Guan, F. & Hu, K. Trends in hemoglobin-to- red cell distribution width ratio and its prognostic value for all-cause, cancer, and cardiovascular mortality: a nationwide cohort study. Sci. Rep. 15 , 7685. https://doi.org/10.1038/s41598-025-92228-w (2025). Xi, L. et al. Association of hemoglobin-to-red blood cell distribution width ratio and depression in older adults: A cross sectional study. J. Affect. Disord. 344 , 191–197. https://doi.org/10.1016/j.jad.2023.10.027 (2024). Xiong, Y. et al. Hemoglobin-to-red blood cell distribution width ratio is negatively associated with stroke: a cross-sectional study from NHANES. Sci. Rep. 14 , 28098. https://doi.org/10.1038/s41598-024-79520-x (2024). Chen, H. et al. Hemoglobin to red cell distribution width ratio: A predictor of clinical outcome and diuretic response in patients with acute heart failure. Int. J. Cardiol. 394 , 131368. https://doi.org/10.1016/j.ijcard.2023.131368 (2024). Li, Y., Xu, C., Qin, Z. & Ge, L. Relationship Between the Hemoglobin-to-Red Cell Distribution Width Ratio and in-Hospital Mortality in Patients with Chronic Heart Failure. Vasc. Health Risk Manag. 20 , 553–565. https://doi.org/10.2147/vhrm.S486075 (2024). Krongsut, S. & Piriyakhuntorn, P. Unlocking the potential of HB/RDW ratio as a simple marker for predicting mortality in acute ischemic stroke patients after thrombolysis. J. stroke Cerebrovasc. diseases: official J. Natl. Stroke Association . 33 , 107874. https://doi.org/10.1016/j.jstrokecerebrovasdis.2024.107874 (2024). Xie, X., He, K., Zhang, Y. & Wu, J. Association of hemoglobin-to-red cell distribution width ratio with the three-month outcomes in patients with acute ischemic stroke. Front. Neurol. 15 , 1425633. https://doi.org/10.3389/fneur.2024.1425633 (2024). Rahamim, E. et al. The Ratio of Hemoglobin to Red Cell Distribution Width: A Strong Predictor of Clinical Outcome in Patients with Heart Failure. J. Clin. Med. 11 https://doi.org/10.3390/jcm11030886 (2022). Qu, J. et al. Correlation Analysis of Hemoglobin-to-Red Blood Cell Distribution Width Ratio and Frailty in Elderly Patients With Coronary Heart Disease. Front. Cardiovasc. Med. 8 , 728800. https://doi.org/10.3389/fcvm.2021.728800 (2021). Coradduzza, D. et al. Assessing the Predictive Power of the Hemoglobin/Red Cell Distribution Width Ratio in Cancer: A Systematic Review and Future Directions. Medicina (Kaunas, Lithuania) 59, (2023). https://doi.org/10.3390/medicina59122124 Yılmaz, A., Mirili, C., Tekin, S. B. & Bilici, M. The ratio of hemoglobin to red cell distribution width predicts survival in patients with gastric cancer treated by neoadjuvant FLOT: a retrospective study. Ir. J. Med. Sci. 189 , 91–102. https://doi.org/10.1007/s11845-019-02153-x (2020). Wang, J. et al. Relationship between the Hemoglobin-to-Red Cell Distribution Width Ratio and All-Cause Mortality in Septic Patients with Atrial Fibrillation: Based on Propensity Score Matching Method. J. Cardiovasc. Dev. disease . 9 https://doi.org/10.3390/jcdd9110400 (2022). Feng, X., Zhang, Y., Li, Q., Wang, B. & Shen, J. Hemoglobin to red cell distribution width ratio as a prognostic marker for ischemic stroke after mechanical thrombectomy. Front. Aging Neurosci. 15 , 1259668. https://doi.org/10.3389/fnagi.2023.1259668 (2023). Ganz, T. Anemia of Inflammation. N. Engl. J. Med. 381 , 1148–1157. https://doi.org/10.1056/NEJMra1804281 (2019). García-Escobar, A. et al. Red Blood Cell Distribution Width is a Biomarker of Red Cell Dysfunction Associated with High Systemic Inflammation and a Prognostic Marker in Heart Failure and Cardiovascular Disease: A Potential Predictor of Atrial Fibrillation Recurrence. High. blood Press. Cardiovasc. prevention: official J. Italian Soc. Hypertens. 31 , 437–449. https://doi.org/10.1007/s40292-024-00662-0 (2024). Lin, Q., Liao, J., Dong, W., Zhou, F. & Xu, Y. The relationship between hemoglobin/red blood cell distribution width ratio and mortality in patients with intracranial hemorrhage: a possible protective effect for the elderly? Intern. Emerg. Med. 18 , 2301–2310. https://doi.org/10.1007/s11739-023-03431-4 (2023). Yang, D. et al. Hemoglobin-to-Red Cell Distribution Width Ratio is Associated with All-Cause Mortality in Critically Ill Patients with Traumatic Brain Injury. Neuroendocrinol. Lett. 44 , 223–233 (2023). Liu, J. & Wang, J. Association between hemoglobin-to-red blood cell distribution width ratio and hospital mortality in patients with non-traumatic subarachnoid hemorrhage. Front. Neurol. 14 , 1180912. https://doi.org/10.3389/fneur.2023.1180912 (2023). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Supplementary Materials Please see Table S1 and Fig. S1 in the Supplementary Materials. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6634211\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":454647786,\"identity\":\"ec6099ff-7851-4bf0-b0e4-8bb61b23288e\",\"order_by\":0,\"name\":\"Yanqun Huang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3PMWvCQBTA8ScnNz289QKiX+GmtKUBB7/IO4S4tNBJMjhcSEkGFVc/hpM4GgKZzt0x4tC5W7r1A7R46dbhfvP7894D8Lx/iIs0baiNUIxvZUPJ0p0MZJWp6zoeBiaeqcbW7mQE8zxoeBUp8xIG13fW4TAojSSsUIENE204iGJF9xOWGkWPc3zo5YuLPg5B2vPeuYUIn/EpY4eLthyUfHUl2pyIM1Q1hG86Z52S1BCforL9ELolssxAr2MMdnwmydbo/GW8LT6+2jaaCMnKzzZZjkSxuZ/8gH8b9zzP8371DdXSShtzGN9sAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"the First Affiliated Hospital of Guangxi Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Yanqun\",\"middleName\":\"\",\"lastName\":\"Huang\",\"suffix\":\"\"},{\"id\":454647787,\"identity\":\"c9acf1ab-25c8-4c2a-aed1-8f712d562a7a\",\"order_by\":1,\"name\":\"Hui Liang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"the First Affiliated Hospital of Guangxi Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hui\",\"middleName\":\"\",\"lastName\":\"Liang\",\"suffix\":\"\"},{\"id\":454647788,\"identity\":\"639c8e66-db49-4d75-87d2-91e846cb31b4\",\"order_by\":2,\"name\":\"Senhu Tang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"the First Affiliated Hospital of Guangxi Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Senhu\",\"middleName\":\"\",\"lastName\":\"Tang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-05-10 10:23:16\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6634211/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6634211/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":83047039,\"identity\":\"bbd3ec64-e178-4306-9899-372bf17e63f3\",\"added_by\":\"auto\",\"created_at\":\"2025-05-19 11:53:59\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":170049,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlowchart illustrating the selection of patients from the MIMIC-IV database.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6634211/v1/80adcd00c028263106249f36.png\"},{\"id\":83047044,\"identity\":\"649de6f4-2551-46ed-942f-3723e9de92ac\",\"added_by\":\"auto\",\"created_at\":\"2025-05-19 11:53:59\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":539648,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAssociation between baseline hemoglobin-to-red blood cell distribution width ratio (HRR) and all-cause hospital mortality. (a). Kaplan–Meier survival analysis curves for all-cause mortality. Kaplan–Meier curves showing cumulative probability of all-cause mortality according to groups at 60 days. (b). Hazard ratios (95% CIs) for mortality according to HRR quartiles after adjusting for age, admission type, race, marital status, diabetes, cerebral cysts, dementia, paralysis, kidney failure, metabolic disorders, anemia, chronic obstructive pulmonary disease, red blood cell, hematocrit, platelet, basophil, glucose, creatinine, blood urea nitrogen, bicarbonate, phosphate, total calcium, chloride, anion gap, prothrombin time, and international normalized ratio. Error bars indicate 95% CIs. The first quartile is the reference. (c). Restricted cubic spline curve for the HRR hazard ratio. Heavy central lines represent the estimated adjusted hazard ratios, with shaded ribbons denoting 95% confidence intervals. The horizontal dotted lines represent the hazard ratio of 1.0. CI, confidence interval. HRR quartiles: Q1 (0.164–0.771), Q2 (0.771–0.932), Q3 (0.932–1.054), Q4 (1.054–1.642).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6634211/v1/410a7a02e63f8bfe7baa0a0e.png\"},{\"id\":83048579,\"identity\":\"eb1a40f2-2ec6-47ca-a0f4-e2f4f98b7a38\",\"added_by\":\"auto\",\"created_at\":\"2025-05-19 12:09:59\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":136473,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eForest plots of hazard ratios for the hospital mortality in different subgroups. HR, hazard ratio; CI, confidence interval.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6634211/v1/7135e07770f2867baaaa41d7.png\"},{\"id\":83047564,\"identity\":\"cd3c17aa-42e8-4e7b-9a37-5c2a01964833\",\"added_by\":\"auto\",\"created_at\":\"2025-05-19 12:01:59\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":853285,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDynamic changes of hemoglobin-to-red blood cell distribution width ratio (HRR) (a), hemoglobin (Hb) (b) and red blood cell distribution width (RDW) (c) in intracerebral hemorrhage (ICH) patients in 14 days post admission. The data was depicted as mean and 95% confidence interval. (d) Number of ICH patients with available RDW and Hb measurements at each time point.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6634211/v1/9f1e49a9469e8e56a37aefc3.png\"},{\"id\":83047043,\"identity\":\"f2f31693-ca53-4398-9947-a054c6ceee67\",\"added_by\":\"auto\",\"created_at\":\"2025-05-19 11:53:59\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":435392,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTemporal trends of hemoglobin-to-red blood cell distribution width ratio (HRR) in different subgroups.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6634211/v1/137906086d88fd28a6dae309.png\"},{\"id\":83047046,\"identity\":\"c0af5f81-603b-4578-8a0f-1202502a6f9e\",\"added_by\":\"auto\",\"created_at\":\"2025-05-19 11:53:59\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":920399,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePearson correlation analysis between hemoglobin-to-red blood cell distribution width ratio (HRR) and mortality over time. (a). HRR-mortality Pearson coefficients over time of patients classified by HRR quartiles. (b-d). Pearson correlation heatmap between mortality and HRR (b), hemoglobin (Hb) (c), red cell distribution width (RDW) (d).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6634211/v1/f016f79deb816c2fd3c0612c.png\"},{\"id\":83047567,\"identity\":\"5727a856-9046-46b6-965a-16a3993794da\",\"added_by\":\"auto\",\"created_at\":\"2025-05-19 12:01:59\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":331079,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eForest plots of hazard ratios for mortality associated with daily hemoglobin-to-red blood cell distribution width ratio (HRR) and HRR changes in 14 days post admission. (a) Results from Cox proportional hazards regression analyses. (b) Results from logistic regression analyses. Both analyses were adjusted for age, admission type, race, marital status, diabetes, cerebral cysts, dementia, paralysis, kidney failure, metabolic disorders, anemia, chronic obstructive pulmonary disease, red blood cell, hematocrit, platelet, basophil, glucose, creatinine, blood urea nitrogen, bicarbonate, phosphate, total calcium, chloride, anion gap, prothrombin time, and international normalized ratio. HR, hazard ratio; OR, odds ratio; CI, confidence interval\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6634211/v1/60d4d4861ce1f2107ff9e177.png\"},{\"id\":83047565,\"identity\":\"353c9e50-4f44-4652-bfb3-55566d350944\",\"added_by\":\"auto\",\"created_at\":\"2025-05-19 12:01:59\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":469489,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eROC curves and AUCs of mortality prediction using (a) hemoglobin-to-red blood cell distribution width ratio (HRR), (b) hemoglobin (Hb), and (c) red cell distribution width (RDW) across post-admission time points as predictors. The logistic regression model was developed and validated using 998 patients with available daily Hb and RDW measurements during the first 7 days of hospitalization (155 non-survivors), randomly split into training (n=699) and test (n=299) sets. Shown are the prediction results for the test set.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6634211/v1/ab8dffd3fe1107e00a2335f7.png\"},{\"id\":83047052,\"identity\":\"be584292-4a64-41f0-ae8f-c1e569c9a0e8\",\"added_by\":\"auto\",\"created_at\":\"2025-05-19 11:53:59\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":815571,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAssociation between baseline hemoglobin-to-red blood cell distribution width ratio (HRR) cut-off and mortality. (a) Kaplan–Meier curves for all-cause mortality of intracerebral hemorrhage patients stratified by HRR cut-off. (b) HRR trends over time of patients stratified by baseline HRR cut-off.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6634211/v1/0dca63cd597bb4131ac90d93.png\"},{\"id\":90697759,\"identity\":\"b7fcacf8-5c9a-4149-aed6-7a96999fcb9f\",\"added_by\":\"auto\",\"created_at\":\"2025-09-05 21:01:38\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":6086044,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6634211/v1/138c9aa0-7abe-4359-97a8-8dc3de489d04.pdf\"},{\"id\":83047037,\"identity\":\"8edbd5d5-8ec0-4db5-adf9-14a5f39ebaf8\",\"added_by\":\"auto\",\"created_at\":\"2025-05-19 11:53:59\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":218449,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary Materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePlease see Table S1 and Fig. S1 in the Supplementary Materials.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Supplementarymaterials.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6634211/v1/e1ef4028d0718045cf6493e9.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Association of dynamic changes of hemoglobin-to-red blood cell distribution width ratio with all-cause mortality in patients with intracerebral hemorrhage\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eIntracerebral hemorrhage (ICH), caused by ruptured intracranial vessels leading to blood extravasation into brain tissue\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e\\u0026zwnj;, represents the most devastating stroke subtype, accounting for 15\\u0026ndash;20% of all strokes and driving disproportionately high rates of acute mortality, long-term disability and socioeconomic burden\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e. Over recent decades, shifting vascular risk profiles\\u0026zwnj; have contributed to rising ICH incidence\\u003csup\\u003e3,4\\u003c/sup\\u003e,and while fewer than 50% of patients survive beyond one year, with survivors often facing permanent functional impairments and catastrophic healthcare costs\\u003csup\\u003e1\\u003c/sup\\u003e. This dire prognosis underscores the critical need for dynamic biomarker monitoring during hospitalization to enable real-time risk stratification and targeted interventions\\u0026zwnj;. Recent research has focused on the identification of readily measurable biological markers capable of reliably assessing patient prognosis\\u003csup\\u003e5,\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e. Their routine availability, low cost, and strong prognostic value make them valuable tools in both clinical and public health settings.\\u003c/p\\u003e \\u003cp\\u003eSome studies aimed to discover prognostic biomarkers for stratifying high-risk ICH cohorts and guiding clinical decisions. \\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e. Routine blood markers are gaining clinical relevance due to easy availability and their ability to reflect various health and disease states, especially hemoglobin (Hb) and red cell distribution width (RDW), which been proven to be two key blood parameters for ICH patients\\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e. Hb serves as a quantitative indicator of erythrocytic oxygen-carrying capacity. Hb depletion demonstrates significant correlation not merely with anemia progression but also functions as an independent prognostic indicator across multisystem chronic pathologies. Studies have shown that low Hb levels have been associated with adverse outcomes in cardiovascular and cerebrovascular diseases, such as acute coronary syndrome\\u003csup\\u003e\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u003c/sup\\u003e, heart failure\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e, and ischemic stroke\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e. Particularly, while elevated Hb is associated with protective effects in spontaneous ICH, low levels may worsen tissue damage after bleeding\\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e\\u003c/sup\\u003e. RDW measures red blood cell size variation, with higher values suggesting inflammation or oxidative stress. Elevated RDW has been linked to a greater incidence of various type of stroke, including ischemic stroke\\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u003c/sup\\u003e and ICH\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u003c/sup\\u003e. RDW independently predicted long-term mortality and median RDW levels within the first month after admission were better predictors of long-term mortality than RDW levels on admission\\u003csup\\u003e\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, complex interactions between Hb and RDW have been documented\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR19\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u003c/sup\\u003e, suggesting their interdependent relationship. The hemoglobin-to-red cell distribution width ratio (HRR) has emerged as an innovative combined indicator, which offers the key advantage of evaluating both blood oxygen transport efficiency and associated erythropoietic stress or impairment\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e, influencing the progression of various diseases\\u003csup\\u003e\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u003c/sup\\u003e. A study demonstrated a significant inverse correlation between HRR and stroke incidence, with each unit increment in HRR corresponding to a 58% reduction in stroke risk\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u003c/sup\\u003e. Besides, previous research has shown that a lower HRR was significantly associated with an increased risk of mortality in various cardiovascular and cerebrovascular diseases, including heart failure\\u003csup\\u003e\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u003c/sup\\u003e and acute ischemic stroke\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eDespite preliminary evidence linking HRR to stroke risks and clinical outcomes, the critical temporal association between dynamic HRR trajectories during hospitalization and mortality risk in ICH patients remains unclear. Real-time monitoring and analysis of HRR fluctuations may provide critical prognostic insights, as these variations reflect metabolic instability and disease severity. Such monitoring enables timely interventions to prevent acute complications, thereby potentially reducing ICH-related mortality rates. Therefore, this study aimed to investigate the association between HRR and in-hospital mortality of ICH utilizing data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV, version 3.1) database. Furthermore, we explored the temporal changes in HRR during hospitalization to evaluate its potential as a predictive clinical indicator.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCohort selection\\u003c/h2\\u003e \\u003cp\\u003eThis retrospective study analyzed data from MIMIC-IV 3.1, which is a publicly available critical care database maintained by the Massachusetts Institute of Technology. The MIMIC-IV 3.1 database comprises more than 220 thousand patients\\u0026rsquo; demographics, vital signs, laboratory indicators, and diagnoses using International Classification of Diseases and Ninth Revision (ICD-9) and Tenth Revision (ICD-10) codes. One author (Yanqun Huang) obtained database access following Institutional Review Board approval (certification number: 39674606).\\u003c/p\\u003e \\u003cp\\u003eWe identified ICH patients using ICD-9 codes (430\\u0026ndash;432) and ICD-10 codes (I60-I62). Among 223,452 adult patients in the MIMIC-IV 3.1 database, 217,300 without ICH were excluded first. After excluding 121 patients with a hospital stay duration of less than 2 days, we further excluded 3,524 patients with missing Hb and RDW data on the first day of admission, followed by 60 patients with fewer than 2 HRR measurements during hospitalization. Finally, a total of 2,447 patients with ICH were included in the cohort and divided into four groups based on quartiles of baseline HRR values at admission (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eFeature selection\\u003c/h3\\u003e\\n\\u003cp\\u003eBaseline characteristics at admission included demographics (age, sex, marital status and race), hospitalization details (admission type, length of stay), 16 comorbidities (prevalence\\u0026thinsp;\\u0026ge;\\u0026thinsp;300 patients; e.g., hypertension, diabetes, cerebral infarction, cerebral cysts, dementia, paralysis, peripheral vascular disease [PVD], myocardial infarction, heart failure, other heart disease, respiratory failure, kidney failure, metabolic disorders, anemia, cancer, chronic obstructive pulmonary diseases [COPD]), and 24 baseline laboratory indicators (\\u0026lt;\\u0026thinsp;50% missing values; e.g., RDW, Hb, red blood cells [RBC], white blood cells [WBC], hematocrit, platelet count [PLT], neutrophils, monocytes, basophils, eosinophils, lymphocytes, glucose, creatinine, blood urea nitrogen [BUN], bicarbonate, phosphate, total calcium, chloride, potassium, magnesium, anion gap, prothrombin time [PT], activated partial thromboplastin time [APTT] and international normalized ratio [INR]). Missing baseline laboratory indicators were imputed via the Random Forest algorithm, with all non-missing variables as predictors. Time-varying RDW and Hb values (recorded daily for each patient) were retained. For each patient, HRR was calculated as Hb (g/dL) divided by RDW (%) and rounded to two decimal places. The outcome was all-cause mortality during hospitalization.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eFor baseline characteristics, continuous variables were summarized as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (normally distributed) or median with interquartile range (non-normal), and compared using Student\\u0026rsquo;s t-test or Mann-Whitney U test, respectively. Categorical variables were expressed as counts (percentages) and analyzed with Chi-square test or Fisher\\u0026rsquo;s exact test.\\u003c/p\\u003e \\u003cp\\u003e\\u0026zwnj;Kaplan-Meier survival analysis was employed to compare mortality risk across HRR quartiles, with group differences assessed using log-rank tests. Cox proportional hazards models\\u0026zwnj; estimated hazard ratios (HR) and 95% confidence interval (CI) for the association between baseline HRR (modeled as continuous or ordinal variables, with the first quartile as reference) and all-cause mortality. Clinically relevant variables were included in univariate Cox regression analysis. Three hierarchical models were then constructed: Model 1 contained only baseline HRR without adjustment; Model 2 was adjusted for demographics; and \\u0026zwnj;Model 3 was further adjusted for demographics and covariates selected via univariate analysis. Additionally, a restricted cubic spline (RCS) regression model with four knots analyzed the nonlinear association between baseline HRR and mortality, and P values for nonlinear trends were calculated. To assess the consistency of the association between HRR and mortality within the general population and to identify specific population characteristics, we performed subgroup analyses and interaction tests based on age (\\u0026le;\\u0026thinsp;65 and \\u0026gt;\\u0026thinsp;65 years), sex, hypertension, diabetes, cerebral infarction, cerebral cysts, dementia, paralysis, PVD, respiratory failure, metabolic disorders, anemia and COPD. The interactions between HRR and stratification variables were examined using likelihood ratio tests. A two-tailed P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was regarded as statistically significant.\\u003c/p\\u003e \\u003cp\\u003eFor temporal analyses, we employed Pearson correlation analysis to examine correlations between daily HRR and mortality. Longitudinal trends of HRR during hospitalization were analyzed using linear regression. Receiver operating characteristic (ROC) curves were constructed to evaluate HRR's time-specific predictive performance for mortality in ICH patients, with predictive accuracy quantified by the area under the ROC curve (AUC). Cox proportional hazards regression was conducted to analyze the dynamic association between time-dependent daily HRR and mortality, adjusted for multiple confounders. To assess the impact of HRR fluctuations relative to admission values, we calculated daily deviations from baseline HRR and analyzed their association with mortality using logistic regression.\\u003c/p\\u003e \\u003cp\\u003eBesides, we identified the optimal baseline HRR cut-off value using the \\u0026lsquo;surv_cutpoint\\u0026rsquo; function (survminer package) in R 4.3.1, then categorized patients into high- and low-HRR groups via \\u0026lsquo;surv_categorize\\u0026rsquo;\\u0026zwnj; function. Kaplan-Meier survival curves with Log-rank tests were employed to compare mortality between these predefined groups\\u0026zwnj;.\\u003c/p\\u003e \\u003cp\\u003eAll statistical analyses were performed using R 4.3.1 and Python 3.7.13.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBaseline characteristics\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e presents the baseline characteristics of 2,447 ICH patients stratified into quartiles based on HRR (Q1: 0.16\\u0026ndash;0.77; Q2: 0.77\\u0026ndash;0.93; Q3: 0.93\\u0026ndash;1.05; Q4: 1.05\\u0026ndash;1.64). The cohort demonstrated a median age of 68 years (interquartile range [IQR]: 56\\u0026ndash;79), with male predominance (1,482 [60.56%]). The median HRR was 0.93 (IQR: 0.77\\u0026ndash;1.05). The median hospital length of stay was 8 days (IQR 4\\u0026ndash;14), with an observed all-cause hospital mortality rate of 15.57% (381 patients). The median HRR (IQR) for each quartile was 0.64 (0.53\\u0026ndash;0.71), 0.86 (0.82\\u0026ndash;0.90), 0.99 (0.96\\u0026ndash;1.02), and 1.14 (1.09\\u0026ndash;1.20), respectively. Patients in Q4 were the youngest (median age 61 years, IQR 51\\u0026ndash;71), while Q2 patients were the oldest (72 years, IQR 60\\u0026ndash;82). Mortality rates progressively decreased across ascending HRR quartiles (Q1 to Q4: 23.73\\u0026ndash;9.65%; P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Higher HRR quartiles demonstrated inverse associations with most comorbidities (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 except PVD, myocardial infarction, and metabolic disorders). Patients in Q4 exhibited significantly higher levels of Hb, RBC, WBC, hematocrit, PLT and total calcium (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), alongside lower levels of RDW, BUN, phosphate, and PT (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBaseline characteristics of participants categorized by HRR quartiles\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOverall (n\\u0026thinsp;=\\u0026thinsp;2447)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ1 (n\\u0026thinsp;=\\u0026thinsp;611)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eQ2 (n\\u0026thinsp;=\\u0026thinsp;595)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eQ3 (n\\u0026thinsp;=\\u0026thinsp;619)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eQ4 (n\\u0026thinsp;=\\u0026thinsp;622)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDemographic characters\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge, years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e68.00 (56.00\\u0026ndash;79.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69.00 (60.00\\u0026ndash;80.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e72.00 (60.00\\u0026ndash;82.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e68.00 (55.00\\u0026ndash;79.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e61.00 (51.00\\u0026ndash;71.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex (males)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1482 (60.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e331 (54.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e318 (53.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e355 (57.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e478 (76.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarital status (Married)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1646 (67.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e372 (60.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e375 (63.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e445 (71.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e454 (72.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRace (white)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e831 (33.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e225 (36.82)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e192 (32.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e199 (32.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e215 (34.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.261\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHospitalization information\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAdmission type (emergency)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e465 (19.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e123 (20.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e117 (19.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e104 (16.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e121 (19.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.439\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLength of stay, days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8.00 (4.00\\u0026ndash;14.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.00 (5.00\\u0026ndash;16.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.00 (4.00\\u0026ndash;13.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.00 (4.00\\u0026ndash;13.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e8.00 (4.00\\u0026ndash;14.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.037\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eComorbidities\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHypertension\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1525 (62.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e438 (71.69)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e384 (64.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e363 (58.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e340 (54.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e896 (36.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e287 (46.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e236 (39.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e197 (31.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e176 (28.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCerebral infarction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e459 (18.76)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e151 (24.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e105 (17.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e108 (17.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e95 (15.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCerebral cysts\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e545 (22.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e165 (27.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e136 (22.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e132 (21.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e112 (18.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDementia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e307 (12.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e156 (25.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e63 (10.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e54 (8.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e34 (5.47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eParalysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e469 (19.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e172 (28.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e115 (19.33)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e98 (15.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e84 (13.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePeripheral vascular disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e531 (21.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e125 (20.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e153 (25.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e128 (20.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e125 (20.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.057\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMyocardial infarction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e724 (29.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e166 (27.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e182 (30.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e197 (31.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e179 (28.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.297\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHeart failure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e365 (14.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e169 (27.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e71 (11.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e68 (10.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e57 (9.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther heart disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e597 (24.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e202 (33.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e159 (26.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e133 (21.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e103 (16.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRespiratory failure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e570 (23.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e176 (28.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e152 (25.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e145 (23.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e97 (15.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eKidney failure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e440 (17.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e199 (32.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e108 (18.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e78 (12.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e55 (8.84)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMetabolic disorders\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1070 (43.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e298 (48.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e239 (40.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e273 (44.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e260 (41.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.016\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnemia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e885 (36.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e202 (33.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e217 (36.47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e227 (36.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e239 (38.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.259\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCancer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e511 (20.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e273 (44.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e120 (20.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e80 (12.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e38 (6.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCOPD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1746 (71.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e465 (76.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e438 (73.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e440 (71.08)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e403 (64.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLaboratory tests\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHRR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.93 (0.77\\u0026ndash;1.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.64 (0.53\\u0026ndash;0.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.86 (0.82\\u0026ndash;0.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.99 (0.96\\u0026ndash;1.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.14 (1.09\\u0026ndash;1.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRDW, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13.70 (13.00-14.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16.00 (14.80\\u0026ndash;17.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.00 (13.40\\u0026ndash;14.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13.40 (13.00-13.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e12.90 (12.40\\u0026ndash;13.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHemoglobin, g/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12.70 (11.30\\u0026ndash;14.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.10 (8.90\\u0026ndash;10.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.00 (11.50\\u0026ndash;12.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13.20 (12.70\\u0026ndash;13.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e14.80 (14.10\\u0026ndash;15.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRBC, m/uL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.21 (3.75\\u0026ndash;4.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.43 (2.99\\u0026ndash;3.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.98 (3.74\\u0026ndash;4.31)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.32 (4.10\\u0026ndash;4.58)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.82 (4.49\\u0026ndash;5.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWBC, K/uL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.30 (7.10\\u0026ndash;12.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.30 (6.00-11.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.20 (7.00-12.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9.60 (7.50\\u0026ndash;12.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e10.10 (7.80\\u0026ndash;13.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHematocrit, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38.10 (34.30\\u0026ndash;41.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31.10 (27.80\\u0026ndash;34.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e36.20 (34.50\\u0026ndash;38.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e39.30 (37.70\\u0026ndash;41.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e43.40 (41.30\\u0026ndash;45.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePLT, K/uL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e217.00 (167.00-273.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e194.00 (126.00-277.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e217.00 (166.25-275.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e222.00 (178.50\\u0026ndash;273.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e227.00 (187.00-271.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeu, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e77.80 (67.17\\u0026ndash;85.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e75.55 (64.83\\u0026ndash;84.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e79.10 (67.85\\u0026ndash;85.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e78.65 (68.95\\u0026ndash;86.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e78.50 (67.47\\u0026ndash;85.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMono, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.30 (3.60\\u0026ndash;7.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.90 (3.80\\u0026ndash;8.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.30 (3.73\\u0026ndash;7.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.00 (3.30\\u0026ndash;6.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e5.30 (3.68\\u0026ndash;7.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBaso, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.40 (0.20\\u0026ndash;0.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.30 (0.10\\u0026ndash;0.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.30 (0.20\\u0026ndash;0.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.40 (0.20\\u0026ndash;0.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.40 (0.20\\u0026ndash;0.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEos, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.70 (0.20\\u0026ndash;1.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00 (0.10\\u0026ndash;2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.70 (0.20\\u0026ndash;1.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.70 (0.20\\u0026ndash;1.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.60 (0.20\\u0026ndash;1.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.033\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLym, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13.80 (8.00-21.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.00 (8.22\\u0026ndash;21.82)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13.00 (7.50-21.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13.85 (7.70-21.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e14.30 (8.70\\u0026ndash;22.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.358\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGlucose, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e124.00 (104.00-157.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e118.00 (99.00-155.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e125.00 (104.00-158.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e126.50 (107.00-157.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e124.00 (106.00-156.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCreatinine, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.90 (0.70\\u0026ndash;1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.00 (0.70\\u0026ndash;1.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.90 (0.70\\u0026ndash;1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.80 (0.70-1.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.90 (0.80\\u0026ndash;1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBUN, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17.00 (13.00\\u0026ndash;23.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.00 (14.00\\u0026ndash;33.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17.00 (13.00\\u0026ndash;24.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e16.00 (13.00\\u0026ndash;20.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e15.00 (12.00\\u0026ndash;20.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBicarbonate, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24.00 (22.00\\u0026ndash;26.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.00 (21.00\\u0026ndash;26.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24.00 (22.00\\u0026ndash;26.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e24.00 (22.00\\u0026ndash;26.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e24.00 (22.00\\u0026ndash;27.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePhosphate, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.30 (2.80\\u0026ndash;3.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.50 (2.90\\u0026ndash;4.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.30 (2.80\\u0026ndash;3.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.20 (2.80\\u0026ndash;3.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.10 (2.70\\u0026ndash;3.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal calcium, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.00 (8.50\\u0026ndash;9.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.80 (8.40\\u0026ndash;9.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.90 (8.40\\u0026ndash;9.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9.00 (8.60\\u0026ndash;9.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e9.20 (8.70\\u0026ndash;9.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChloride, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102.00 (99.00-105.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e102.00 (98.00-105.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e103.00 (100.00-105.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e103.00 (100.00-105.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e102.00 (99.00-105.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.033\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSerum sodium, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e139.00 (137.00-141.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e138.00 (136.00-141.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e139.00 (136.00-141.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e139.00 (137.00-141.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e139.00 (137.00-141.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSerum potassium, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.10 (3.70\\u0026ndash;4.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.10 (3.80\\u0026ndash;4.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.00 (3.70\\u0026ndash;4.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.00 (3.70\\u0026ndash;4.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.00 (3.70\\u0026ndash;4.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.013\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSerum magnesium, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.90 (1.80\\u0026ndash;2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.00 (1.70\\u0026ndash;2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.90 (1.70\\u0026ndash;2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.00 (1.80\\u0026ndash;2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.00 (1.80\\u0026ndash;2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.074\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnion Gap, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15.00 (13.00\\u0026ndash;17.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.00 (13.00\\u0026ndash;17.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15.00 (13.00\\u0026ndash;17.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e15.00 (13.00\\u0026ndash;17.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e15.00 (13.00\\u0026ndash;17.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.183\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePT, sec\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12.40 (11.40-14.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.50 (11.90\\u0026ndash;16.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.50 (11.60\\u0026ndash;14.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12.30 (11.30\\u0026ndash;13.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e11.90 (11.10-13.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAPTT, sec\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e28.10 (25.30\\u0026ndash;31.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e29.50 (25.90-35.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28.10 (25.50-31.48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e27.45 (25.00-30.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e27.80 (25.15\\u0026ndash;30.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eINR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.10 (1.00-1.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.20 (1.10\\u0026ndash;1.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.10 (1.00-1.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.10 (1.00-1.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.10 (1.00-1.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eOutcome\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHospital mortality\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e381 (15.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e145 (23.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e93 (15.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e83 (13.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e60 (9.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eBaseline characteristics between survivors and non-survivors are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Non-survivors were older, and exhibited a higher prevalence of hypertension, diabetes, cerebral cysts, dementia, paralysis, PVD, respiratory failure, kidney failure, metabolic disorders, cancer, and COPD (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Median HRR was significantly lower in non-survivors compared to survivors (0.86 vs. 0.94; P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Most laboratory indicators differed significantly between non-survivors and survivors, with lower Hb, RBC, hematocrit, and PLT in non-survivors (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001)\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBaseline characteristic of survivors and non-survivors\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOverall (n\\u0026thinsp;=\\u0026thinsp;2447)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSurvivors (n\\u0026thinsp;=\\u0026thinsp;2066)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNon-survivors (n\\u0026thinsp;=\\u0026thinsp;381)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDemographic characters\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge, years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e68.00 (56.00\\u0026ndash;79.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e67.00 (55.00\\u0026ndash;78.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e70.00 (60.00\\u0026ndash;81.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex (males)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1482 (60.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1256 (60.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e226 (59.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.588\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarital status (Married)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1646 (67.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1317 (63.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e329 (86.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRace (white)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e831 (33.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e652 (31.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e179 (46.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHospitalization information\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAdmission type (emergency)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e465 (19.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e416 (20.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e49 (12.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLength of stay, days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8.00 (4.00\\u0026ndash;14.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.00 (4.00\\u0026ndash;15.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.00 (3.00\\u0026ndash;13.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eComorbidities\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHypertension\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1525 (62.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1253 (60.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e272 (71.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e896 (36.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e708 (34.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e188 (49.34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCerebral infarction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e459 (18.76)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e377 (18.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e82 (21.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.132\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCerebral cysts\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e545 (22.27)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e339 (16.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e206 (54.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDementia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e307 (12.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e233 (11.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e74 (19.42)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eParalysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e469 (19.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e378 (18.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e91 (23.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.011\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePeripheral vascular disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e531 (21.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e433 (20.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e98 (25.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.038\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMyocardial infarction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e724 (29.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e599 (28.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e125 (32.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.134\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHeart failure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e365 (14.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e305 (14.76)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60 (15.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther heart disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e597 (24.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e498 (24.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e99 (25.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.432\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRespiratory failure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e570 (23.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e441 (21.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e129 (33.86)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eKidney failure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e440 (17.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e317 (15.34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e123 (32.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMetabolic disorders\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1070 (43.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e815 (39.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e255 (66.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnemia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e885 (36.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e748 (36.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e137 (35.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.926\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCancer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e511 (20.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e394 (19.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e117 (30.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCOPD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1746 (71.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1449 (70.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e297 (77.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLaboratory tests\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHRR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.93 (0.77\\u0026ndash;1.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.94 (0.79\\u0026ndash;1.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.86 (0.68-1.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRDW, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13.70 (13.00-14.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.60 (13.00-14.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.20 (13.30\\u0026ndash;15.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHemoglobin, g/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12.70 (11.30\\u0026ndash;14.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.80 (11.40\\u0026ndash;14.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.30 (10.50\\u0026ndash;13.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRBC, m/uL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.21 (3.75\\u0026ndash;4.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.23 (3.80\\u0026ndash;4.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.06 (3.46\\u0026ndash;4.57)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWBC, K/uL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.30 (7.10\\u0026ndash;12.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.20 (7.00-12.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.90 (7.70\\u0026ndash;15.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHematocrit, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38.10 (34.30\\u0026ndash;41.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38.30 (34.70-41.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e36.90 (32.70\\u0026ndash;40.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePLT, K/uL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e217.00 (167.00-273.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e222.00 (173.00-276.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e192.50 (136.75\\u0026ndash;252.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeu, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e77.80 (67.17\\u0026ndash;85.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e77.20 (66.62\\u0026ndash;85.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e83.25 (71.43\\u0026ndash;88.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMono, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.30 (3.60\\u0026ndash;7.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.40 (3.70\\u0026ndash;7.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.05 (3.42\\u0026ndash;7.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.258\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBaso, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.40 (0.20\\u0026ndash;0.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.40 (0.20\\u0026ndash;0.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.25 (0.10\\u0026ndash;0.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEos, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.70 (0.20\\u0026ndash;1.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.70 (0.20\\u0026ndash;1.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.30 (0.00\\u0026ndash;1.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLym, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13.80 (8.00-21.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.55 (8.70\\u0026ndash;22.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.45 (5.93\\u0026ndash;16.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGlucose, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e124.00 (104.00-157.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e121.00 (103.00-152.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e146.00 (116.00-184.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCreatinine, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.90 (0.70\\u0026ndash;1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.90 (0.70\\u0026ndash;1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00 (0.70\\u0026ndash;1.45)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBUN, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17.00 (13.00\\u0026ndash;23.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16.00 (13.00\\u0026ndash;22.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20.00 (15.00-29.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBicarbonate, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24.00 (22.00\\u0026ndash;26.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.00 (22.00\\u0026ndash;26.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23.00 (20.00\\u0026ndash;26.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePhosphate, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.30 (2.80\\u0026ndash;3.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.30 (2.80\\u0026ndash;3.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.30 (2.80\\u0026ndash;4.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.102\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal calcium, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.00 (8.50\\u0026ndash;9.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.00 (8.60\\u0026ndash;9.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.70 (8.20\\u0026ndash;9.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChloride, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102.00 (99.00-105.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e103.00 (100.00-105.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e101.00 (98.00-105.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSerum sodium, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e139.00 (137.00-141.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e139.00 (137.00-141.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e139.00 (136.00-141.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.113\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSerum potassium, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.10 (3.70\\u0026ndash;4.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.10 (3.70\\u0026ndash;4.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.10 (3.70\\u0026ndash;4.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.095\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSerum magnesium, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.90 (1.80\\u0026ndash;2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.90 (1.80\\u0026ndash;2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.90 (1.70\\u0026ndash;2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.176\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnion Gap, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15.00 (13.00\\u0026ndash;17.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.00 (13.00\\u0026ndash;17.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16.00 (14.00\\u0026ndash;19.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePT, sec\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12.40 (11.40-14.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.30 (11.40\\u0026ndash;13.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.90 (11.70\\u0026ndash;15.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAPTT, sec\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e28.10 (25.30\\u0026ndash;31.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.10 (25.30\\u0026ndash;31.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28.20 (25.10-32.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.493\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eINR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.10 (1.00-1.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.10 (1.00-1.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.10 (1.00-1.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociation between baseline HRR and mortality\\u003c/h2\\u003e \\u003cp\\u003eUnivariate analysis (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e) indicated that higher HRR was associated with reduced mortality risk in ICH patients. Significant mortality factors (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) of mortality included HRR, age, admission type, race, marital status, diabetes, cerebral cysts, dementia, paralysis, kidney failure, metabolic disorders, anemia, COPD, RDW, Hb, RBC, hematocrit, PLT, basophil, glucose, creatinine, BUN, bicarbonate, phosphate, total calcium, chloride, anion gap, PT, and INR.\\u003c/p\\u003e \\u003cp\\u003eBoth unadjusted and adjusted models demonstrated a negative relationship between HRR and mortality (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). When modeled continuously, lower HRR values were independently associated with elevated mortality risk across all models: unadjusted (HR: 0.346; 95% CI: 0.226\\u0026ndash;0.528; P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), partially adjusted (0.346; 0.223\\u0026ndash;0.537; P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and fully adjusted models (0.115, 0.048\\u0026ndash;0.280; P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). When analyzed as ordinal quartiles, higher HRR categories showed graded mortality reductions. In unadjusted analysis, the highest quartile (Q4) exhibited a 56.1% risk reduction compared to Q1 (HR: 0.439; 95% CI: 0.323\\u0026ndash;0.597; P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). This association persisted after adjustment, with Q4 showing 53.1% and 70.7% risk reductions in partially (HR: 0.469; 95% CI: 0.342\\u0026ndash;0.642; P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and fully adjusted models (HR: 0.293; 95% CI: 0.186\\u0026ndash;0.464; P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), respectively.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eCox proportional hazard (HR) for all-cause mortality\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"10\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCategories\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eModel1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c7\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eModel2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c10\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003eModel3\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP for trend\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eHR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eP for trend\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eHR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eP for trend\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eContinuous variable per unit\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.346 (0.226\\u0026ndash;0.528)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.346 (0.223\\u0026ndash;0.537)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.115 (0.048\\u0026ndash;0.280)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQuartile\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQ1 (N\\u0026thinsp;=\\u0026thinsp;615)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eref\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eref\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eref\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQ2 (N\\u0026thinsp;=\\u0026thinsp;613)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.751 (0.579\\u0026ndash;0.975)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.032\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.724 (0.558\\u0026ndash;0.940)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.015\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.657 (0.483\\u0026ndash;0.893)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQ3 (N\\u0026thinsp;=\\u0026thinsp;610)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.682 (0.521\\u0026ndash;0.891)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.644 (0.492\\u0026ndash;0.844)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.500 (0.350\\u0026ndash;0.713)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eQ4 (N\\u0026thinsp;=\\u0026thinsp;609)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.439 (0.323\\u0026ndash;0.597)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.469 (0.342\\u0026ndash;0.642)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.293 (0.186\\u0026ndash;0.464)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eKaplan-Meier curves demonstrated a stepwise reduction in all-cause mortality across ascending HRR quartiles (Q1: 23.73%, Q2: 15.63%, Q3: 13.41%, Q4: 9.65%; log-rank P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea). Patients in higher HRR quartiles exhibited progressively lower mortality risk compared to lower quartiles (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb). RCS analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec) indicated that the relationship between HRR and hospital mortality risk was more likely to be linear (P for non-linearity\\u0026thinsp;=\\u0026thinsp;0.213).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSubgroup analyses (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) indicated a consistent inverse association between elevated HRR and lower mortality risk across most subgroups (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). However, the association was not statistically significant in three subgroups: patients with PVD, anemia, and non-COPD cases. No significant interaction effects were detected across subgroups for any confounder (all P for interaction\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eTemporal trends of HRR\\u003c/h3\\u003e\\n\\u003cp\\u003eFigure\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea-c depicted the temporal trends in HRR, Hb, and RDW during the 14-day post-admission period for ICH patients. Both survivors and non-survivors exhibited progressive declines in HRR and Hb levels, while RDW levels increased gradually. From admission to day 14, mean HRR decreased significantly in non-survivors (0.835 to 0.553) and survivors (0.919 to 0.710) (both P for trend\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Linear regression analyses revealed temporal associations for each biomarker: HRR declined at a rate of -0.014 per day (r = -0.971), Hb at -0.157 per day (r = -0.963), and RDW increased at 0.054 per day (r\\u0026thinsp;=\\u0026thinsp;0.994) (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Temporal trajectories during the whole hospitalization of HRR, Hb, and RDW were visualized in Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003eAt each time point during the 14-day post-admission period, non-survivors exhibited significantly lower HRR (mean difference: 0.128) and Hb (mean difference: 1.074), along with higher RDW (mean difference: 1.303), compared to the survivors (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 by Mann-Whitney U test at each time point). Longitudinal analysis revealed progressively widening 95% CIs for HRR, Hb and RDW, reflecting increased variability over time (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea\\u0026ndash;c). This variability likely stems from the diminishing cohort size, as fewer patients underwent repeated laboratory assessments during hospitalization (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ed). Despite greater variability resulting from reduced sample size, the directional trends remained statistically robust.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSubgroup analyses by HRR temporal trends across demographic and clinical factors (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e) revealed consistent downward trajectories in all subgroups (all P for trend\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), though decline rates varied. Males maintained higher HRR levels throughout hospitalization compared to females (mean difference: 0.078, P\\u0026thinsp;=\\u0026thinsp;0.003). Age-stratified analysis showed comparable HRR declines between older (\\u0026gt;\\u0026thinsp;65 years) and younger patients (\\u0026le;\\u0026thinsp;65 years, P\\u0026thinsp;=\\u0026thinsp;0.448). Patients without chronic comorbidities (e.g., hypertension, diabetes, cerebral cysts, dementia, paralysis, and major organ failure) maintained significantly higher HRR levels than those with (all P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The most pronounced differences were observed in patients with cancer (mean difference: 0.196, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), kidney failure (0.124, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), dementia (0.109, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), cerebral cysts (0.095, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and heart failure (0.081, P\\u0026thinsp;=\\u0026thinsp;0.002).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003eAssociations between dynamic HRR and mortality\\u003c/h3\\u003e\\n\\u003cp\\u003eFigure\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e illustrated Pearson correlation coefficients between HRR, Hb, RDW, and mortality risk across post-admission time points. Most time points demonstrated negative correlations between HRR, Hb and mortality, alongside positive RDW-mortality associations. These correlations strengthened as discharge neared, with HRR-mortality and Hb-mortality correlations becoming progressively more negative (HRR: from \\u0026minus;\\u0026thinsp;0.141 to -0.281; Hb: from \\u0026minus;\\u0026thinsp;0.101 to -0.238) and RDW-mortality correlations increasing from 0.155 to 0.261 (all P for trend\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Notably, the HRR-mortality correlation intensified sharply within the first four days post-admission, particularly in baseline HRR quartiles 3 (-0.004 to -0.238) and 4 (0.083 to -0.223) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003ea). Adjacent time points exhibited a higher correlation in HRR values, suggesting a closer relationship within short time intervals (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eb) and similar phenomena were observed in Hb and RDW (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003ec and d).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eDaily HRR levels during the 14-day post-admission period demonstrated a consistent inverse association with mortality risk, with adjusted hazard ratios (HRs)\\u0026thinsp;\\u0026lt;\\u0026thinsp;1.0 and P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 at all time points (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). Day-to-day HRR variability, quantified as deviations from admission-day baseline, showed a significant independent association with mortality from day 3 to day 14 (OR range: 0.027 to 0.107, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Time-varying Cox proportional hazards models and logistic regression analyses confirmed that both sustained HRR elevation and reduced acute fluctuations were independently linked to the mortality in hospitalized patients.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo evaluate the temporal predictive utility of HRR for mortality, we developed logistic regression models incorporating repeated HRR measurements. As demonstrated in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e, predictive performance improved progressively as measurements were taken closer to discharge, with AUCs increasing from 0.631 (Day 1) to 0.731 (Day 7) and 0.763 (the last day). HRR-based models consistently outperformed those using Hb or RDW alone at all time points, achieving the highest discriminative ability (AUC 0.763 vs. 0.736 for Hb and 0.705 for RDW).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAssociation between HRR cut-off and mortality\\u003c/h2\\u003e \\u003cp\\u003eThe optimal baseline HRR cutoff for mortality risk stratification was identified as 0.74 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003ea). Patients with baseline HRR\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.74 maintained higher HRR levels throughout hospitalization compared to those with baseline HRR\\u0026thinsp;\\u0026le;\\u0026thinsp;0.74 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003eb). Median survival time was significantly longer in the HRR\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.74 group (79 days) than in the HRR\\u0026thinsp;\\u0026le;\\u0026thinsp;0.74 group (41 days, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u0026zwnj; Patients with baseline HRR\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.74 exhibited greater declines in serial HRR measurements over 25 days post-admission (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003eb).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAs shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, patients with HRR\\u0026thinsp;\\u0026le;\\u0026thinsp;0.74 were older, had longer hospital stays, and exhibited significantly higher comorbidity burdens. Compared to the HRR\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.74 group, this cohort showed higher prevalence of hypertension, diabetes, cerebral infarction, cerebral cysts, dementia, paralysis, heart failure, respiratory failure, kidney failure, metabolic disorders, cancer, and COPD. Notably, in-hospital mortality was substantially higher in the HRR\\u0026thinsp;\\u0026le;\\u0026thinsp;0.74 group (25.14% vs. 12.93%, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eComparison of baseline characteristics of patients stratified by baseline HRR cut-off.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHRR\\u0026thinsp;\\u0026le;\\u0026thinsp;0.74 (n\\u0026thinsp;=\\u0026thinsp;529)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHRR\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.74 (n\\u0026thinsp;=\\u0026thinsp;1918)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDemographic characters\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge, years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e69.00(60.00\\u0026ndash;80.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e67.00(55.00\\u0026ndash;78.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex (males)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e291(55.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1191(62.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMarital status (Married)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e323(61.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1323(68.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRace (white)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e196(37.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e635(33.11)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHospitalization information\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAdmission type (emergency)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e107(20.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e358(18.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.418\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLength of stay, days\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8.00(5.00\\u0026ndash;17.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.00(4.00\\u0026ndash;14.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eComorbidities\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHypertension\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e382(72.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1143(59.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e258(48.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e638(33.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCerebral infarction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e135(25.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e324(16.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCerebral cysts\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e149(28.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e396(20.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDementia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e143(27.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e164(8.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eParalysis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e149(28.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e320(16.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePeripheral vascular disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102(19.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e429(22.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.127\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMyocardial infarction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e144(27.22)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e580(30.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.178\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHeart failure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e150(28.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e215(11.21)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther heart disease\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e174(32.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e423(22.05)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRespiratory failure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e158(29.87)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e412(21.48)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eKidney failure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e184(34.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e256(13.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMetabolic disorders\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e266(50.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e804(41.92)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnemia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e174(32.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e711(37.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.077\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCancer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e255(48.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e256(13.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCOPD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e402(75.99)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1344(70.07)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLaboratory tests\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHRR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.62(0.51\\u0026ndash;0.69)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.98(0.89\\u0026ndash;1.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRDW, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16.30(15.00\\u0026ndash;18.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.40(12.90\\u0026ndash;14.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHemoglobin, g/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.90(8.80\\u0026ndash;10.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.20(12.30-14.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRBC, m/uL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.35(2.92\\u0026ndash;3.82)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.36(4.00-4.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWBC, K/uL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8.40(5.90\\u0026ndash;11.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.60(7.40\\u0026ndash;12.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHematocrit, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30.60(27.30\\u0026ndash;33.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e39.40(36.70\\u0026ndash;42.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlatelet, K/uL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e191.00(121.00-277.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e222.00(177.00-273.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeu, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e75.40(64.60\\u0026ndash;84.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e78.70(68.00\\u0026ndash;86.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMono, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.90(3.80\\u0026ndash;8.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.20(3.60\\u0026ndash;7.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBaso, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.30(0.10\\u0026ndash;0.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.40(0.20\\u0026ndash;0.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEos, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.90(0.10\\u0026ndash;2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.60(0.20\\u0026ndash;1.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.085\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLym, %\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13.90(8.00-21.30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.80(7.90\\u0026ndash;22.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.434\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGlucose, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e119.00(99.00-155.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e125.00(105.00-158.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCreatinine, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.10(0.80\\u0026ndash;1.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.90(0.70\\u0026ndash;1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBUN, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e21.50(15.00\\u0026ndash;34.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16.00(13.00\\u0026ndash;21.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBicarbonate, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24.00(21.00\\u0026ndash;26.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.00(22.00\\u0026ndash;26.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.035\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePhosphate, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.60(3.00-4.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.20(2.80\\u0026ndash;3.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal calcium, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8.80(8.38\\u0026ndash;9.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.00(8.50\\u0026ndash;9.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChloride, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102.00(98.00-105.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e103.00(100.00-105.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSerum sodium, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e138.00(135.25\\u0026ndash;141.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e139.00(137.00-141.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSerum potassium, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.10(3.80\\u0026ndash;4.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.00(3.70\\u0026ndash;4.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSerum magnesium, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.00(1.70\\u0026ndash;2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.90(1.80\\u0026ndash;2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.555\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnion Gap, mEq/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15.00(13.00\\u0026ndash;18.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.00(13.00\\u0026ndash;17.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.967\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePT, sec\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13.60(12.00-16.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.20(11.30\\u0026ndash;13.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAPTT, sec\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e29.70(26.10\\u0026ndash;35.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.90(25.20-30.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eINR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.20(1.10\\u0026ndash;1.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.10(1.00-1.20)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eOutcome\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHospital mortality\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e133(25.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e248(12.93)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussions\",\"content\":\"\\u003cp\\u003eThis study assessed the association between the dynamic HRR during hospitalization and all-cause mortality in ICH patients, revealing that lower HRR was associated with higher mortality risk. Throughout hospitalization, HRR progressively declined, with non-survivors exhibiting persistently lower levels than survivors. While all subgroups demonstrated significant temporal declines, males and patients with comorbidities maintained consistently lower HRR values across all time points. The inverse relationship between HRR and mortality strengthened progressively over time, peaking near discharge, where final-day HRR alone achieved a peak predictive accuracy (AUC = 0.763) in temporal models. A baseline HRR threshold of 0.74 effectively stratified mortality risk, with HRR ≤ 0.74 patients showing significantly shorter survival (25.14% vs. 12.93% mortality, P \\u0026lt; 0.001) compared to those with HRR \\u0026gt; 0.74. These findings underscore HRR as both a time-sensitive prognostic biomarker and a potential target for early interventions to improve outcomes in high-risk ICH populations.\\u003c/p\\u003e \\u003cp\\u003eHRR has recently emerged as a crucial prognostic marker for severe conditions such as coronary heart disease\\u003csup\\u003e\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e, cancer\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e, sepsis\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e, and ischemic stroke\\u003csup\\u003e\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e. Evidence highlighted its inverse relationship with mortality, with higher HRR levels correspond to 53% lower all-cause mortality, 49% lower cancer-specific mortality, and 57% reduced cardiovascular mortality\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e. Notably, acute ischemic stroke patients with suboptimal post-thrombectomy outcomes exhibited low HRR levels\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e, and those with HRR ≤ 0.76 faced nearly tripled mortality risks alongside heightened susceptibility to pneumonia and septicemia\\u003csup\\u003e\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u003c/sup\\u003e. This study confirms this inverse HRR-mortality link in ICH patients, demonstrating its persistence throughout hospitalization even after adjusting for age, comorbidities, and key laboratory confounders.\\u003c/p\\u003e \\u003cp\\u003eThe prognostic value of HRR likely stems from its dual-pathway integration of hemoglobin (Hb) and red cell distribution width (RDW) - two hematologic parameters reflecting oxygen transport capacity and inflammatory status. As a composite biomarker, HRR bridges erythrocyte heterogeneity (RDW) and oxygen-carrying function (Hb), encapsulating the interplay between hematologic stress and tissue vulnerability. Mechanistically, elevated HRR may mitigate chronic inflammation through synergistic effects: enhancing Hb's capacity to capture inflammatory molecules\\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u003c/sup\\u003e while reducing RDW-associated inflammatory responses\\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e. This dual regulation improves oxygen delivery to hypoxia-sensitive neural tissues and reduces systemic inflammation, ultimately decreasing cerebral hemorrhage mortality risk. From a clinical perspective, higher HRR levels indicate optimized erythrocyte functionality - better oxygen transport efficiency coupled with reduced inflammatory burden, collectively contributing to improved outcomes in hemorrhagic stroke and other critical illnesses. These pathophysiological insights position HRR as an applicable biomarker reflecting both inflammatory-hematologic crosstalk and tissue oxygen homeostasis.\\u003c/p\\u003e \\u003cp\\u003eOur study enhances understanding of HRR as a dynamic prognostic biomarker in ICH by analyzing longitudinal in-hospital trajectories, contrasting with prior static baseline assessments. While previous investigations, including studies by Lin et al. \\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e, Xiong et al.\\u003csup\\u003e\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u003c/sup\\u003e, and Feng et al.\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e, primarily examined single-time-point HRR measurements at admission across diverse stroke populations, our analysis focuses on short-term HRR evolution during hospitalization and its direct association with ICH-specific mortality. We demonstrate that the inverse HRR-mortality relationship not only persists but strengthens over time, with HRR levels progressively declining post-admission and the Pearson correlation coefficient between HRR and mortality risk escalating from 0.140 (Day 1) to 0.238 (Day 14). Survivors consistently maintained higher HRR trajectories compared to non-survivors, whose HRR declined more rapidly. Logistic regression confirmed temporal HRR changes as independent mortality predictors, emphasizing HRR’s utility as a time-sensitive prognostic indicator. Unlike a previous population-based longitudinal study documenting gradual HRR attenuation over decades\\u003csup\\u003e\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u003c/sup\\u003e, this study highlighted the clinical relevance of serial in-hospital HRR monitoring for identifying deteriorating patients in real time, with a critical advantage over static baseline assessments. These temporal findings provide actionable insights into evolving risk profiles, enabling timely intervention strategies to mitigate mortality in high-risk ICH cohorts.\\u003c/p\\u003e \\u003cp\\u003eMoreover, subgroup analyses revealed sustained HRR declines across demographic and comorbid populations, with notable variations in trajectory and magnitude. Throughout hospitalization, females demonstrated consistently higher HRR levels compared to males, while patients with severe comorbidities such as cancer, renal failure, heart failure, and cerebral cysts exhibited notably lower HRR than those without. These findings imply that HRR may be as a sensitive, stratification-ready marker, with comorbidity-driven divergence in trajectories reflecting differential pathophysiological burdens.\\u003c/p\\u003e \\u003cp\\u003eIn addition, our findings confirm HRR's independent prognostic value for ICH mortality, with predictive performance progressively improving from admission (AUC 0.631) to discharge (AUC 0.763). This aligns with prior evidence establishing HRR as a robust prognostic marker across diverse clinical populations, including heart failure\\u003csup\\u003e\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e\\u003c/sup\\u003e, cancer\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e, and stroke patients\\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e, with reported AUC ranges spanning 0.548 to 0.790. Particularly in ischemic stroke, HRR achieved clinically significant discrimination for functional outcomes (AUC 0.790) and mortality (AUC 0.771)\\u003csup\\u003e\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e. Critically, HRR demonstrated superior predictive capacity compared to Hb and RDW at all time points in our cohort, reaching optimal discrimination (AUC 0.763) versus Hb (0.736) and RDW (0.705). Consistent with Qu et al.'s findings in patients with coronary heart disease (HRR AUC 0.652 vs. Hb 0.618/RDW 0.650) \\u003csup\\u003e\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u003c/sup\\u003e and Yang et al.'s traumatic brain injury cohort (HRR AUC 0.713 vs. Hb 0.688/RDW 0.680 for 120-day mortality) \\u003csup\\u003e\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e, our results reinforce HRR's prognostic superiority over conventional hematologic markers.\\u003c/p\\u003e \\u003cp\\u003eOur threshold analysis identified 0.74 as a critical baseline HRR cutoff for mortality risk stratification in ICH patients, with those exhibiting HRR ≤ 0.74 demonstrating a 2.97-fold increased mortality risk, shorter survival duration, and higher comorbidity burdens compared to their HRR \\u0026gt; 0.74 counterparts. While existing literature reported variable HRR thresholds across clinical contexts, including HRR \\u0026lt; 9.50 predicting in-hospital mortality in non-traumatic subarachnoid hemorrhage\\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u003c/sup\\u003eand HRR \\u0026lt; 5.877 associated with septic atrial fibrillation mortality\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u003c/sup\\u003e, these studies collectively affirm HRR's prognostic utility. Notably, prior ICH-specific research revealed protective effects of elevated HRR against 28-day (HRR \\u0026gt; 0.92) and 90-day (HRR \\u0026gt; 0.93) mortality in older adults, underscoring its subtype-specific relevance\\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u003c/sup\\u003e. Although optimal thresholds differ across populations, these findings consistently validate HRR's capacity to stratify mortality risk. Future studies could explore dynamic threshold adjustment during hospitalization to enhance predictive accuracy, particularly for patients with fluctuating HRR trajectories.\\u003c/p\\u003e \"},{\"header\":\"Limitations\",\"content\":\"\\u003cp\\u003eThis study has several limitations. First, the retrospective single-center design using publicly available data restricted analyses to routinely collected clinical information, resulting in incomplete laboratory parameters for some patients (e.g., due to early discharge/mortality), which may introduce selection bias. Second, the modest sample size limited statistical power for detecting subtle HRR interactions. Third, the absence of external validation restricts generalizability. Fourth, while adjusting for multiple confounders, residual bias persists from unmeasured variables, including genetic predispositions, socioeconomic determinants, and neuroimaging-based parameters (e.g., ICH subtype, hematoma location/volume). These factors may influence ICH outcomes and independently modulate physiological reserve/recovery trajectories, potentially confounding the HRR-mortality relationship. Future multi-center prospective studies with larger cohorts should incorporate standardized neuroimaging protocols and external validation to confirm HRR’s utility across ICH phenotypes, while exploring subgroup analyses to assess robustness.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis study established HRR as a dynamic, time-dependent prognostic biomarker for mortality risk stratification in ICH, outperforming Hb and RDW individually. Lower baseline HRR values independently predicted higher all-cause mortality, while serial measurements demonstrated a time-intensifying association with survival risk\\u0026mdash;peaking near discharge. Notably, a baseline HRR cutoff of 0.74 effectively identified high-risk patients. This routine, cost-effective metric holds significant potential for clinical risk assessment, particularly during early hospitalization when intervention windows are critical. Future research could validate HRR-guided therapeutic strategies and elucidate the pathophysiological pathways linking HRR dynamics to adverse outcomes, thereby strengthening its translational applicability in acute neurocritical care.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eICH\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eIntracerebral hemorrhage\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eHRR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eHemoglobin-to-red blood cell distribution width ratio\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eRDW\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eRed blood cell distribution width\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eHb\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eHemoglobin\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePVD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ePeripheral vascular disease\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eRCS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eRestricted cubic spline\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCOPD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eChronic obstructive pulmonary disease\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eRBC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eRed blood cells\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eWBC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eWhite blood cells\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePLT\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ePlatelet count\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eBUN\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eBlood urea nitrogen\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003ePT\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eProthrombin time\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eAPTT\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eActivated partial thromboplastin time\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eINR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eInternational normalized ratio\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eCompeting interests\\u003c/h2\\u003e \\u003cp\\u003eAll authors declare no competing interests.\\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis study was supported by the Outstanding Young Doctoral Program Research Initiation Fund of the First Affiliated Hospital of Guangxi Medical University (grant 202302).\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eY.H. was responsible for conceptualization, data curation, methodology, and writing the original draft. H.L. ensured data integrity and analysis accuracy. S.T. conducted a comprehensive review and validation. All authors actively participated and approved the final manuscript.\\u003c/p\\u003e\\u003ch2\\u003eData availability\\u003c/h2\\u003e \\u003cp\\u003eAll survey data utilized in this research are available in the Medical Information Mart for Intensive Care-IV (MIMIC-IV, version 3.1) database, accessible at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://physionet.org/content/mimiciv/3.1/\\u003c/span\\u003e\\u003cspan address=\\\"https://physionet.org/content/mimiciv/3.1/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003ePuy, L. et al. 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The relationship between hemoglobin/red blood cell distribution width ratio and mortality in patients with intracranial hemorrhage: a possible protective effect for the elderly? \\u003cem\\u003eIntern. Emerg. Med.\\u003c/em\\u003e \\u003cb\\u003e18\\u003c/b\\u003e, 2301\\u0026ndash;2310. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s11739-023-03431-4\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s11739-023-03431-4\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2023).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang, D. et al. Hemoglobin-to-Red Cell Distribution Width Ratio is Associated with All-Cause Mortality in Critically Ill Patients with Traumatic Brain Injury. \\u003cem\\u003eNeuroendocrinol. 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Neurol.\\u003c/em\\u003e \\u003cb\\u003e14\\u003c/b\\u003e, 1180912. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3389/fneur.2023.1180912\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fneur.2023.1180912\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2023).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Hemoglobin-to-red blood cell distribution width ratio (HRR), Dynamic changes, Intracerebral hemorrhage, Hospital mortality\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6634211/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6634211/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study aimed to evaluate the time-dependent prognostic value of HRR for all-cause mortality in ICH patients. We included 2,447 ICH patients from the MIMIC-IV 3.1 database. Cox regression assessed HRR-mortality associations, while restricted cubic spline model evaluated non-linear relationships. Serial HRR trends were analyzed using temporal Pearson correlation analyses and ROC curves, with the optimal cutoff identified via surv_cutpoint. Results demonstrated a dynamic inverse association with all-cause mortality in ICH patients, with higher baseline HRR independently linked to an 88.5% reduced mortality risk. Both survivors and non-survivors exhibited progressive HRR declines during hospitalization, though non-survivors showed a steeper 14-day trajectory (0.835 to 0.553 vs. 0.919 to 0.710 in survivors, P \\u0026lt; 0.001 for trend) and a daily decrease rate of -0.014 (r = -0.971). Consistent HRR declines across all subgroups. Daily HRR levels inversely correlated with mortality risk throughout hospitalization (adjusted HRs \\u0026lt;1.0 at all time points, P \\u0026lt; 0.05), with discharge HRR achieving peak discriminative accuracy (AUC = 0.763). A baseline HRR cutoff ≤0.74 identified high-risk patients with 25.14% mortality. HRR may serve as a dynamic prognostic indicator for ICH mortality risk stratification.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Association of dynamic changes of hemoglobin-to-red blood cell distribution width ratio with all-cause mortality in patients with intracerebral hemorrhage\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-19 11:53:53\",\"doi\":\"10.21203/rs.3.rs-6634211/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"c24ec13f-9c95-4622-aa33-53c370a090c4\",\"owner\":[],\"postedDate\":\"May 19th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":48342762,\"name\":\"Health sciences/Biomarkers\"},{\"id\":48342763,\"name\":\"Health sciences/Diseases\"},{\"id\":48342764,\"name\":\"Health sciences/Neurology/Neurological disorders/Cerebrovascular disorders\"}],\"tags\":[],\"updatedAt\":\"2025-09-05T20:53:25+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-05-19 11:53:53\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6634211\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6634211\",\"identity\":\"rs-6634211\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}