Dynamic RDW Trajectories Predict Mortality in Sepsis-Associated Delirium: A Group-Based Trajectory Modeling Study

Dynamic RDW Trajectories Predict Mortality in Sepsis-Associated Delirium: A Group-Based Trajectory Modeling Study | 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 Research Article Dynamic RDW Trajectories Predict Mortality in Sepsis-Associated Delirium: A Group-Based Trajectory Modeling Study Shuyang Dai, Bingjie Li, Zongshan Zhang, Gaoli Zhang, Poshi Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9080605/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Sepsis-associated delirium (SAD) is a frequent complication in the intensive care unit (ICU) associated with poor prognosis. While red blood cell distribution width (RDW) is a potential biomarker reflecting inflammation and oxidative stress, its longitudinal dynamic patterns and prognostic value specifically within the SAD population remain elusive. Methods This retrospective cohort study included adult patients diagnosed with SAD from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (2008–2022). Group-based trajectory modeling (GBTM) was employed to identify distinct longitudinal patterns of RDW during the first 5 days of ICU admission. The primary outcome was 30-day all-cause mortality, and the secondary outcome was 365-day all-cause mortality. Multivariable Cox proportional hazards models were used to evaluate the association between RDW trajectories and mortality risk, adjusting for demographics, disease severity, comorbidities, and therapeutic interventions. Results A total of 1,924 SAD patients (median age: 66.0 years; 59.25% male) were included, with a 30-day mortality rate of 19.59%. GBTM identified three distinct RDW trajectories: "Stable-Low" (Class 1, 61.4%), "Slowly-Rising" (Class 2, 31.9%), and "Persistently-Rising" (Class 3, 6.7%). Compared to the Stable-Low group, patients in the Persistently-Rising group exhibited the highest baseline organ failure scores (median SOFA: 10 vs. 6) and most severe anemia (median hemoglobin: 8.5 g/dL vs. 11.4 g/dL). After full adjustment (Model 3), the Persistently-Rising trajectory was independently associated with a significantly increased risk of 30-day mortality (HR = 3.765, 95% CI: 2.727–5.199, P < 0.001) and 365-day mortality (HR = 3.635, 95% CI: 2.664–4.960, P < 0.001). Restricted cubic spline analysis revealed a linear positive correlation between baseline RDW and mortality risk. Subgroup analyses and sensitivity analyses excluding transfused patients confirmed the robustness of these associations. Conclusion Longitudinal RDW trajectories are independent predictors of short- and long-term mortality in patients with SAD. Specifically, a persistently rising RDW pattern indicates an extremely high risk of death and may serve as a valuable metric for early risk stratification and personalized intervention. Sepsis-associated delirium Red blood cell distribution width Longitudinal trajectory Mortality MIMIC-IV Critical care Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, remains a leading cause of global morbidity and mortality[ 1 , 2 ]. Delirium, a common neurological complication of sepsis, affects 50% to 80% of critically ill patients. It is associated with prolonged mechanical ventilation, extended ICU stays, long-term cognitive impairment, and increased mortality[ 3 – 5 ]. Although recent advances have elucidated the pathophysiological mechanisms of sepsis-associated delirium (SAD)-including neuroinflammation, blood-brain barrier disruption, and microcirculatory dysfunction[ 6 , 7 ]-there remains a critical need for simple, dynamic, and effective biomarkers to identify high-risk patients early and guide prognostic assessment. Traditionally used for the differential diagnosis of anemia, red blood cell distribution width (RDW) is increasingly recognized as a marker reflecting ineffective erythropoiesis, chronic inflammation, oxidative stress, and nutritional deficiencies[ 8 , 9 ]. Previous cross-sectional studies have established that elevated baseline RDW is independently associated with disease severity and mortality in sepsis[ 10 , 11 ]. However, single-timepoint measurements may fail to capture the dynamic evolution of the disease process. In critical illness, the longitudinal trajectory of RDW may provide a more comprehensive reflection of the evolving inflammatory burden and the bone marrow's compensatory capacity than baseline values alone[ 10 , 12 ]. Currently, research on the dynamic trajectories of RDW specifically within the SAD population is scant. Most existing studies focus solely on admission levels, overlooking the prognostic implications of longitudinal fluctuations during ICU treatment[ 13 , 14 ]. Furthermore, common ICU interventions such as blood transfusion, acute kidney injury (AKI), and vasopressor use can influence RDW levels, yet prior studies have often inadequately adjusted for these time-varying confounders[ 15 , 16 ]. Group-based trajectory modeling (GBTM), an advanced person-centered statistical approach, identifies homogeneous subgroups within heterogeneous populations based on similar developmental patterns. While GBTM has demonstrated utility in predicting outcomes in cardiovascular disease and acute kidney disease[ 17 , 18 ], its application to RDW dynamics in SAD remains unexplored. Therefore, leveraging the large-scale public MIMIC-IV database, this study aims to: (1) characterize the longitudinal trajectories of RDW during the early ICU stay in patients with SAD; (2) investigate the association between distinct RDW trajectory patterns and 30-day and 365-day all-cause mortality; and (3) validate the robustness of these findings through rigorous sensitivity analyses, including the exclusion of transfused patients. We hypothesized that a persistently rising RDW trajectory would independently predict adverse outcomes in SAD patients, demonstrating superior prognostic discrimination compared to single baseline measurements. Our findings aim to provide new evidence-based insights for dynamic risk stratification in this high-risk population. Methods Data Source This retrospective cohort study utilized data from version 3.0 of the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, which contains comprehensive electronic health records of ICU patients at Beth Israel Deaconess Medical Center in Boston, Massachusetts, USA, from 2008 to 2022[ 19 ]. All patient data in the database have been de-identified in accordance with the privacy provisions of the Health Insurance Portability and Accountability Act (HIPAA), and no individual informed consent was required[ 20 ]. Researchers obtained access to the database after completing the Collaborative Institutional Training Initiative (CITI) program conducted by the National Institutes of Health (record number: 14012091). All datasets were de-identified before release, and no direct or indirect patient identification information was accessed at any stage of the study analysis. The MIMIC-IV project has been approved by the Institutional Review Boards (IRBs) of Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology, with a waiver of informed consent. This study exclusively used publicly available de-identified data and required no additional IRB review. In accordance with journal policies, all ethical and data source information is stated in this section. Study Population The study cohort included adult patients (≥ 18 years old) admitted to the ICU with a concurrent diagnosis of sepsis and delirium. The diagnosis of delirium was required to be established within 72 hours of ICU admission to mitigate the confounding effect of late-onset delirium on RDW trajectory evolution. Sepsis was identified using International Classification of Diseases (ICD)-9 and ICD-10 codes (ICD-9: 995.91, 995.92; ICD-10: A40.x, A41.x), supplemented by a Sequential Organ Failure Assessment (SOFA) score ≥ 2 points, in line with the Sepsis-3 criteria[ 21 ]. Delirium was diagnosed using the Confusion Assessment Method for the ICU (CAM-ICU), and its diagnostic criteria were extracted from the delirium assessment module of the MIMIC-IV database, a method validated in previous studies[ 1 , 22 ]. The following exclusion criteria were applied: (1) ICU length of stay (LOS) < 72 hours; (2) missing RDW measurement data for any 24-hour period within the first 5 days of ICU admission; (3) presence of malignant tumors; and (4) multiple ICU admissions due to sepsis (only the first admission was included). Data Collection Data extraction was performed using Structured Query Language (SQL) with PostgreSQL (Version 15) and Navicat Premium (Version 17). The extracted variables included: (1) Demographic characteristics: age, gender, race, body mass index (BMI); (2) Comorbidities: hypertension, type 2 diabetes mellitus, heart failure, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), pneumonia, malignant tumor, acute myocardial infarction, ischemic cardiomyopathy, ischemic stroke; (3) Laboratory parameters: white blood cell count (WBC), hemoglobin, platelet count, serum chloride, potassium, lactate, glucose, anion gap, serum sodium, and RDW (measured at least every 24 hours within the first 120 hours); (4) Vital signs: systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate, respiratory rate, body temperature, and oxygen saturation; (5) Disease severity scores: SOFA score[ 23 ], Simplified Acute Physiology Score II (SAPS II)[ 24 ], Acute Physiology Score III (APS III)[ 25 ], Oxford Acute Severity of Illness Score (OASIS)[ 26 ], and Charlson Comorbidity Index (CCI)[ 27 ]; (6) Therapeutic interventions: mechanical ventilation, vasoactive drugs; (7) Clinical outcomes: ICU mortality, in-hospital mortality, and 30-day survival status. For patients with multiple RDW measurements within 24 hours, the first recorded value was used. Variables with missing data exceeding 20% were excluded from the analysis, whereas those with ≤ 20% missingness were handled via multiple imputation[ 28 ]. The variance inflation factor (VIF) was used to assess multicollinearity of covariates in the Cox proportional hazards model, with a VIF value < 5 indicating no significant multicollinearity. The blood transfusion variable was determined using specific codes and item labels in the MIMIC-IV database: a binary variable for blood transfusion (filtered red blood cells, suspended red blood cells, and packed red blood cells) during ICU stay was created. This variable was included in the adjusted model and used to define the exclusion subgroup (all transfused patients excluded) in the sensitivity analysis. Outcome The primary outcome was 30-day all-cause mortality after ICU admission; the secondary outcome was 365-day all-cause mortality. Statistical Analysis Group-based trajectory modeling (GBTM), a person-centered analytical approach, was used to identify distinct longitudinal patterns of RDW changes in patients during ICU stay[ 29 ]. This semi-parametric mixed modeling technique identifies homogeneous subgroups in heterogeneous populations based on similar temporal developmental trajectories. The optimal number of trajectory subgroups was determined by systematically evaluating 2 to 5 latent class models. Model selection was based on multiple statistical criteria, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Sample-size Adjusted Bayesian Information Criterion (SABIC), entropy, log-likelihood ratio, and clinical interpretability. The model fit was evaluated for potential trajectory shapes (linear, quadratic, cubic) to select the optimal configuration. The final model was required to meet the following conditions: the proportion of each trajectory subgroup in the study population > 5%; the average posterior probability of each trajectory subgroup > 0.7; and good model convergence with clinical significance[ 30 ]. This study strictly followed the Guidelines for Reporting on Latent Trajectory Studies (GRoLTS) checklist to ensure methodological rigor and transparency[ 31 ]. A three-class trajectory model was ultimately identified as the best-fitting model (see Supplementary Tables S2 and S3 for details). Continuous variables were presented as mean ± standard deviation (SD) or median (interquartile range, IQR) based on normality test results (Kolmogorov-Smirnov test). Categorical variables were reported as frequencies and percentages. Intergroup comparisons of continuous variables were performed using analysis of variance (ANOVA) or the Kruskal-Wallis test, and those of categorical variables using the chi-square test or Fisher’s exact test. Kaplan-Meier survival analysis with the log-rank test was used to compare mortality risks across different RDW trajectories. Univariate Cox regression (Model 1) was used to screen covariates associated with 30-day mortality (Supplementary Table 5), and variables with P < 0.05 were included in the multivariate model. Model 2 was adjusted for gender, age, race, BMI, and respiratory rate. Model 3 was further adjusted for disease severity scores (SOFA, APS III, SAPS II, OASIS, CCI), comorbidities (hypertension, acute kidney injury, pneumonia, chronic kidney disease, heart failure), therapeutic indicators (vasoactive drugs, continuous renal replacement therapy [CRRT], blood transfusion), and laboratory parameters (hemoglobin, platelets, anion gap, chloride, potassium). Subgroup analyses were conducted for age, gender, BMI, hypertension, AKI, heart failure, and vasoactive drug use. To verify the robustness of the study results, two sensitivity analyses were performed. Given that the proportion of missing data for all covariates was less than 5% (Supplementary Table 4), we first imputed missing values using the mean imputation method and repeated the main analysis. Second, for the analysis of 30-day and 365-day mortality, we re-analyzed the data after excluding all patients who received blood transfusion during their ICU stay. All statistical analyses were performed using R statistical software (Version 4.2.2) and DecisionLine 1.0 software. A two-sided P < 0.05 was considered statistically significant, and hazard ratios and regression coefficients were reported with 95% confidence intervals (95% CI). This study was conducted in accordance with the Declaration of Helsinki. Since the MIMIC-IV database used is public and de-identified, this retrospective analysis was exempt from additional ethical review, and no informed consent from patients was required. Results Identification of RDW Trajectory Subgroups A total of 1,924 SAD patients met the inclusion criteria (Fig. 1 ). Three distinct RDW trajectory patterns were identified in ICU patients using group-based trajectory modeling (Fig. 2 ), with details of model fitting shown in Supplementary Tables S2 and S3. The trajectory patterns were named based on initial levels and change trends: Class 1 (Stable-Low): n = 1,180 (61.4%). RDW remained stable at a low level of approximately 14%, exhibiting minimal fluctuation. Class 2 (Slowly-Rising): n = 651 (31.9%). RDW rose slowly from approximately 16.7% to 17.2%. Class 3 (Persistently-Rising): n = 128 (6.7%). RDW started at a high level of 22% and continued to rise to nearly 23%. The posterior probabilities of each subgroup were 97.8%, 95.5%, and 98.5%, respectively (Supplementary Table S3), indicating excellent model classification accuracy. Baseline Characteristics of Different RDW Trajectory Patterns As shown in Table 1 , among the 1,924 SAD patients, the mean age was 66.0 years, and 59.25% were male. Significant differences were observed in baseline characteristics, disease severity, and clinical outcomes across the different RDW trajectory groups (P < 0.05). Compared with the Stable-Low group (Class 1), patients in the Slowly-Rising (Class 2) and Persistently-Rising (Class 3) groups had higher organ failure scores (SOFA, APS III, etc.), more severe anemia (median hemoglobin: 11.4, 9.2, and 8.5 g/dL, respectively), and a higher incidence of acute kidney injury (AKI). The demand for critical care interventions (e.g., CRRT and blood transfusion) increased significantly with the elevation of RDW trajectory grade. Notably, mortality increased in a stepwise manner: the 30-day and 365-day mortality rates in Class 3 were as high as 57.03% and 60.16%, respectively, which were significantly higher than those in Class 1 (12.96% and 14.48%, P < 0.001). These findings suggest that the drivers of the rising RDW trajectory are closely linked to severe underlying pathophysiology, including organ dysfunction and inflammatory burden. Table 1 Clinical characteristics and outcomes by RDW trajectory in ARF Patients Variables Total(n = 1,924) Class 1 (n = 1,181) Class 2 (n = 615) Class 3 (n = 128) P Age (years) 66 (55–77) 65 (54–77) 67 (57–78) 64 (54-77.25) 0.011 Gender, n(%) Female 784 (40.75) 446 (37.76) 272 (44.23) 66 (51.56) 0.001 Male 1140 (59.25) 735 (62.24) 343 (55.77) 62 (48.44) Race (%) Black 165 (8.58) 92 (7.79) 60 (9.76) 13 (10.16) 0.269 White 1500 (77.96) 940 (79.59) 462 (75.12) 98 (76.56) Others 259 (13.46) 149 (12.62) 93 (15.12) 17 (13.28) BMI 28.191 (24.215–33.298) 28.056 (24.408–32.809) 28.406 (23.692–33.955) 27.984 (24.411–33.385) 0.215 SOFA 7 (4–10) 6 (4–9) 8 (5–11) 10 (6–13) < 0.001 APSIII 49 (37–67) 46 (35–61) 55 (41–72) 65 (50.75–84.25) < 0.001 SAPS II 40 (32–51) 38 (31–48) 43 (34–54) 47.5 (37.5–57) < 0.001 OASIS 36 (31–42) 35 (30–41) 37 (32–43) 37 (31–43) < 0.001 CCI 5 (3–7) 4 (2–6) 6 (4–8) 6 (4–8) < 0.001 Hypertension (%) No 1177 (61.17) 670 (56.73) 414 (67.32) 93 (72.66) < 0.001 Yes 747 (38.83) 511 (43.27) 201 (32.68) 35 (27.34) AKI(%) No 904 (46.99) 638 (54.02) 229 (37.24) 37 (28.91) < 0.001 Yes 1020 (53.01) 543 (45.98) 386 (62.76) 91 (71.09) Pneumonia (%) No 1075 (55.87) 675 (57.15) 331 (53.82) 69 (53.91) 0.361 Yes 849 (44.13) 506 (42.85) 284 (46.18) 59 (46.09) Stroke (%) No 1760 (91.48) 1077 (91.19) 568 (92.36) 115 (89.84) 0.557 Yes 164 (8.52) 104 (8.81) 47 (7.64) 13 (10.16) CKD(%) No 1532 (79.63) 986 (83.49) 448 (72.85) 98 (76.56) < 0.001 Yes 392 (20.37) 195 (16.51) 167 (27.15) 30 (23.44) T2DM (%) No 1330 (69.13) 842 (71.30) 400 (65.04) 88 (68.75) 0.024 Yes 594 (30.87) 339 (28.70) 215 (34.96) 40 (31.25) Heart failure (%) No 1289 (67.00) 835 (70.70) 375 (60.98) 79 (61.72) < 0.001 Yes 635 (33.00) 346 (29.30) 240 (39.02) 49 (38.28) Ischemic cardiomyopathy (%) No 1228 (63.83) 751 (63.59) 386 (62.76) 91 (71.09) 0.196 Yes 696 (36.17) 430 (36.41) 229 (37.24) 37 (28.91) COPD (%) No 1596 (82.95) 1021 (86.45) 467 (75.93) 108 (84.38) < 0.001 Yes 328 (17.05) 160 (13.55) 148 (24.07) 20 (15.62) MI (%) No 1677 (87.16) 1035 (87.64) 527 (85.69) 115 (89.84) 0.325 Yes 247 (12.84) 146 (12.36) 88 (14.31) 13 (10.16) Hemoglobin (g/dL) 10.5 (8.8–12.3) 11.4 (9.8–12.9) 9.2 (7.85–10.9) 8.5 (7.6–10) < 0.001 Platelets (×103/µL) 178 (121–243) 188 (139–243) 163 (101.5–247) 131.5 (67.75-214.25) 0.012 WBC (×103/µL) 12.7 (8.9–17.3) 12.8 (9.3–17.2) 12.3 (8.25–17.35) 12.4 (8.25-19.025) 0.516 Anion gap 15 (12–18) 14 (12–17) 15 (12–18) 17 (13–20) < 0.001 Chloride(mEq/L) 104 (100–108) 104 (101–108) 103 (98–108) 102 (96–107) < 0.001 FBG (mg/dL) 137 (110–180) 138 (112–181) 136 (109-180.5) 127.5 (97.5–171) 0.046 Potassium (mEq/L) 4.2 (3.7–4.7) 4.1 (3.7–4.6) 4.2 (3.7–4.8) 4.1 (3.6-4.725) 0.107 Sodium (mEq/L) 139 (135–142) 139 (136–142) 138 (135–142) 138 (133–141) 0.037 SBP(mmHg) 118 (102–138) 120 (103–139) 116 (101-135.5) 114.5 (101-131.5) 0.032 Heart rate(beats/min) 89 (77–104) 88 (76–103) 91 (77–107) 91.5 (80-105.25) 0.006 DBP(mmHg) 68 (57–80) 68 (58–81) 67 (56–78) 66 (55–80) 0.713 Respiration rate(breaths/min) 19 (16–24) 19 (16–23) 20 (16–24) 19 (16–24) 0.036 Temperature(f) 98.4 (97.8–99) 98.5 (97.9–99.1) 98.3 (97.7–98.9) 98.1 (97.675–98.6) 0.015 SpO2(%) 99 (96–100) 99 (96–100) 99 (96–100) 98 (95–100) 0.670 Invasive ventilation (%) No 61 (3.17) 36 (3.05) 20 (3.25) 5 (3.91) 0.862 Yes 1863 (96.83) 1145 (96.95) 595 (96.75) 123 (96.09) Vasoactive drugs(%) No 410 (21.31) 285 (24.13) 107 (17.40) 18 (14.06) < 0.001 Yes 1514 (78.69) 896 (75.87) 508 (82.60) 110 (85.94) CRRT (%) No 1712 (88.98) 1101 (93.23) 515 (83.74) 96 (75.00) < 0.001 Yes 212 (11.02) 80 (6.77) 100 (16.26) 32 (25.00) Blood transfusion (%) No 1236 (64.24) 865 (73.24) 314 (51.06) 57 (44.53) < 0.001 Yes 688 (35.76) 316 (26.76) 301 (48.94) 71 (55.47) 30-day mortality(%) 377 (19.59) 153 (12.96) 151 (24.55) 73 (57.03) < 0.001 365-day mortality(%) 415 (21.57) 171 (14.48) 167 (27.15) 77 (60.16) < 0.001 Data are presented as median (interquartile range, IQR) or n (%). Abbreviations: BMI, body mass index; SOFA, Sequential Organ Failure Assessment; APS III, Acute Physiology Score III; SAPS II, Simplified Acute Physiology Score II; OASIS, Oxford Acute Severity of Illness Score; CCI, Charlson Comorbidity Index; AKI, acute kidney injury; CKD, chronic kidney disease; T2DM, type 2 diabetes mellitus; AMI, acute myocardial infarction; COPD, chronic obstructive pulmonary disease; WBC, white blood cell count; FBG, fasting blood glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO2, peripheral oxygen saturation; CRRT, continuous renal replacement therapy. Association Between RDW and Outcomes Restricted cubic spline (RCS) modeling was used to assess the dose-response relationship between baseline RDW levels and 30-day mortality risk in SAD patients (Fig. 3 ). The results showed a significant positive linear correlation between RDW and mortality risk (P for Overall < 0.001). The adjusted hazard ratio increased monotonically with the elevation of baseline RDW values. Figure 4 presents the Kaplan-Meier survival curves for all-cause mortality in SAD patients stratified by RDW trajectory groups, with (A) for 30-day survival analysis and (B) for 365-day survival analysis. The log-rank test results indicated significant differences in survival probabilities across different RDW trajectory groups (all intergroup comparisons P < 0.001). Specifically, patients in the Stable-Low group (Class 1) had the highest cumulative survival rate, which remained relatively stable over time, while the survival curves of patients in the Slowly-Rising (Class 2) and Persistently-Rising (Class 3) groups decreased significantly in a stepwise manner, with the worst prognosis in Class 3. The rapid decline in the number at risk within Class 3 during follow-up underscores the utility of a dynamically rising RDW trajectory as a potent indicator for predicting short-term and long-term mortality risk in SAD patients. Table 2 shows the results of multivariate Cox proportional hazards regression analysis on the association between RDW levels and 30-day and 365-day all-cause mortality in SAD patients. In the unadjusted Model 1, both the highest RDW tertile group (T3) and the high-risk trajectory group (Class 3) exhibited an extremely high mortality risk (HR > 3.0–6.0, P < 0.001). Even after full adjustment for demographic characteristics, disease severity scores (SOFA, APS III, etc.), comorbidities, therapeutic interventions, and key laboratory indicators in Model 3, elevated RDW remained an independent and significant predictor of mortality risk: for 30-day mortality, the hazard ratio (HR) of RDW trajectory Class 3 was 3.765 (95% CI: 2.727–5.199, P < 0.001), while that of the baseline RDW T3 group was 1.962 (95% CI: 1.427–2.697, P < 0.001). Similarly, during the 365-day follow-up period, the adjusted HRs of Class 3 and T3 were 3.635 (95% CI: 2.664–4.960) and 1.807 (95% CI: 1.338–2.442), respectively, with all P values < 0.001. Notably, the dynamic trajectory grouping of RDW (especially Class 3) demonstrated better predictive efficacy than single-time-point baseline measurement, indicating that progressive elevation of RDW is a powerful and independent biomarker for poor prognosis in SAD patients. Table 2 Association of RDW Levels with 30-Day and 365-Day Mortality Among SAD Patients Outcome Model 1 Model 2 Model 3 HR (95% CI) P HR (95% CI) P HR (95% CI) P 30-day mortality Baseline RDW tertiles T 1 ref ref ref T 2 1.731 (1.284, 2.334) < 0.001 1.62 (1.199, 2.187) 0.002 1.276 (0.933, 1.746) 0.127 T 3 3.31 (2.51, 4.365) < 0.001 3.096 (2.343, 4.093) < 0.001 1.962 (1.427, 2.697) < 0.001 RDW trajectories Class 1 ref ref ref Class 2 2.042 (1.631, 2.557) < 0.001 1.95 (1.556, 2.444) < 0.001 1.52 (1.185, 1.949) 0.001 Class 3 6.014 (4.548, 7.954) < 0.001 6.042 (4.565, 7.999) < 0.001 3.765 (2.727, 5.199) < 0.001 365-day mortality Baseline RDW tertiles T 1 ref ref ref T 2 1.724 (1.303, 2.28) < 0.001 1.609 (1.215, 2.131) 0.001 1.261 (0.94, 1.691) 0.121 T 3 3.107 (2.393, 4.033) < 0.001 2.903 (2.231, 3.779) < 0.001 1.807 (1.338, 2.442) < 0.001 RDW trajectories Class 1 ref ref ref Class 2 2.036 (1.645, 2.52) < 0.001 1.944 (1.57, 2.409) < 0.001 1.496 (1.181, 1.895) 0.001 Class 3 5.884 (4.492, 7.706) < 0.001 5.934 (4.526, 7.78) < 0.001 3.635 (2.664, 4.96) < 0.001 T1: RDW < 13.90, T2: 13.90 ≤ RDW < 15.70, T3: RDW ≥ 15.70. Model 1: Unadjusted crude model. Model 2: Adjusted for gender, age, race, BMI and respiratory rate. Model 3: Model 2 plus disease severity scores (SOFA, APS III, SAPS II, OASIS, CCI), comorbidities (hypertension, AKI, pneumonia, CKD, heart failure); treatment indicators (vasoactive drugs, CRRT and blood transfusion) and laboratory parameters (hemoglobin, platelets, anion gap, chloride and potassium). Subgroup and Sensitivity Analyses Figure 5 shows the association between dynamic RDW trajectory grouping and all-cause mortality in SAD patients after stratification by baseline characteristics including age, gender, BMI, hypertension, acute kidney injury (AKI), heart failure, and vasoactive drug use. The results showed that the high-risk RDW trajectory group (Class 3) had a significantly increased mortality risk (HR > 2.0–7.0) in all subgroups, and this trend was consistent in short-term and long-term follow-up. Tests for interaction showed that except for a marginally statistically significant heterogeneity in the heart failure subgroup in the 365-day mortality analysis (P for interaction = 0.038), no significant effect modification was observed for all other covariates (all P > 0.05). These findings indicate that the predictive value of RDW trajectory for mortality risk is highly robust and generalizable in populations with different clinical phenotypes, supporting its broad application potential as an independent prognostic biomarker. In the sensitivity analysis, Cox regression analysis on the dataset with missing values imputed by the mean method yielded results highly consistent with the initial analysis (Supplementary Table S6), confirming that the RDW trajectory is an important predictor of 30-day all-cause mortality regardless of missing data handling methods. An additional sensitivity analysis for hematological confounding factors showed that after excluding all transfused patients, the effect estimates of each trajectory pattern did not change significantly (Supplementary Table S9). Discussion This retrospective analysis of 1,924 patients with SAD utilized group-based trajectory modeling (GBTM) to delineate early ICU dynamics of RDW and confirmed their independent association with both short- and long-term mortality. Our key findings include: (1) RDW changes in SAD patients follow three distinct trajectories: stable-low, slowly-rising, and persistently-rising; (2) Patients exhibiting a persistently-rising trajectory (Class 3) presented with the most severe baseline organ dysfunction and the poorest clinical outcomes, with a 30-day mortality risk 3.76 times higher than those in the stable-low group; and (3) The prognostic value of RDW trajectories remained robust even after adjusting for blood transfusion, continuous renal replacement therapy (CRRT), and multiple severity-of-illness scores. Pathophysiological Mechanisms Linking RDW Trajectories to Prognosis The significantly higher mortality observed in the persistently-rising RDW group aligns with previous studies on baseline RDW but offers a more dynamic perspective[ 32 , 33 ]. Elevated RDW likely represents the convergence of multifaceted pathological mechanisms. First, the "cytokine storm" characteristic of sepsis (e.g., elevated IL-6 and TNF-α) can directly suppress erythropoietin (EPO) production and disrupt iron metabolism, leading to impaired erythropoiesis and anisocytosis[ 34 , 35 ]. Second, oxidative stress-induced damage to red blood cell membranes and shortened RBC lifespan contribute to widening RDW[ 36 ]. In SAD patients, a continuous rise in RDW may indicate uncontrolled systemic inflammation or persistent microcirculatory dysfunction and tissue hypoxia, thereby potentially exacerbating neurological injury and precipitating multi-organ failure[ 37 , 38 ]. Additionally, the higher incidence of AKI (71.09%) and need for CRRT (25.00%) in Class 3 patients suggest that worsening renal function—leading to reduced EPO synthesis and accumulation of uremic toxins—may further drive the vicious cycle of rising RDW[ 39 ]. Comparison with Previous Studies Numerous studies have validated the prognostic value of baseline RDW in sepsis. For instance, Zhang et al. utilizing the MIMIC-IV database demonstrated that elevated RDW at admission is an independent predictor of both short- and long-term all-cause mortality in critically ill patients with obstructive sleep apnea (OSA)[ 32 ]. However, such studies are limited by their reliance on single-timepoint measurements, which cannot reflect dynamic changes during treatment. Our study demonstrates that while the highest baseline RDW tertile (T3) was associated with mortality (HR = 1.96), the persistently-rising trajectory (Class 3) conferred a substantially higher risk (HR = 3.76). This suggests that monitoring the trend of RDW offers superior prognostic discrimination compared to admission values alone. This finding parallels observations by Huang et al. in acute myocardial infarction, where dynamic worsening of RDW was a stronger predictor of adverse cardiovascular events than baseline levels[ 40 ]. Furthermore, by focusing specifically on the SAD subgroup—a population with inherently high mortality and complex pathophysiology—our study fills a critical gap in the literature regarding longitudinal biomarkers in neuro-critical care. Clinical Implications Our findings hold significant potential for clinical translation. Firstly, RDW is a routine, low-cost, and readily available parameter in complete blood counts. Identifying a "persistently-rising" trajectory could provide clinicians with an early warning signal, prompting more aggressive anti-inflammatory strategies, intensified organ function monitoring, or early rehabilitation interventions for these high-risk patients[ 41 ]. Secondly, for patients exhibiting a continuous rise in RDW, clinicians should maintain a high index of suspicion for uncontrolled infection sources, persistent microcirculatory disturbances, or severe metabolic/nutritional derangements, and adjust therapeutic strategies accordingly[ 42 ]. Finally, integrating RDW trajectories into existing prognostic scoring systems (e.g., SOFA, APACHE II) may enhance the accuracy of mortality prediction in SAD patients. Strengths and Limitations The strengths of this study include a large sample size (n = 1,924), the use of an authoritative database (MIMIC-IV), and the application of advanced GBTM to identify heterogeneous trajectories. Moreover, we conducted comprehensive sensitivity analyses, including the exclusion of transfused patients—a known confounder for RDW levels—with consistent results, thereby strengthening the credibility of our conclusions. The study also adjusted for a wide range of confounders, including detailed comorbidities, laboratory parameters, and critical care interventions. However, several limitations must be acknowledged. First, given the retrospective observational design, we can ascertain association but not causality between RDW trajectories and mortality. Second, despite adjusting for transfusion, we cannot entirely rule out the influence of other unmeasured confounders, such as specific nutritional support protocols or antibiotic regimens. Third, as MIMIC-IV data originate primarily from a single medical center in the United States, the generalizability of our findings requires validation in cohorts with diverse racial backgrounds and healthcare systems[ 43 ]. Fourth, delirium diagnosis relied on CAM-ICU documentation, which may be subject to under-diagnosis or assessment bias, although we employed strict inclusion criteria to minimize such biases[ 44 ]. Future prospective, multicenter studies should explore whether interventions targeting the rising RDW trajectory (e.g., via anti-inflammatory or microcirculation-improving therapies) can improve clinical outcomes in SAD patients. Conclusion In conclusion, longitudinal dynamic changes in RDW are powerful independent predictors of prognosis in patients with SAD. Specifically, a persistently rising RDW pattern signifies an extremely high risk of mortality. Clinicians should incorporate dynamic RDW monitoring into the routine management of SAD patients, combining it with other clinical indicators for comprehensive risk assessment to potentially improve survival outcomes in this vulnerable population. Declarations Consent to participate: The MIMIC-IV database, which de-identifies patient records, has been approved by the institutional ethics review boards of both MIT and the Beth Israel Deaconess Medical Center and is exempt from the requirement for informed consent. Theinformation of all patients was anonymized prior to extraction and data analysis,thereby waiving the requirement for individual patient consent. This study adhered tothe principles outlined in the Declaration of Helsinki. Shuyang Dai was authorized to access the database after completing online training and assessments, as evidenced by certification number 14012091. Consent for publication: Not applicable. Clinical trial number: Not applicable. Availability of data and materials: The availability of data and materials can be obtained by reaching out to the corresponding author via email at [email protected] . Competing interests The authors have no conflicts of interest to declare. Funding No Authors' contributions Shuyang Dai, Bingjie Li and Zongshan Zhang: original draft, investigation, formal analysis, and data curation. Gaoli Zhang: Methodology, Formal analysis, Data curation. Poshi Xu: original draft, methodology, formal analysis, data curation. Acknowledgements We sincerely thank the MIMIC-IV database team for providing the clinical data required for this study, and the Biostatistics Research Center of Fuwai Central China Cardiovascular Hospital for the guidance on statistical modeling and analysis. References Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. 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JAMA. 1993;270:2957–63. https://doi.org/10.1001/jama.270.24.2957. Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest. 1991;100:1619–36. https://doi.org/10.1378/chest.100.6.1619. Johnson AEW, Kramer AA, Clifford GD. A new severity of illness scale using a subset of Acute Physiology And Chronic Health Evaluation data elements shows comparable predictive accuracy. Crit Care Med. 2013;41:1711–8. https://doi.org/10.1097/CCM.0b013e31828a24fe. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83. https://doi.org/10.1016/0021-9681(87)90171-8. De Rosa S, Samoni S, Ronco C. Creatinine-based definitions: from baseline creatinine to serum creatinine adjustment in intensive care. Crit Care. 2016;20:69. https://doi.org/10.1186/s13054-016-1218-4. Nagin DS, Jones BL, Elmer J. Recent Advances in Group-Based Trajectory Modeling for Clinical Research. Annu Rev Clin Psychol. 2024;20:285–305. https://doi.org/10.1146/annurev-clinpsy-081122-012416. Xu X, Huang R, Lin Y, Guo Y, Xiong Z, Zhong X, et al. High triglyceride-glucose index in young adulthood is associated with incident cardiovascular disease and mortality in later life: insight from the CARDIA study. Cardiovasc Diabetol. 2022;21:155. https://doi.org/10.1186/s12933-022-01593-7. van de Schoot R, Sijbrandij M, Winter SD, Depaoli S, Vermunt JK. The grolts-checklist: guidelines for reporting on latent trajectory studies. Struct Equ Model. Zhang C, Xiao J, Gao X, Guo C, Feng N, Zhang Y, et al. The role of red blood cell distribution width in prognosis prediction among critically ill patients with obstructive sleep apnea: Insights from the MIMIC-IV database. Sci Rep. 2026. https://doi.org/10.1038/s41598-026-43197-1. Ding S, Zhang Z, Bo Q, Ma C, Wu M, Di X, et al. Prognostic value of red blood cell distribution width in traumatic brain injury: A mediation and deep learning analysis. PLoS One. 2026;21:e0339879. https://doi.org/10.1371/journal.pone.0339879. Ferreira JP, Lamiral Z, Bakris G, Mehta C, White WB, Zannad F. Red cell distribution width in patients with diabetes and myocardial infarction: An analysis from the EXAMINE trial. Diabetes Obes Metab. 2021;23:1580–7. https://doi.org/10.1111/dom.14371. Sarkar S, Kannan S, Khanna P, Singh AK. Role of red blood cell distribution width, as a prognostic indicator in COVID-19: A systematic review and meta-analysis. Rev Med Virol. 2022;32:e2264. https://doi.org/10.1002/rmv.2264. Liu F, Lin B, Huang W, Dai J, Hu Y, Wu Z, et al. Association Between Hemoglobin-to-Red Blood Cell Distribution Width Ratio and Arterial Stiffness. J Clin Hypertens (Greenwich). 2025;27:e70141. https://doi.org/10.1111/jch.70141. Wang C, Zhu C, Deng X, Zhang W. Dual effects and balanced regulation of cytokines in sepsis. Trends Immunol. 2025;46:762–5. https://doi.org/10.1016/j.it.2025.10.002. Serova V, Klibus M, Marcinkevics Z, Rubins U, Grabovskis A, Sabelnikovs O. Microcirculation Monitoring in Septic Shock: Focused Review. Medicina (Kaunas). 2026;62:346. https://doi.org/10.3390/medicina62020346. Silverberg DS, Wexler D, Sheps D, Blum M, Keren G, Baruch R, et al. The effect of correction of mild anemia in severe, resistant congestive heart failure using subcutaneous erythropoietin and intravenous iron: a randomized controlled study. J Am Coll Cardiol. 2001;37:1775–80. https://doi.org/10.1016/s0735-1097(01)01248-7. Huang Y-L, Hu Z-D, Liu S-J, Sun Y, Qin Q, Qin B-D, et al. Prognostic value of red blood cell distribution width for patients with heart failure: a systematic review and meta-analysis of cohort studies. PLoS One. 2014;9:e104861. https://doi.org/10.1371/journal.pone.0104861. Devlin JW, Skrobik Y, Gélinas C, Needham DM, Slooter AJC, Pandharipande PP, et al. Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU. Crit Care Med. 2018;46:e825–73. https://doi.org/10.1097/CCM.0000000000003299. Marik PE. The demise of early goal-directed therapy for severe sepsis and septic shock. Acta Anaesthesiol Scand. 2015;59:561–7. https://doi.org/10.1111/aas.12479. Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10:1. https://doi.org/10.1038/s41597-022-01899-x. Ely EW, Inouye SK, Bernard GR, Gordon S, Francis J, May L, et al. Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the intensive care unit (CAM-ICU). JAMA. 2001;286:2703–10. https://doi.org/10.1001/jama.286.21.2703. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.doc Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 29 Mar, 2026 Editor invited by journal 20 Mar, 2026 Editor assigned by journal 20 Mar, 2026 Submission checks completed at journal 18 Mar, 2026 First submitted to journal 16 Mar, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9080605","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615428544,"identity":"c1c66b5d-6fb7-4d33-8304-c92b2c896766","order_by":0,"name":"Shuyang Dai","email":"","orcid":"","institution":"Fuwai Central China Cardiovascular Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuyang","middleName":"","lastName":"Dai","suffix":""},{"id":615428545,"identity":"90cf2643-722e-4b39-ba17-bc2f0cf2f6b8","order_by":1,"name":"Bingjie Li","email":"","orcid":"","institution":"Fuwai Central China Cardiovascular Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bingjie","middleName":"","lastName":"Li","suffix":""},{"id":615428546,"identity":"531376ba-202f-4ed5-b275-e7288ee187e5","order_by":2,"name":"Zongshan Zhang","email":"","orcid":"","institution":"Fuwai Central China Cardiovascular Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zongshan","middleName":"","lastName":"Zhang","suffix":""},{"id":615428547,"identity":"b808463c-247a-4c59-9401-eee586ddbbf4","order_by":3,"name":"Gaoli Zhang","email":"","orcid":"","institution":"Fuwai Central China Cardiovascular Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gaoli","middleName":"","lastName":"Zhang","suffix":""},{"id":615428548,"identity":"c7fd946e-684d-4b3e-8271-f1fe50e1af90","order_by":4,"name":"Poshi Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBAC++PNBx98/GPDzM/eQKyeM8eSDWc2pLFL9hwgVsuNHDNh3obD/AYzEojUwTgjwYxx5o40aQPJxxtvMNTYRBPUwszzIO3BxzM2xubSacUWDMfSchsIaWFjTzhuOIMtLdlydo6ZBGPDYcJaeBgS26R52A7Xb7h5hkgtEhzJbNK8bYeZDW7wEKnFgOcYs+GMM2nMkj1AvyQQ4xcD9v6PDz5UgKLy8MYbH2psCGtB0S6RQIpyiBZSdYyCUTAKRsHIAADcg0JhMwA4FgAAAABJRU5ErkJggg==","orcid":"","institution":"Fuwai Central China Cardiovascular Hospital","correspondingAuthor":true,"prefix":"","firstName":"Poshi","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2026-03-10 07:38:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9080605/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9080605/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106069133,"identity":"93ac28bf-0730-41d1-9ab8-3f5ee6c44e48","added_by":"auto","created_at":"2026-04-03 06:22:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148544,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the selection of patients\u003c/strong\u003e. MIMIC-IV: Medical Information Mart for Intensive Care IV; ICU: intensive care unit; RDW, red blood cell distribution width; BMI, body mass index.\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-9080605/v1/ddd01cd0fbd230f5c4d4e38d.png"},{"id":106095380,"identity":"b90d0420-2f8b-49b1-af71-acc50c2b4f42","added_by":"auto","created_at":"2026-04-03 11:47:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151462,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrajectories of RDW during the first 5 days of ICU admission. \u003c/strong\u003eWe identified three distinct classes of RDW changes during the first 5 days of ICU admission. \u0026nbsp;Class 1, Stable-Low; Class 2, Slowly-Rising; Class 3, Persistently-Rising.\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-9080605/v1/b9398844620a67bba895a3d5.png"},{"id":106094773,"identity":"b3a4ccf5-2016-466e-b79a-4ddc69776283","added_by":"auto","created_at":"2026-04-03 11:43:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe restricted cubic spline (RCS) curve for the potential linear associations between baseline RDW levels and the risk of 30-day mortality in patients with SAD.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-9080605/v1/9f6ca0f072ef7e4af3a70a9f.png"},{"id":106069136,"identity":"cfca0d7d-6d34-45c8-b7b8-d4a10fa9ce91","added_by":"auto","created_at":"2026-04-03 06:22:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":148714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival curves for all-cause mortality according to RDW trajectories in SAD patients. \u003c/strong\u003e(A) 30-day all-cause mortality; (B) 365-day all-cause mortality. Log-rank test was used for intergroup comparison.\u003c/p\u003e","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-9080605/v1/d8edec5623cbc6743f64d57a.png"},{"id":106094925,"identity":"e2671626-d1c2-4f04-a445-514e8b2799c9","added_by":"auto","created_at":"2026-04-03 11:43:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":535436,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis of the association between RDW trajectories and all-cause mortality in SAD patients.\u003c/strong\u003e (A) 30-day all-cause mortality; (B) 365-day all-cause mortality. P for interaction was calculated to assess the homogeneity of HRs across subgroups. Abbreviations: AKI, acute kidney injury; BMI, body mass index.\u003c/p\u003e","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-9080605/v1/db65c211dff2c852bbd726aa.png"},{"id":106096547,"identity":"c306b206-1853-4a77-bff2-8013f9a3bb45","added_by":"auto","created_at":"2026-04-03 11:55:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2440360,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9080605/v1/d5db10f4-4013-4d78-903b-a9db197680f5.pdf"},{"id":106069134,"identity":"6c8bf1fe-1018-4d31-89df-43f85efa0a26","added_by":"auto","created_at":"2026-04-03 06:22:48","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":258048,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-9080605/v1/9f15048d935a3fbc02ade404.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic RDW Trajectories Predict Mortality in Sepsis-Associated Delirium: A Group-Based Trajectory Modeling Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, remains a leading cause of global morbidity and mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Delirium, a common neurological complication of sepsis, affects 50% to 80% of critically ill patients. It is associated with prolonged mechanical ventilation, extended ICU stays, long-term cognitive impairment, and increased mortality[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although recent advances have elucidated the pathophysiological mechanisms of sepsis-associated delirium (SAD)-including neuroinflammation, blood-brain barrier disruption, and microcirculatory dysfunction[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]-there remains a critical need for simple, dynamic, and effective biomarkers to identify high-risk patients early and guide prognostic assessment.\u003c/p\u003e \u003cp\u003eTraditionally used for the differential diagnosis of anemia, red blood cell distribution width (RDW) is increasingly recognized as a marker reflecting ineffective erythropoiesis, chronic inflammation, oxidative stress, and nutritional deficiencies[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Previous cross-sectional studies have established that elevated baseline RDW is independently associated with disease severity and mortality in sepsis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, single-timepoint measurements may fail to capture the dynamic evolution of the disease process. In critical illness, the longitudinal trajectory of RDW may provide a more comprehensive reflection of the evolving inflammatory burden and the bone marrow's compensatory capacity than baseline values alone[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, research on the dynamic trajectories of RDW specifically within the SAD population is scant. Most existing studies focus solely on admission levels, overlooking the prognostic implications of longitudinal fluctuations during ICU treatment[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, common ICU interventions such as blood transfusion, acute kidney injury (AKI), and vasopressor use can influence RDW levels, yet prior studies have often inadequately adjusted for these time-varying confounders[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Group-based trajectory modeling (GBTM), an advanced person-centered statistical approach, identifies homogeneous subgroups within heterogeneous populations based on similar developmental patterns. While GBTM has demonstrated utility in predicting outcomes in cardiovascular disease and acute kidney disease[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], its application to RDW dynamics in SAD remains unexplored.\u003c/p\u003e \u003cp\u003eTherefore, leveraging the large-scale public MIMIC-IV database, this study aims to: (1) characterize the longitudinal trajectories of RDW during the early ICU stay in patients with SAD; (2) investigate the association between distinct RDW trajectory patterns and 30-day and 365-day all-cause mortality; and (3) validate the robustness of these findings through rigorous sensitivity analyses, including the exclusion of transfused patients. We hypothesized that a persistently rising RDW trajectory would independently predict adverse outcomes in SAD patients, demonstrating superior prognostic discrimination compared to single baseline measurements. Our findings aim to provide new evidence-based insights for dynamic risk stratification in this high-risk population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study utilized data from version 3.0 of the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, which contains comprehensive electronic health records of ICU patients at Beth Israel Deaconess Medical Center in Boston, Massachusetts, USA, from 2008 to 2022[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. All patient data in the database have been de-identified in accordance with the privacy provisions of the Health Insurance Portability and Accountability Act (HIPAA), and no individual informed consent was required[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Researchers obtained access to the database after completing the Collaborative Institutional Training Initiative (CITI) program conducted by the National Institutes of Health (record number: 14012091). All datasets were de-identified before release, and no direct or indirect patient identification information was accessed at any stage of the study analysis.\u003c/p\u003e \u003cp\u003e The MIMIC-IV project has been approved by the Institutional Review Boards (IRBs) of Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology, with a waiver of informed consent. This study exclusively used publicly available de-identified data and required no additional IRB review. In accordance with journal policies, all ethical and data source information is stated in this section.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe study cohort included adult patients (\u0026ge;\u0026thinsp;18 years old) admitted to the ICU with a concurrent diagnosis of sepsis and delirium. The diagnosis of delirium was required to be established within 72 hours of ICU admission to mitigate the confounding effect of late-onset delirium on RDW trajectory evolution. Sepsis was identified using International Classification of Diseases (ICD)-9 and ICD-10 codes (ICD-9: 995.91, 995.92; ICD-10: A40.x, A41.x), supplemented by a Sequential Organ Failure Assessment (SOFA) score\u0026thinsp;\u0026ge;\u0026thinsp;2 points, in line with the Sepsis-3 criteria[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Delirium was diagnosed using the Confusion Assessment Method for the ICU (CAM-ICU), and its diagnostic criteria were extracted from the delirium assessment module of the MIMIC-IV database, a method validated in previous studies[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe following exclusion criteria were applied: (1) ICU length of stay (LOS)\u0026thinsp;\u0026lt;\u0026thinsp;72 hours; (2) missing RDW measurement data for any 24-hour period within the first 5 days of ICU admission; (3) presence of malignant tumors; and (4) multiple ICU admissions due to sepsis (only the first admission was included).\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData extraction was performed using Structured Query Language (SQL) with PostgreSQL (Version 15) and Navicat Premium (Version 17). The extracted variables included:\u003c/p\u003e \u003cp\u003e(1) Demographic characteristics: age, gender, race, body mass index (BMI);\u003c/p\u003e \u003cp\u003e(2) Comorbidities: hypertension, type 2 diabetes mellitus, heart failure, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), pneumonia, malignant tumor, acute myocardial infarction, ischemic cardiomyopathy, ischemic stroke;\u003c/p\u003e \u003cp\u003e(3) Laboratory parameters: white blood cell count (WBC), hemoglobin, platelet count, serum chloride, potassium, lactate, glucose, anion gap, serum sodium, and RDW (measured at least every 24 hours within the first 120 hours);\u003c/p\u003e \u003cp\u003e(4) Vital signs: systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate, respiratory rate, body temperature, and oxygen saturation;\u003c/p\u003e \u003cp\u003e(5) Disease severity scores: SOFA score[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Simplified Acute Physiology Score II (SAPS II)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], Acute Physiology Score III (APS III)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Oxford Acute Severity of Illness Score (OASIS)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and Charlson Comorbidity Index (CCI)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e];\u003c/p\u003e \u003cp\u003e(6) Therapeutic interventions: mechanical ventilation, vasoactive drugs;\u003c/p\u003e \u003cp\u003e(7) Clinical outcomes: ICU mortality, in-hospital mortality, and 30-day survival status.\u003c/p\u003e \u003cp\u003eFor patients with multiple RDW measurements within 24 hours, the first recorded value was used. Variables with missing data exceeding 20% were excluded from the analysis, whereas those with \u0026le;\u0026thinsp;20% missingness were handled via multiple imputation[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The variance inflation factor (VIF) was used to assess multicollinearity of covariates in the Cox proportional hazards model, with a VIF value\u0026thinsp;\u0026lt;\u0026thinsp;5 indicating no significant multicollinearity.\u003c/p\u003e \u003cp\u003eThe blood transfusion variable was determined using specific codes and item labels in the MIMIC-IV database: a binary variable for blood transfusion (filtered red blood cells, suspended red blood cells, and packed red blood cells) during ICU stay was created. This variable was included in the adjusted model and used to define the exclusion subgroup (all transfused patients excluded) in the sensitivity analysis.\u003c/p\u003e\n\u003ch3\u003eOutcome\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was 30-day all-cause mortality after ICU admission; the secondary outcome was 365-day all-cause mortality.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eGroup-based trajectory modeling (GBTM), a person-centered analytical approach, was used to identify distinct longitudinal patterns of RDW changes in patients during ICU stay[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This semi-parametric mixed modeling technique identifies homogeneous subgroups in heterogeneous populations based on similar temporal developmental trajectories.\u003c/p\u003e \u003cp\u003eThe optimal number of trajectory subgroups was determined by systematically evaluating 2 to 5 latent class models. Model selection was based on multiple statistical criteria, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Sample-size Adjusted Bayesian Information Criterion (SABIC), entropy, log-likelihood ratio, and clinical interpretability. The model fit was evaluated for potential trajectory shapes (linear, quadratic, cubic) to select the optimal configuration. The final model was required to meet the following conditions: the proportion of each trajectory subgroup in the study population\u0026thinsp;\u0026gt;\u0026thinsp;5%; the average posterior probability of each trajectory subgroup\u0026thinsp;\u0026gt;\u0026thinsp;0.7; and good model convergence with clinical significance[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This study strictly followed the Guidelines for Reporting on Latent Trajectory Studies (GRoLTS) checklist to ensure methodological rigor and transparency[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A three-class trajectory model was ultimately identified as the best-fitting model (see Supplementary Tables S2 and S3 for details).\u003c/p\u003e \u003cp\u003eContinuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range, IQR) based on normality test results (Kolmogorov-Smirnov test). Categorical variables were reported as frequencies and percentages. Intergroup comparisons of continuous variables were performed using analysis of variance (ANOVA) or the Kruskal-Wallis test, and those of categorical variables using the chi-square test or Fisher\u0026rsquo;s exact test. Kaplan-Meier survival analysis with the log-rank test was used to compare mortality risks across different RDW trajectories. Univariate Cox regression (Model 1) was used to screen covariates associated with 30-day mortality (Supplementary Table\u0026nbsp;5), and variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included in the multivariate model. Model 2 was adjusted for gender, age, race, BMI, and respiratory rate. Model 3 was further adjusted for disease severity scores (SOFA, APS III, SAPS II, OASIS, CCI), comorbidities (hypertension, acute kidney injury, pneumonia, chronic kidney disease, heart failure), therapeutic indicators (vasoactive drugs, continuous renal replacement therapy [CRRT], blood transfusion), and laboratory parameters (hemoglobin, platelets, anion gap, chloride, potassium). Subgroup analyses were conducted for age, gender, BMI, hypertension, AKI, heart failure, and vasoactive drug use.\u003c/p\u003e \u003cp\u003eTo verify the robustness of the study results, two sensitivity analyses were performed. Given that the proportion of missing data for all covariates was less than 5% (Supplementary Table\u0026nbsp;4), we first imputed missing values using the mean imputation method and repeated the main analysis. Second, for the analysis of 30-day and 365-day mortality, we re-analyzed the data after excluding all patients who received blood transfusion during their ICU stay.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R statistical software (Version 4.2.2) and DecisionLine 1.0 software. A two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant, and hazard ratios and regression coefficients were reported with 95% confidence intervals (95% CI). This study was conducted in accordance with the Declaration of Helsinki. Since the MIMIC-IV database used is public and de-identified, this retrospective analysis was exempt from additional ethical review, and no informed consent from patients was required.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of RDW Trajectory Subgroups\u003c/h2\u003e \u003cp\u003eA total of 1,924 SAD patients met the inclusion criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Three distinct RDW trajectory patterns were identified in ICU patients using group-based trajectory modeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with details of model fitting shown in Supplementary Tables S2 and S3. The trajectory patterns were named based on initial levels and change trends: Class 1 (Stable-Low): n\u0026thinsp;=\u0026thinsp;1,180 (61.4%). RDW remained stable at a low level of approximately 14%, exhibiting minimal fluctuation. Class 2 (Slowly-Rising): n\u0026thinsp;=\u0026thinsp;651 (31.9%). RDW rose slowly from approximately 16.7% to 17.2%. Class 3 (Persistently-Rising): n\u0026thinsp;=\u0026thinsp;128 (6.7%). RDW started at a high level of 22% and continued to rise to nearly 23%. The posterior probabilities of each subgroup were 97.8%, 95.5%, and 98.5%, respectively (Supplementary Table S3), indicating excellent model classification accuracy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBaseline Characteristics of Different RDW Trajectory Patterns\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, among the 1,924 SAD patients, the mean age was 66.0 years, and 59.25% were male. Significant differences were observed in baseline characteristics, disease severity, and clinical outcomes across the different RDW trajectory groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with the Stable-Low group (Class 1), patients in the Slowly-Rising (Class 2) and Persistently-Rising (Class 3) groups had higher organ failure scores (SOFA, APS III, etc.), more severe anemia (median hemoglobin: 11.4, 9.2, and 8.5 g/dL, respectively), and a higher incidence of acute kidney injury (AKI). The demand for critical care interventions (e.g., CRRT and blood transfusion) increased significantly with the elevation of RDW trajectory grade. Notably, mortality increased in a stepwise manner: the 30-day and 365-day mortality rates in Class 3 were as high as 57.03% and 60.16%, respectively, which were significantly higher than those in Class 1 (12.96% and 14.48%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that the drivers of the rising RDW trajectory are closely linked to severe underlying pathophysiology, including organ dysfunction and inflammatory burden.\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\u003eClinical characteristics and outcomes by RDW trajectory in ARF Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;1,924)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClass 1 (n\u0026thinsp;=\u0026thinsp;1,181)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClass 2 (n\u0026thinsp;=\u0026thinsp;615)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClass 3 (n\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \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=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (55\u0026ndash;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (54\u0026ndash;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (57\u0026ndash;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (54-77.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n(%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e784 (40.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e446 (37.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e272 (44.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (51.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1140 (59.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e735 (62.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e343 (55.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62 (48.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165 (8.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (7.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (9.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (10.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1500 (77.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e940 (79.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e462 (75.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 (76.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e259 (13.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (12.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (15.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (13.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.191 (24.215\u0026ndash;33.298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.056 (24.408\u0026ndash;32.809)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.406 (23.692\u0026ndash;33.955)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.984 (24.411\u0026ndash;33.385)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (4\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (5\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (6\u0026ndash;13)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPSIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (37\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (35\u0026ndash;61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (41\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65 (50.75\u0026ndash;84.25)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPS II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (32\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (31\u0026ndash;48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (34\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.5 (37.5\u0026ndash;57)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOASIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (31\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (30\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (32\u0026ndash;43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (31\u0026ndash;43)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (4\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (4\u0026ndash;8)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1177 (61.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e670 (56.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e414 (67.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93 (72.66)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747 (38.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e511 (43.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201 (32.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (27.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI(%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e904 (46.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e638 (54.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e229 (37.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (28.91)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1020 (53.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e543 (45.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e386 (62.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91 (71.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePneumonia (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1075 (55.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e675 (57.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e331 (53.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69 (53.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e849 (44.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e506 (42.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e284 (46.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 (46.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1760 (91.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1077 (91.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e568 (92.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115 (89.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164 (8.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (8.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (7.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (10.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD(%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1532 (79.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e986 (83.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e448 (72.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 (76.56)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e392 (20.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (16.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167 (27.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (23.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1330 (69.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e842 (71.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400 (65.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (68.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e594 (30.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339 (28.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215 (34.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (31.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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=\"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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1289 (67.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e835 (70.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e375 (60.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79 (61.72)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e635 (33.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346 (29.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240 (39.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 (38.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIschemic cardiomyopathy (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1228 (63.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e751 (63.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e386 (62.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91 (71.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e696 (36.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e430 (36.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e229 (37.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (28.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1596 (82.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1021 (86.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e467 (75.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108 (84.38)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328 (17.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (13.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148 (24.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (15.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1677 (87.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1035 (87.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e527 (85.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115 (89.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247 (12.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (12.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (14.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (10.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5 (8.8\u0026ndash;12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4 (9.8\u0026ndash;12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2 (7.85\u0026ndash;10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.5 (7.6\u0026ndash;10)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (\u0026times;103/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (121\u0026ndash;243)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188 (139\u0026ndash;243)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (101.5\u0026ndash;247)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131.5 (67.75-214.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (\u0026times;103/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.7 (8.9\u0026ndash;17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.8 (9.3\u0026ndash;17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.3 (8.25\u0026ndash;17.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.4 (8.25-19.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnion gap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (12\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (12\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (12\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (13\u0026ndash;20)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChloride(mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (100\u0026ndash;108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (101\u0026ndash;108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (98\u0026ndash;108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102 (96\u0026ndash;107)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137 (110\u0026ndash;180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (112\u0026ndash;181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (109-180.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e127.5 (97.5\u0026ndash;171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2 (3.7\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1 (3.7\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2 (3.7\u0026ndash;4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1 (3.6-4.725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (135\u0026ndash;142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (136\u0026ndash;142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (135\u0026ndash;142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138 (133\u0026ndash;141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (102\u0026ndash;138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (103\u0026ndash;139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (101-135.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114.5 (101-131.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate(beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (77\u0026ndash;104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (76\u0026ndash;103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (77\u0026ndash;107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91.5 (80-105.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (57\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (58\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (56\u0026ndash;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (55\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiration rate(breaths/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (16\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (16\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (16\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (16\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature(f)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.4 (97.8\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.5 (97.9\u0026ndash;99.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.3 (97.7\u0026ndash;98.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.1 (97.675\u0026ndash;98.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO2(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (96\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (96\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (96\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 (95\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasive ventilation (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (3.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (3.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1863 (96.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1145 (96.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e595 (96.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123 (96.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVasoactive drugs(%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e410 (21.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e285 (24.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (17.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (14.06)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1514 (78.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e896 (75.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e508 (82.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 (85.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRRT (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1712 (88.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1101 (93.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e515 (83.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96 (75.00)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (11.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (6.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (16.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood transfusion (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1236 (64.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e865 (73.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e314 (51.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57 (44.53)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e688 (35.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316 (26.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e301 (48.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71 (55.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day mortality(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e377 (19.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153 (12.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151 (24.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 (57.03)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e365-day mortality(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e415 (21.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171 (14.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167 (27.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77 (60.16)\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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are presented as median (interquartile range, IQR) or n (%). Abbreviations: BMI, body mass index; SOFA, Sequential Organ Failure Assessment; APS III, Acute Physiology Score III; SAPS II, Simplified Acute Physiology Score II; OASIS, Oxford Acute Severity of Illness Score; CCI, Charlson Comorbidity Index; AKI, acute kidney injury; CKD, chronic kidney disease; T2DM, type 2 diabetes mellitus; AMI, acute myocardial infarction; COPD, chronic obstructive pulmonary disease; WBC, white blood cell count; FBG, fasting blood glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO2, peripheral oxygen saturation; CRRT, continuous renal replacement therapy.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation Between RDW and Outcomes\u003c/h2\u003e \u003cp\u003eRestricted cubic spline (RCS) modeling was used to assess the dose-response relationship between baseline RDW levels and 30-day mortality risk in SAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results showed a significant positive linear correlation between RDW and mortality risk (P for Overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The adjusted hazard ratio increased monotonically with the elevation of baseline RDW values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the Kaplan-Meier survival curves for all-cause mortality in SAD patients stratified by RDW trajectory groups, with (A) for 30-day survival analysis and (B) for 365-day survival analysis. The log-rank test results indicated significant differences in survival probabilities across different RDW trajectory groups (all intergroup comparisons P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, patients in the Stable-Low group (Class 1) had the highest cumulative survival rate, which remained relatively stable over time, while the survival curves of patients in the Slowly-Rising (Class 2) and Persistently-Rising (Class 3) groups decreased significantly in a stepwise manner, with the worst prognosis in Class 3. The rapid decline in the number at risk within Class 3 during follow-up underscores the utility of a dynamically rising RDW trajectory as a potent indicator for predicting short-term and long-term mortality risk in SAD patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of multivariate Cox proportional hazards regression analysis on the association between RDW levels and 30-day and 365-day all-cause mortality in SAD patients. In the unadjusted Model 1, both the highest RDW tertile group (T3) and the high-risk trajectory group (Class 3) exhibited an extremely high mortality risk (HR\u0026thinsp;\u0026gt;\u0026thinsp;3.0\u0026ndash;6.0, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Even after full adjustment for demographic characteristics, disease severity scores (SOFA, APS III, etc.), comorbidities, therapeutic interventions, and key laboratory indicators in Model 3, elevated RDW remained an independent and significant predictor of mortality risk: for 30-day mortality, the hazard ratio (HR) of RDW trajectory Class 3 was 3.765 (95% CI: 2.727\u0026ndash;5.199, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while that of the baseline RDW T3 group was 1.962 (95% CI: 1.427\u0026ndash;2.697, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, during the 365-day follow-up period, the adjusted HRs of Class 3 and T3 were 3.635 (95% CI: 2.664\u0026ndash;4.960) and 1.807 (95% CI: 1.338\u0026ndash;2.442), respectively, with all P values\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Notably, the dynamic trajectory grouping of RDW (especially Class 3) demonstrated better predictive efficacy than single-time-point baseline measurement, indicating that progressive elevation of RDW is a powerful and independent biomarker for poor prognosis in SAD patients.\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\u003eAssociation of RDW Levels with 30-Day and 365-Day Mortality Among SAD Patients\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\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\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e30-day mortality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eBaseline RDW tertiles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT 1\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 \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eref\u003c/p\u003e \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\u003eT 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.731 (1.284, 2.334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62 (1.199, 2.187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.276 (0.933, 1.746)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.31 (2.51, 4.365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.096 (2.343, 4.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.962 (1.427, 2.697)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eRDW trajectories\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 1\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 \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eref\u003c/p\u003e \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\u003eClass 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.042 (1.631, 2.557)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.95 (1.556, 2.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.52 (1.185, 1.949)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.014 (4.548, 7.954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.042 (4.565, 7.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.765 (2.727, 5.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e365-day mortality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eBaseline RDW tertiles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT 1\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 \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eref\u003c/p\u003e \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\u003eT 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.724 (1.303, 2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.609 (1.215, 2.131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.261 (0.94, 1.691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.107 (2.393, 4.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.903 (2.231, 3.779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.807 (1.338, 2.442)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eRDW trajectories\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 1\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 \u003cp\u003eref\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eref\u003c/p\u003e \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\u003eClass 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.036 (1.645, 2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.944 (1.57, 2.409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.496 (1.181, 1.895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.884 (4.492, 7.706)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.934 (4.526, 7.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.635 (2.664, 4.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eT1: RDW\u0026thinsp;\u0026lt;\u0026thinsp;13.90, T2: 13.90\u0026thinsp;\u0026le;\u0026thinsp;RDW\u0026thinsp;\u0026lt;\u0026thinsp;15.70, T3: RDW\u0026thinsp;\u0026ge;\u0026thinsp;15.70. Model 1: Unadjusted crude model. Model 2: Adjusted for gender, age, race, BMI and respiratory rate. Model 3: Model 2 plus disease severity scores (SOFA, APS III, SAPS II, OASIS, CCI), comorbidities (hypertension, AKI, pneumonia, CKD, heart failure); treatment indicators (vasoactive drugs, CRRT and blood transfusion) and laboratory parameters (hemoglobin, platelets, anion gap, chloride and potassium).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup and Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the association between dynamic RDW trajectory grouping and all-cause mortality in SAD patients after stratification by baseline characteristics including age, gender, BMI, hypertension, acute kidney injury (AKI), heart failure, and vasoactive drug use. The results showed that the high-risk RDW trajectory group (Class 3) had a significantly increased mortality risk (HR\u0026thinsp;\u0026gt;\u0026thinsp;2.0\u0026ndash;7.0) in all subgroups, and this trend was consistent in short-term and long-term follow-up. Tests for interaction showed that except for a marginally statistically significant heterogeneity in the heart failure subgroup in the 365-day mortality analysis (P for interaction\u0026thinsp;=\u0026thinsp;0.038), no significant effect modification was observed for all other covariates (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These findings indicate that the predictive value of RDW trajectory for mortality risk is highly robust and generalizable in populations with different clinical phenotypes, supporting its broad application potential as an independent prognostic biomarker.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the sensitivity analysis, Cox regression analysis on the dataset with missing values imputed by the mean method yielded results highly consistent with the initial analysis (Supplementary Table S6), confirming that the RDW trajectory is an important predictor of 30-day all-cause mortality regardless of missing data handling methods. An additional sensitivity analysis for hematological confounding factors showed that after excluding all transfused patients, the effect estimates of each trajectory pattern did not change significantly (Supplementary Table S9).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective analysis of 1,924 patients with SAD utilized group-based trajectory modeling (GBTM) to delineate early ICU dynamics of RDW and confirmed their independent association with both short- and long-term mortality. Our key findings include: (1) RDW changes in SAD patients follow three distinct trajectories: stable-low, slowly-rising, and persistently-rising; (2) Patients exhibiting a persistently-rising trajectory (Class 3) presented with the most severe baseline organ dysfunction and the poorest clinical outcomes, with a 30-day mortality risk 3.76 times higher than those in the stable-low group; and (3) The prognostic value of RDW trajectories remained robust even after adjusting for blood transfusion, continuous renal replacement therapy (CRRT), and multiple severity-of-illness scores.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePathophysiological Mechanisms Linking RDW Trajectories to Prognosis\u003c/h2\u003e \u003cp\u003eThe significantly higher mortality observed in the persistently-rising RDW group aligns with previous studies on baseline RDW but offers a more dynamic perspective[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Elevated RDW likely represents the convergence of multifaceted pathological mechanisms. First, the \"cytokine storm\" characteristic of sepsis (e.g., elevated IL-6 and TNF-α) can directly suppress erythropoietin (EPO) production and disrupt iron metabolism, leading to impaired erythropoiesis and anisocytosis[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Second, oxidative stress-induced damage to red blood cell membranes and shortened RBC lifespan contribute to widening RDW[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In SAD patients, a continuous rise in RDW may indicate uncontrolled systemic inflammation or persistent microcirculatory dysfunction and tissue hypoxia, thereby potentially exacerbating neurological injury and precipitating multi-organ failure[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, the higher incidence of AKI (71.09%) and need for CRRT (25.00%) in Class 3 patients suggest that worsening renal function\u0026mdash;leading to reduced EPO synthesis and accumulation of uremic toxins\u0026mdash;may further drive the vicious cycle of rising RDW[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eComparison with Previous Studies\u003c/h2\u003e \u003cp\u003eNumerous studies have validated the prognostic value of baseline RDW in sepsis. For instance, Zhang et al. utilizing the MIMIC-IV database demonstrated that elevated RDW at admission is an independent predictor of both short- and long-term all-cause mortality in critically ill patients with obstructive sleep apnea (OSA)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, such studies are limited by their reliance on single-timepoint measurements, which cannot reflect dynamic changes during treatment. Our study demonstrates that while the highest baseline RDW tertile (T3) was associated with mortality (HR\u0026thinsp;=\u0026thinsp;1.96), the persistently-rising trajectory (Class 3) conferred a substantially higher risk (HR\u0026thinsp;=\u0026thinsp;3.76). This suggests that monitoring the trend of RDW offers superior prognostic discrimination compared to admission values alone. This finding parallels observations by Huang et al. in acute myocardial infarction, where dynamic worsening of RDW was a stronger predictor of adverse cardiovascular events than baseline levels[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, by focusing specifically on the SAD subgroup\u0026mdash;a population with inherently high mortality and complex pathophysiology\u0026mdash;our study fills a critical gap in the literature regarding longitudinal biomarkers in neuro-critical care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications\u003c/h2\u003e \u003cp\u003eOur findings hold significant potential for clinical translation. Firstly, RDW is a routine, low-cost, and readily available parameter in complete blood counts. Identifying a \"persistently-rising\" trajectory could provide clinicians with an early warning signal, prompting more aggressive anti-inflammatory strategies, intensified organ function monitoring, or early rehabilitation interventions for these high-risk patients[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Secondly, for patients exhibiting a continuous rise in RDW, clinicians should maintain a high index of suspicion for uncontrolled infection sources, persistent microcirculatory disturbances, or severe metabolic/nutritional derangements, and adjust therapeutic strategies accordingly[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Finally, integrating RDW trajectories into existing prognostic scoring systems (e.g., SOFA, APACHE II) may enhance the accuracy of mortality prediction in SAD patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThe strengths of this study include a large sample size (n\u0026thinsp;=\u0026thinsp;1,924), the use of an authoritative database (MIMIC-IV), and the application of advanced GBTM to identify heterogeneous trajectories. Moreover, we conducted comprehensive sensitivity analyses, including the exclusion of transfused patients\u0026mdash;a known confounder for RDW levels\u0026mdash;with consistent results, thereby strengthening the credibility of our conclusions. The study also adjusted for a wide range of confounders, including detailed comorbidities, laboratory parameters, and critical care interventions.\u003c/p\u003e \u003cp\u003eHowever, several limitations must be acknowledged. First, given the retrospective observational design, we can ascertain association but not causality between RDW trajectories and mortality. Second, despite adjusting for transfusion, we cannot entirely rule out the influence of other unmeasured confounders, such as specific nutritional support protocols or antibiotic regimens. Third, as MIMIC-IV data originate primarily from a single medical center in the United States, the generalizability of our findings requires validation in cohorts with diverse racial backgrounds and healthcare systems[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Fourth, delirium diagnosis relied on CAM-ICU documentation, which may be subject to under-diagnosis or assessment bias, although we employed strict inclusion criteria to minimize such biases[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Future prospective, multicenter studies should explore whether interventions targeting the rising RDW trajectory (e.g., via anti-inflammatory or microcirculation-improving therapies) can improve clinical outcomes in SAD patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, longitudinal dynamic changes in RDW are powerful independent predictors of prognosis in patients with SAD. Specifically, a persistently rising RDW pattern signifies an extremely high risk of mortality. Clinicians should incorporate dynamic RDW monitoring into the routine management of SAD patients, combining it with other clinical indicators for comprehensive risk assessment to potentially improve survival outcomes in this vulnerable population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MIMIC-IV database, which de-identifies patient records, has been approved by the institutional ethics review boards of both MIT and the Beth Israel Deaconess Medical Center and is exempt from the requirement for informed consent. Theinformation of all patients was anonymized prior to extraction and data analysis,thereby waiving the requirement for individual patient consent. This study adhered tothe principles outlined in the Declaration of Helsinki. Shuyang Dai was authorized to access the database after completing online training and assessments, as evidenced by certification number 14012091.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe availability of data and materials can be obtained by reaching out to the corresponding author via email at [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShuyang Dai, Bingjie Li and Zongshan Zhang: original draft, investigation, formal analysis, and data curation.\u003c/p\u003e\n\u003cp\u003eGaoli Zhang: Methodology, Formal analysis, Data curation.\u003c/p\u003e\n\u003cp\u003ePoshi Xu: original draft, methodology, formal analysis, data curation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the MIMIC-IV database team for providing the clinical data required for this study, and the Biostatistics Research Center of Fuwai Central China Cardiovascular Hospital for the guidance on statistical modeling and analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. 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Medicina (Kaunas). 2026;62:346. https://doi.org/10.3390/medicina62020346.\u003c/li\u003e\n \u003cli\u003eSilverberg DS, Wexler D, Sheps D, Blum M, Keren G, Baruch R, et al. The effect of correction of mild anemia in severe, resistant congestive heart failure using subcutaneous erythropoietin and intravenous iron: a randomized controlled study. J Am Coll Cardiol. 2001;37:1775\u0026ndash;80. https://doi.org/10.1016/s0735-1097(01)01248-7.\u003c/li\u003e\n \u003cli\u003eHuang Y-L, Hu Z-D, Liu S-J, Sun Y, Qin Q, Qin B-D, et al. Prognostic value of red blood cell distribution width for patients with heart failure: a systematic review and meta-analysis of cohort studies. PLoS One. 2014;9:e104861. https://doi.org/10.1371/journal.pone.0104861.\u003c/li\u003e\n \u003cli\u003eDevlin JW, Skrobik Y, G\u0026eacute;linas C, Needham DM, Slooter AJC, Pandharipande PP, et al. Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU. Crit Care Med. 2018;46:e825\u0026ndash;73. https://doi.org/10.1097/CCM.0000000000003299.\u003c/li\u003e\n \u003cli\u003eMarik PE. The demise of early goal-directed therapy for severe sepsis and septic shock. Acta Anaesthesiol Scand. 2015;59:561\u0026ndash;7. https://doi.org/10.1111/aas.12479.\u003c/li\u003e\n \u003cli\u003eJohnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023;10:1. https://doi.org/10.1038/s41597-022-01899-x.\u003c/li\u003e\n \u003cli\u003eEly EW, Inouye SK, Bernard GR, Gordon S, Francis J, May L, et al. Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the intensive care unit (CAM-ICU). JAMA. 2001;286:2703\u0026ndash;10. https://doi.org/10.1001/jama.286.21.2703.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sepsis-associated delirium, Red blood cell distribution width, Longitudinal trajectory, Mortality, MIMIC-IV, Critical care","lastPublishedDoi":"10.21203/rs.3.rs-9080605/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9080605/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSepsis-associated delirium (SAD) is a frequent complication in the intensive care unit (ICU) associated with poor prognosis. While red blood cell distribution width (RDW) is a potential biomarker reflecting inflammation and oxidative stress, its longitudinal dynamic patterns and prognostic value specifically within the SAD population remain elusive.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included adult patients diagnosed with SAD from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (2008\u0026ndash;2022). Group-based trajectory modeling (GBTM) was employed to identify distinct longitudinal patterns of RDW during the first 5 days of ICU admission. The primary outcome was 30-day all-cause mortality, and the secondary outcome was 365-day all-cause mortality. Multivariable Cox proportional hazards models were used to evaluate the association between RDW trajectories and mortality risk, adjusting for demographics, disease severity, comorbidities, and therapeutic interventions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1,924 SAD patients (median age: 66.0 years; 59.25% male) were included, with a 30-day mortality rate of 19.59%. GBTM identified three distinct RDW trajectories: \"Stable-Low\" (Class 1, 61.4%), \"Slowly-Rising\" (Class 2, 31.9%), and \"Persistently-Rising\" (Class 3, 6.7%). Compared to the Stable-Low group, patients in the Persistently-Rising group exhibited the highest baseline organ failure scores (median SOFA: 10 vs. 6) and most severe anemia (median hemoglobin: 8.5 g/dL vs. 11.4 g/dL). After full adjustment (Model 3), the Persistently-Rising trajectory was independently associated with a significantly increased risk of 30-day mortality (HR\u0026thinsp;=\u0026thinsp;3.765, 95% CI: 2.727\u0026ndash;5.199, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 365-day mortality (HR\u0026thinsp;=\u0026thinsp;3.635, 95% CI: 2.664\u0026ndash;4.960, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Restricted cubic spline analysis revealed a linear positive correlation between baseline RDW and mortality risk. Subgroup analyses and sensitivity analyses excluding transfused patients confirmed the robustness of these associations.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eLongitudinal RDW trajectories are independent predictors of short- and long-term mortality in patients with SAD. Specifically, a persistently rising RDW pattern indicates an extremely high risk of death and may serve as a valuable metric for early risk stratification and personalized intervention.\u003c/p\u003e","manuscriptTitle":"Dynamic RDW Trajectories Predict Mortality in Sepsis-Associated Delirium: A Group-Based Trajectory Modeling Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 06:22:43","doi":"10.21203/rs.3.rs-9080605/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-29T15:57:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-20T18:49:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T18:48:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-18T23:48:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2026-03-16T09:22:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37c6356e-2be5-4449-b35e-6ab2bf48dfac","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T06:22:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 06:22:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9080605","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9080605","identity":"rs-9080605","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}