Prognostic value of hemoglobin to red cell distribution width ratio in patients with pulmonary embolism

Prognostic value of hemoglobin to red cell distribution width ratio in patients with pulmonary embolism | 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 Prognostic value of hemoglobin to red cell distribution width ratio in patients with pulmonary embolism Jian Liao, Dingyu Lu, Maojuan Wang, Wei Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6888191/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 his study investigates the prognostic value of the hemoglobin to red cell distribution width ratio (HRR) in pulmonary embolism (PE), a life-threatening cardiovascular disease. While inflammation and hypercoagulability drive PE pathogenesis, the role of HRR remains unexplored. Methods In this retrospective cohort study, data from 1,658 critically ill PE patients (2008–2022) were extracted from the MIMIC-IV database. Patients were stratified by HRR quartiles (Q1–Q4). COX proportional hazards regression analysis, Kaplan- Meier survival curves and restricted cubic spline models were employed to investigate the association of RDW and HRR levels with mortality. Time-dependent receiver operating characteristic curve (ROC) analysis was conducted to evaluate the accuracy of RDW and HRR in predicting mortality in patients with PE. Results Patients with a poor prognosis and mortality had significantly lower HRR levels at admission. When HHR was considered as a continuous variable, HRR was inversely associated with 28-day mortality (HR = 0.44, 95% CI = 0.22–0.86, p < 0.017) and 90-day mortality (HR = 0.29, 95% CI = 0.16–0.52, p < 0.001) after adjusting for various potential confounders. The Kaplan-Meier survival curve showed that the survival rate for 28-day increased for the higher HRR groups compared to the lower HRR groups (log-rank test p < 0.001). Moreover, the 90-day survival curve demonstrated similar results. Receiver-operating characteristic curve analysis demonstrated that HRR appears to be a more reliable predictor for both 28-day mortality ( The AUC is 0.610) and 90-day mortality ( The AUC is 0.641) than RDW and hemoglobin. Conclusions HRR levels as a simple, novel, cost-effective, and valuable biomarker, are an independent predictor of poor prognosis for patients with pulmonary embolism. However, further research is necessary to elucidate the underlying biological mechanisms and confirm the clinical utility of HRR. pulmonary embolism red cell distribution width hemoglobin to red cell distribution width ratio (HRR) mortality MIMIC- IV Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Pulmonary embolism (PE) is a common and potentially life-threatening cardiovascular disease [ 1 ], characterized by an obstruction in the pulmonary artery due to a clot, tumor, air or fat [ 2 ]. Deep vein thrombosis (DVT) constitutes the primary pathophysiological origin of pulmonary thromboembolism (PTE). DVT typically develops in the deep venous systems of the lower extremities or pelvic vasculature. Subsequent embolization through venous return transports dislodged thrombi into the pulmonary arterial circulation and its branches [ 3 ]. PE affects between 39 and 115 out of every 100,000 people each year, which greatly impacts medical care, hospital stay lengths, and mortality rates [ 4 ]. Despite advances in diagnostic techniques and treatments, PE remains a significant cause of morbidity and mortality worldwide. Early identification of high-risk patients and accurate prognosis assessment are crucial for improving clinical outcomes [ 5 ]. Recently, emerging evidence has suggested that inflammation and hypercoagulability play important roles in the pathogenesis and progression of PE [ 6 ]. Hemoglobin (Hb), a critical hematologic parameter in complete blood count (CBC) profiles, serves as a biomarker reflecting nutritional reserves (particularly iron metabolism and protein synthesis) and modulates immune competence through oxygen-dependent cellular functions [ 7 ]. The red blood cell distribution width (RDW) is a readily available clinical indicator that reflects the heterogeneity in red blood cell size [ 8 ]. It has been documented that an elevated RDW may stem from various factors such as inflammation, oxidative stress, nutritional deficiencies, and renal dysfunction [ 9 , 10 ]. Hemoglobin and red cell RDW are not only reflect the balance between hematopoietic function and red blood cell survival but also play a critical role in inflammation, oxidative stress, and the vascular innate immune system [ 11 , 12 ]. The Hb/RDW ratio (HRR), a novel biomarker integrating two hematologic parameters, demonstrates greater specificity and sensitivity than either Hb or RDW alone [ 13 ]. This enables it to more accurately reflect patients' inflammation levels and better evaluate its correlation with disease progression across multiple conditions, such as stroke [ 14 ], acute decompensated heart failure [ 15 ], ischemic stroke [ 16 ] and multiple cancers [ 17 – 20 ]. In patients with pulmonary embolism (PE), thrombus formation augments inflammatory responses, while excessive inflammation reciprocally promotes thrombosis. This bidirectional pathological interplay primarily stems from the activation of leukocytes, platelets, and endothelial cells [ 21 ]. However, the relationship between HRR and mortality in patients with PE remains unclear. Additionally, it is also unclear whether HRR outperforms RDW as a predictor of mortality in this population. Therefore, our primary objective is to evaluate the association of both RDW and HRR with mortality in PE patients and to compare their predictive performance. Methods Ethical Statement and Data Source This study utilized de-identified patient data extracted from Medical Information Mart for Intensive Care IV (MIMIC-IV v3.0), a publicly accessible critical care database jointly managed by Beth Israel Deaconess Medical Center (BIDMC) and Massachusetts Institute of Technology (MIT). All included patients were tracked for up to 1 year post-discharge. The institutional review boards (IRBs) of both BIDMC and MIT granted full approval for establishing and maintaining the MIMIC-IV database. Because the data is publicly available, both the statement regarding ethical approval and the necessity for informed consent were waived for this investigation. The research protocol adhered rigorously to the Declaration of Helsinki. The research excluded individuals below the age of 18 during their initial admission, individuals who experienced multiple admissions to the ICU due to PE (only data from the first admission were considered). We excluded patients who met the following criteria: less than 18 years (n = 2), length of ICU stay < 24h (n = 941). The vital signs and laboratory variables were collected only within the first 24 hours after the patient’s admission. When multiple outcomes were present, the mean value was utilized. Finally, a total of 1658 patients formed the final study cohort and were divided into four groups based on the quartiles of the HHR observed on their first day in the ICU. Data collection To conduct the data extraction, we utilized PostgresSQL software and pgAdmin 4 tool by employing Structured Query Language (SQL). The extraction process prioritized four distinct categories of potential variables: demographic factors, vital signs, laboratory tests, severity of illness score, comorbidities and treatment during ICU stay. All vital signs and laboratory tests data were measured for the first time within 24 hours of ICU admission. Outcomes The main outcome of this study was 28-day mortality after ICU admission. Secondary outcomes focused on 90-day mortality in hospital. Statistical analysis The patients were categorized into five groups based on their admission HRR levels: Quartile 1 (0.178 ≤ HRR < 0.548 ), Quartile 2 (0.548 ≤ HRR < 0.707), Quartile 3 (0.707 ≤ HRR < 0.88) and Quartile 4 (0.88 ≤ HRR ≤ 1.33). Continuous variables were presented as the mean ± SD or median and interquartile range (IQR). Categorical variables were expressed as numbers or percentages (%). ANOVA analysis, the Kruskal-Wallis test for continuous variables, or the chi-square test for categorical data, as applicable. Kaplan-Meier survival analysis was used to assess the incidence rate of primary outcome events in different stratified groups based on the HRR level. The log-rank test was employed to examine any observed disparities. Binary logistic regression analysis was conducted to evaluate factors influencing the risk of all-cause mortality. Cox regression analysis was conducted to investigate the relationship between RDW, HRR and 28-day mortality. The selection of confounders was based on clinical relevance, existing literature, and all signifcant covariates identifed in the univariate analysis. Model 1: unadjusted; Model 2 was adjusted for age, gender, BMI, smoke, atrial fibrillation, phlebothrombosis, pulmonary hypertension, sepsis, hypertension, diabetes, heart failure, acute myocardial infarction (AMI), acute kidney injury(AKI), vasoactive drugs, anticoagulant drugs, antiplatelet drugs, white blood cell (WBC), platelet, albumin, sodium, potassium, calciumtotal, glucose, creatinine, Sequential Organ Failure Assessment (SOFA), Acute Physiology Score III (APSIII), Oxford acute severity of illness score (OASIS), Charlson, continuous renal replacement therapy (CRRT), non-invasive ventilation, invasive ventilation. HRs were counted and the findings were presented with 95% confidence intervals (CI). The lowest quartile of the HHR was used as the baseline group in all four models. We also conducted an analysis to examine the linear relationship between the baseline HRR and the risk of mortality. This analysis was done using a restricted cubic spline regression model with five knots. To evaluate the dynamic prognostic performance of HRR, RDW and hemoglobin across temporal dimensions, we implemented time-dependent receiver operating characteristic (ROC) analysis using the timeROC package. The data analyses were conducted using R software (R version 4.2.2, R Foundation for Statistical Computing). For all analyses, a two-side P < 0.05 was considered statistically significant. Results Baseline characteristics The study enrolled a total of 1658 critically ill patients with PE from the MIMIC-IV database, as depicted in Fig. 1 . The average age of the patients was 58 ± 15 years, with males comprising 53.2% of the sample, the 28-day mortality rate was 13.8%, and the 90-day mortality rate was 19.6%. The median hospital length of stay was 11 days (IQR 6–21), with an ICU length of stay was 3 days (IQR 2–7). Patients in the Quartiles 4 demonstrated highest hemoglobin levels, lowest RDW values, the highest HRR levels and shortest hospital length of stay. However, no statistically differences were observed among the four groups in ICU length of stay. The increase in HHR was associated with decreased rate of 28-day mortality (18.1% vs. 13.5% vs. 11.8% vs. 11.6%, p < 0.001) and 90-day mortality (28.4% vs. 19.1% vs. 16.4% vs. 14.5%, p < 0.001) (Table 1 ). Primary outcomes Survival analysis The Kaplan-Meier survival curve showed that the survival rate for 28-day increased for the higher HRR groups compared to the lower HRR groups (log-rank test p < 0.001). Moreover, the 90-day survival curve demonstrated similar results (Fig. 2 ). Table 1 Baseline characteristics of participants stratified by HHR quartile levels Variable Overall Quartiles 1 Quartiles 2 Quartiles 3 Quartiles 4 P N = 1658 0.178 ≤ HRR < 0.548 N = 415 0.548 ≤ HRR < 0.707 N = 414 0.707 ≤ HRR < 0.88 N = 414 0.88 ≤ HRR ≤ 1.33 N = 415 Demographic Age, years 58 ± 15 58 ± 14 59 ± 15 59 ± 14 57 ± 15 0.104 Male, n(%) 882 (53.2%) 180 (43.4%) 207 (50.0%) 220 (53.1%) 275 (66.3%) < 0.001 BMI 32 ± 11 31 ± 11 31 ± 9 33 ± 11 33 ± 11 < 0.001 Vital signs Temperature, ℃ 36.86 ± 1.61 36.91 ± 0.67 36.81 ± 1.82 36.95 ± 0.69 36.75 ± 2.48 0.279 HR, beats/min 95 ± 22 99 ± 22 95 ± 21 94 ± 22 94 ± 22 0.010 SBP, mmHg 121 ± 22 117 ± 22 117 ± 21 122 ± 22 127 ± 21 < 0.001 DBP, mmHg 115 ± 1,727 68 ± 18 69 ± 16 72 ± 16 249 ± 3,460 0.347 RR, beats/min 21 ± 7 21 ± 7 21 ± 7 21 ± 7 21 ± 6 0.801 SPO2, % 96.3 ± 4.0 96.7 ± 3.5 96.4 ± 4.7 96.1 ± 4.0 96.1 ± 3.9 0.166 Laboratory tests WBC, K/uL 11 (8, 16) 11 (7, 17) 12 (8, 16) 11 (8, 16) 12 (9, 16) 0.312 Platelet, K/uL 222 ± 120 224 ± 144 234 ± 139 212 ± 98 219 ± 86 0.068 Hemoglobin, g/dL 10.65 ± 2.35 8.05 ± 1.19 9.72 ± 1.13 11.43 ± 1.18 13.41 ± 1.46 < 0.001 RDW, % 15.37 ± 2.46 18.08 ± 2.54 15.55 ± 1.63 14.44 ± 1.40 13.43 ± 1.04 < 0.001 HRR 0.72 ± 0.21 0.45 ± 0.07 0.63 ± 0.05 0.79 ± 0.05 1.00 ± 0.10 < 0.001 Albumin, g/dL 2.85 ± 0.51 2.69 ± 0.57 2.78 ± 0.46 2.90 ± 0.44 3.04 ± 0.51 < 0.001 Sodium, mmol/L 138.1 ± 5.4 137.1 ± 5.4 137.7 ± 6.0 138.7 ± 5.1 138.9 ± 4.7 < 0.001 Potassium, mmol/L 4.20 ± 0.73 4.21 ± 0.75 4.21 ± 0.75 4.12 ± 0.68 4.23 ± 0.75 0.107 Calciumtotal, mmol/L 8.31 ± 0.82 8.10 ± 0.91 8.24 ± 0.82 8.37 ± 0.80 8.52 ± 0.70 < 0.001 Glucose, mg/dL 126 (105, 163) 121 (101, 159) 124 (104, 165) 132 (109, 171) 128 (106, 161) 0.001 Creatinine, mg/dL 0.90 (0.70, 1.40) 1.00 (0.70, 1.80) 0.90 (0.70, 1.50) 0.90 (0.70, 1.20) 0.90 (0.70, 1.20) 0.001 Severity of illness score SOFA 4.7 ± 3.7 5.4 ± 3.8 4.9 ± 3.6 4.4 ± 3.7 3.9 ± 3.5 < 0.001 APSIII 45 ± 21 51 ± 21 46 ± 20 44 ± 21 40 ± 22 < 0.001 OASIS 31 ± 9 32 ± 8 31 ± 8 31 ± 9 31 ± 9 0.344 Charlson 4.06 ± 2.79 4.75 ± 2.94 4.43 ± 2.84 3.82 ± 2.67 3.23 ± 2.43 < 0.001 Comorbidities Smoke, n(%) 310 (18.7%) 73 (17.6%) 79 (19.1%) 87 (21.0%) 71 (17.1%) 0.471 Atrial fibrillation, n(%) 362 (21.8%) 91 (21.9%) 101 (24.4%) 86 (20.8%) 84 (20.2%) 0.478 Phlebothrombosis, n(%) 387 (23.3%) 110 (26.5%) 104 (25.1%) 89 (21.5%) 84 (20.2%) 0.108 Pulmonary hypertension, n(%) 106 (6.4%) 24 (5.8%) 31 (7.5%) 32 (7.7%) 19 (4.6%) 0.203 Sepsis, n(%) 942 (56.8%) 258 (62.2%) 251 (60.6%) 223 (53.9%) 210 (50.6%) 0.002 Hypertension, n(%) 587 (35.4%) 131 (31.6%) 128 (30.9%) 166 (40.1%) 162 (39.0%) 0.005 Diabetes, n(%) 420 (25.3%) 120 (28.9%) 114 (27.5%) 107 (25.8%) 79 (19.0%) 0.005 Heart failure, n(%) 414 (25.0%) 114 (27.5%) 123 (29.7%) 109 (26.3%) 68 (16.4%) < 0.001 AMI, n(%) 112 (6.8%) 32 (7.7%) 35 (8.5%) 21 (5.1%) 24 (5.8%) 0.173 AKI, n(%) 1,300 (78.4%) 326 (78.6%) 319 (77.1%) 324 (78.3%) 331 (79.8%) 0.824 Treatment during hospitalization Vasoactive drugs, n(%) 441 (26.6%) 107 (25.8%) 108 (26.1%) 109 (26.3%) 117 (28.2%) 0.861 Anticoagulant drugs, n(%) 722 (43.5%) 182 (43.9%) 167 (40.3%) 184 (44.4%) 189 (45.5%) 0.465 Antiplatelet drugs, n(%) 447 (27.0%) 122 (29.4%) 103 (24.9%) 122 (29.5%) 100 (24.1%) 0.157 CRRT, n(%) 98 (5.9%) 40 (9.6%) 28 (6.8%) 12 (2.9%) 18 (4.3%) < 0.001 Non-invasive ventilation, n(%) 1,193 (72.0%) 292 (70.4%) 296 (71.5%) 307 (74.2%) 298 (71.8%) 0.668 Invasive ventilation, n(%) 660 (39.8%) 141 (34.0%) 170 (41.1%) 177 (42.8%) 172 (41.4%) 0.043 Outcomes 28-day mortality, n(%) 228 (13.8%) 75 (18.1%) 56 (13.5%) 49 (11.8%) 48 (11.6%) 0.023 90-day mortality, n(%) 325 (19.6%) 118 (28.4%) 79 (19.1%) 68 (16.4%) 60 (14.5%) < 0.001 LOS in hospital, days 11 (6, 21) 14 (7, 25) 11 (7, 19) 11 (7, 19) 9 (5, 20) < 0.001 LOS in ICU, days 3 (2, 7) 3 (2, 6) 3 (2, 6) 3 (2, 7) 3 (2, 8) 0.064 BMI, body mass index; HR, heart rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; RR, respiratory rate; WBC, white blood cell; RDW, red cell distribution width; HRR, hemoglobin-to-red blood cell distribution width ratio; AMI, acute myocardial infarction; AKI, acute kidney injury; CRRT, continuous renal replacement therapy; LOS in hospital, length of stay in hospital; LOS in ICU, length of stay in ICU Associations of HRR and RDW with the risk of 28-day mortality In the multivariable Cox proportional hazard model, RDW showed a positive association with increased risk of 28-day mortality (HR = 1.15, 95% CI = 1.09–1.20, p < 0.001) when expressed as a continuous variable in modle 1 (no adjusted), and the risk of mortality was elevated by 10% (HR = 1.10, 95% CI = 1.04–1.16, p < 0.001) in model 2 after adjusting for potential confounders. When HHR was considered as a continuous variable, HRR was inversely associated with the risk of 28-day mortality (HR = 0.44, 95% CI = 0.22–0.86, p = 0.017) after adjustment. When HHR was treated as a nominal variable, patients in the highest quartile group had a 0.64-fold lower risk of 28-day mortality (HR = 0.64, 95% CI = 0.42–0.96, p = 0.032) than those in the lowest quartile group after adjusting for potential confounders (Table 2 ). Associations of HRR and RDW with the risk of 90-day mortality The results showed that RDW also had a positive association with increased risk of 90-day mortality (HR = 1.13, 95% CI = 1.08–1.18, p < 0.001) when expressed as a continuous variable in modle 2 after adjusting for potential confounders. HRR was also inversely associated with the risk of 90-day mortality (HR = 0.29, 95% CI = 0.16–0.52, p < 0.001) in modle 2 when HHR was considered as a continuous variable. When HHR was treated as a nominal variable, patients in the highest quartile group had a 0.53-fold lower risk of 90-day mortality (HR = 0.53, 95% CI = 0.37–0.75, p < 0.001) than those in the lowest quartile group after adjusting for potential confounders (Table 3 ). Tables 2 and Table 3 demonstrated that higher HRR level was associated with progressively reduced mortality risks at both 28-day and 90-day follow-up intervals. Table 2 The associations of RDW and HRR with the risk of 28-day mortality Variables Model 1 Model 2 HR (95%CI) P HR (95%CI) P RDW per 1 unit increment 1.15 (1.09 ~ 1.20) < .001 1.10 (1.04 ~ 1.16) < .001 HRR per 1 unit increment 0.27 (0.14 ~ 0.49) < .001 0.44 (0.22 ~ 0.86) 0.017 Quartiles Q1(0.178 ≤ HRR < 0.548) 1.00 (Reference) 1.00 (Reference) Q2( 0.548 ≤ HRR < 0.707) 0.74 (0.52 ~ 1.05) 0.089 0.76 (0.53 ~ 1.09) 0.132 Q3(0.707 ≤ HRR < 0.88) 0.55 (0.39 ~ 0.80) 0.001 0.66 (0.45 ~ 0.96) 0.030 Q4(0.88 ≤ HRR ≤ 1.33) 0.49 (0.34 ~ 0.71) < .001 0.64 (0.42 ~ 0.96) 0.032 HR: Hazard Ratio, CI: Confidence Interval Model 1: no adjusted Model 2: adjusted for Age, Gender, BMI, Smoke, Atrial fibrillation, Phlebothrombosis, Pulmonary hypertension, Sepsis, Hypertension, Diabetes, Heart failure, AMI, AKI, Vasoactive drugs, Anticoagulant drugs, Antiplatelet drugs, WBC, Platelet, Albumin, Sodium, Potassium, Calciumtotal, Glucose, Creatinine, SOFA, APSIII, OASIS, Charlson, CRRT, Non-invasive ventilation, Invasive ventilation Table 3 The associations of RDW and HRR with the risk of 90-day mortality Variables Model 1 Model 2 HR (95%CI) P HR (95%CI) P RDW per 1 unit increment 1.17 (1.13 ~ 1.22) < .001 1.13 (1.08 ~ 1.18) < .001 HRR per 1 unit increment 0.18 (0.11 ~ 0.30) < .001 0.29 (0.16 ~ 0.52) < .001 Quartiles Q1(0.178 ≤ HRR < 0.548) 1.00 (Reference) 1.00 (Reference) Q2( 0.548 ≤ HRR < 0.707) 0.68 (0.51 ~ 0.91) 0.009 0.71 (0.53 ~ 0.95) 0.023 Q3(0.707 ≤ HRR < 0.88) 0.50 (0.37 ~ 0.67) < .001 0.59 (0.43 ~ 0.80) < .001 Q4(0.88 ≤ HRR ≤ 1.33) 0.40 (0.29 ~ 0.54) < .001 0.53 (0.37 ~ 0.75) < .001 HR: Hazard Ratio, CI: Confidence Interval Model 1: no adjusted Model 2: adjusted for Age, Gender, BMI, Smoke, Atrial fibrillation, Phlebothrombosis, Pulmonary hypertension, Sepsis, Hypertension, Diabetes, Heart failure, AMI, AKI, Vasoactive drugs, Anticoagulant drugs, Antiplatelet drugs, WBC, Platelet, Albumin, Sodium, Potassium, Calciumtotal, Glucose, Creatinine, SOFA, APSIII, OASIS, Charlson, CRRT, Non-invasive ventilation, Invasive ventilation The linear relationship between the HRR and the risk of mortality The study employed a restricted cubic splines regression model to investigate the relationship between HRR level and the risk of mortality. After adjusting for confounders in model 2, we found that there was a linear relationship between HRR and 28-day mortality (p for non-linearity = 0.843) and 90-day mortality (p for non-linearity = 0.666) in patients with PE (Fig. 3 ). Predictive value of HHR for 28-day and 90-day mortality ROC curves were performed to evaluate the predictive value of HRR, RDW and hemoglobin for mortality in patients with PE. The AUC of HRR, RDW and hemoglobin were 0.610, 0.558 and 0.513, respectively. Meanwhile, the AUC of HRR, RDW and hemoglobin were 0.641, 0.595 and 0.542, respectively. The results indicate that HRR appears to be a more reliable predictor, providing a more accurate assessment of both 28-day mortality and 90-day mortality (Fig. 4 ). Discussion In this large retrospective cohort study using MIMIC-IV database from 2008 to 2022, we clearly revealed that HRR was independently associated with an increased risk of 28-day mortality and 90-day mortality in patients with PE. In our investigation, an linear correlation was identified between the HRR and mortality during hospitalization. Moreover, both RDW and HRR are effective predictors of all-cause mortality across different follow-up periods. However, HRR appears to exhibit superior predictive value compared to RDW. This indicates that the HRR has the potential to be a valuable tool in identifying individuals with a high risk of mortality in patients with PE. HRR is calculated using hemoglobin and red blood cell distribution width, which reflect anemia and red blood cell heterogeneity [ 13 ]. Anemia, indicated by low Hb, is linked to poor outcomes in patients with PE. It reduces oxygen-carrying capacity, exacerbating hypoxia and potentially leading to multiple organ dysfunction [ 22 ]. RDW, a marker of red blood cell size variation, is associated with inflammation and oxidative stress. Elevated RDW levels may signify systemic inflammation, which plays a pivotal role in PE by promoting thrombogenesis and vascular damage [ 23 ]. HRR integrates Hb and RDW, offering a comprehensive assessment of a patient’s inflammatory and nutritional status. A low HRR suggests either low Hb, high RDW, or both, indicating anemia, red blood cell heterogeneity, and underlying inflammation. In patients with PE, inflammation is a key driver of thrombus formation and complications [ 6 ]. The inflammatory response triggers the release of cytokines and adhesion molecules, promoting platelet activation and coagulation, thereby worsening PE [ 9 , 10 , 24 ]. Oxidative stress, often accompanying inflammation, can damage vascular endothelial cells, further disrupting hemostasis and aggravating PE [ 25 ]. Recent studies have confirmed that higher HRR was positively associated with a decreased risk of all-cause mortality in patients with different diseases, such as stroke,acute decompensated heart failure, ischemic stroke [ 14 – 16 ]. Huang et al. conducted a large retrospective cohort, which included a total of 8,853 critically ill patients with Sepsis-Associated Encephalopathy, and found a linear relationship between all-cause mortality and HRR in patients with SAE, with low HRR being inversely associated with increased all-cause mortality in patients with SAE [ 26 ]. However, there are currently no studies investigating its relationship with PE. Building upon these findings, our study focused on patients with PE and found that when the HHR was considered as a continuous variable, HRR was inversely associated with the risk of 28-day mortality (HR = 0.44, 95% CI = 0.22–0.86, p = 0.017) and 90-day mortality (HR = 0.29, 95% CI = 0.16–0.52, p < 0.001). The Hemoglobin to RDW Ratio (HRR), initially proposed by Sun .et al as a novel biomarker demonstrating prognostic value in malignancies, exhibits significant associations with survival outcomes in esophageal squamous cell carcinoma (ESCC) patients [ 27 ]. Reductions in HRR result from either diminished Hb levels or elevated RDW values. Consequently, patients with lower HRR ratios clinically manifest as increased mortality risk and poorer prognosis, reflecting underlying systemic inflammation and nutritional depletion [ 28 ]. RDW is a laboratory index used in the differential diagnosis of anemia. It is a simple laboratory test that evaluates the variability in the size and form of red blood cells. Recently, there have been several studies linking high RDW to increased mortality in various medical conditions such as coronary disease [ 29 , 30 ], heart failure [ 31 ], pulmonary hypertension [ 32 ], pulmonary embolism [ 33 ] and sepsis induced cardiomyopathy [ 34 ]. Our study showed that HRR is linearly and negatively linked to 28-day and 90-day mortality. Higher HRR means lower risk of mortality. ROC curve analysis indicated that HRR is a better predictor of clinical outcomes in patients with PE than RDW and Hb. It may serve as a strong independent predictor for stratifying the risk of in-hospital and ICU mortality. The possible mechanism is that the inflammatory response triggers the secretion of large amounts of inflammatory cytokines, which damage the red blood cell membrane and reduce the deformability of red blood cells [ 35 ]. Chronic hypoxia causes peroxides to accumulate in the lungs, leading to cell apoptosis and disrupting the ventilation-perfusion ratio [ 36 ]. This triggers an inflammatory response, prompting the production of inflammatory mediators. These mediators act on vascular endothelial and smooth muscle cells, causing pulmonary vasoconstriction and vascular remodeling [ 37 ]. As red blood cells pass through constricted vessels, they are damaged, increasing red blood cell heterogeneity. Additionally, inflammatory factors and oxidative stress stimulate erythropoiesis, alter the half-life of red blood cells, and reduce their lifespan. This results in the premature release of immature red blood cells into the bloodstream [ 38 ]. Increased red blood cell heterogeneity can raise blood viscosity, slow blood flow, and prolong cell-vessel wall contact time. This leads to platelet activation and fibrinogen action, promoting thrombosis [ 39 ]. This study utilized a large, publicly available critical-care database to assess the relationship between HRR and mortality risk in PE patients. However, the study has limitations. First, its retrospective cohort design may introduce selection bias, as researchers relied on pre-existing data and records, which can contain errors. Second, despite matching participants and controlling for variables, unadjusted confounders could still affect the results. Finally, the study did not evaluate long-term patient outcomes. Thus, caution is needed when using these findings to predict long-term prognosis. Conclusions Our study reveals that HRR levels serve as a simple,novel, cost - effective and valuable biomarker, which is an independent indicator of unfavorable outcomes for patients with pulmonary embolism. However, further research is needed to clarify the underlying biological mechanisms and establish the clinical utility of HRR. Abbreviations HRR Hemoglobin to red cell distribution width ratio PE Pulmonary embolism DVT Deep vein thrombosis PTE Pulmonary thromboembolism BMI Body mass index HR Heart rate SBP Systolic blood pressure DBP Diastolic blood pressure RR Respiratory rate WBC White blood cell RDW Red cell distribution width AMI Acute myocardial infarction AKI Acute kidney injury CRRT Continuous renal replacement therapy LOS in hospital Length of stay in hospital LOS in ICU Length of stay in ICU ROC receiver operating characteristic curve Declarations Ethics approval and consent to participate Because the data is publicly available, both the statement regarding ethical approval and the necessity for informed consent were waived for this investigation. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article [and its supplementary information files. Competing Interests The authors declare that they have no competing interests. Funding This research received no external funding. Authors' contributions Conception and design: .JL., D.L. Collection and assembly of data: DL.,LZ. Data analysis and interpretation: JL,. DL.Critical revisions and supervision: LZ,.MW. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Rali P, Gandhi V, Malik K. Pulmonary embolism. Crit Care Nurs Q. 2016; 39 (2): 131-138. Rali PM, Criner GJ. Submassive pulmonary embolism. Am J Respir Crit Care Med. 2018;198(5): 588-598. Kalaitzopoulos DR, Panagopoulos A, Samant S, et al. Management of venous thromboembolism in pregnancy. Thromb Res. 2022;211:106-113. Nguyen E, Caranfa JT, Lyman GH, et al. Clinical prediction rules for mortality in patients with pulmonary embolism and cancer to guide outpatient management: a meta- analysis. J Thromb Haemost. 2018;16(2): 279-292. Konstantinides SV, Meyer G, Becattini C, et al. 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European Respiratory Society (ERS). Eur Heart J. 2020;41(4): 543-603. Raper JD, Thomas AM, Lupez K, et al. Can right ventricular assessments improve triaging of low risk pulmonary embolism?. Acad Emerg Med. 2022;29(7):835-850. Liu J, Wang J. Association between hemoglobin-to-red blood cell distribution width ratio and hospital mortality in patients with non-traumatic subarachnoid hemorrhage. Front Neurol. 2023;14:1180912. Salvagno GL, Sanchis-Gomar F, Picanza A, Lippi G. Red blood cell distribution width: A simple parameter with multiple clinical applications. Crit Rev Clin Lab Sci. 2015;52(2):86-105. Lippi G, Turcato G, Cervellin G, Sanchis-Gomar F. Red blood cell distribution width in heart failure: A narrative review. World J Cardiol. 2018;10(2):6-14. Pernow J, Mahdi A, Yang J, Zhou Z. Red blood cell dysfunction: a new player in cardiovascular disease. Cardiovasc Res. 2019;115(11):1596-1605. Emans ME, van der Putten K, van Rooijen KL, et al. Determinants of red cell distribution width (RDW) in cardiorenal patients: RDW is not related to erythropoietin resistance. J Card Fail. 2011;17(8):626-633. Esposito E, Zhang F, Park JH, et al. Diurnal Differences in Immune Response in Brain, Blood and Spleen After Focal Cerebral Ischemia in Mice. Stroke. 2022; 53 (12): e507-e511. Liu S, Zhang H, Zhu P, Chen S, Lan Z. Predictive role of red blood cell distribution width and hemoglobin-to-red blood cell distribution width ratio for mortality in patients with COPD: evidence from NHANES 1999-2018. BMC Pulm Med. 2024;24(1):413. Eyiol A, Ertekin B. The relationship between hemoglobin-to-red cell distribution width (RDW) ratio (HRR) and mortality in stroke patients. Eur Rev Med Pharmacol Sci. 2024;28(4):1504-1512. Kanzaki Y, Minamisawa M, Motoki H, et al. Prognostic Impact of the Ratio of Hemoglobin to Red Blood Cell Distribution Width in Patients after Acute Decompensated Heart Failure. Intern Med. 2025;64(6):807-816. Feng X, Zhang Y, Li Q, Wang B, Shen J. Hemoglobin to red cell distribution width ratio as a prognostic marker for ischemic stroke after mechanical thrombectomy. Front Aging Neurosci. 2023;15:1259668. Fang Y, Sun X, Zhang L, Xu Y, Zhu W. Hemoglobin/Red Blood Cell Distribution Width Ratio in Peripheral Blood Is Positively Associated with Prognosis of Patients with Primary Hepatocellular Carcinoma. Med Sci Monit. 2022; 28: e937146. Zhou G, Yang L, Lu Y, Lu G. Prognostic value of hemoglobin to red blood cell distribution width ratio in pancreatic ductal adenocarcinoma: a retrospective study. BMC Gastroenterol. 2024;24(1):288. Wu F, Yang S, Tang X, Liu W, Chen H, Gao H. Prognostic value of baseline hemoglobin-to-red blood cell distribution width ratio in small cell lung cancer: A retrospective analysis. Thorac Cancer. 2020;11(4):888-897. Su YC, Wen SC, Li CC, et al. Low Hemoglobin-to-Red Cell Distribution Width Ratio Is Associated with Disease Progression and Poor Prognosis in Upper Tract Urothelial Carcinoma. Biomedicines. 2021;9(6):672. Imiela AM, Mikołajczyk TP, Guzik TJ, Pruszczyk P. Acute Pulmonary Embolism and Immunity in Animal Models. Arch Immunol Ther Exp (Warsz). 2024;72(1):10.2478/aite-2024-0003. Yılmaz H, Yılmaz A, Demirağ G. Prognostic significance of hemoglobin-to-red cell distribution width ratio in patients with metastatic renal cancer. Future Oncol. 2021;17(29):3853-3864. doi:10.2217/fon-2021-0040 Qin Z, Liao N, Lu X, Duan X, Zhou Q, Ge L. Relationship Between the Hemoglobin-to-Red Cell Distribution Width Ratio and All-Cause Mortality in Ischemic Stroke Patients with Atrial Fibrillation: An Analysis from the MIMIC-IV Database. Neuropsychiatr Dis Treat. 2022;18:341-354. Wang J, Chen Z, Yang H, Li H, Chen R, Yu J. Relationship between the Hemoglobin-to-Red Cell Distribution Width Ratio and All-Cause Mortality in Septic Patients with Atrial Fibrillation: Based on Propensity Score Matching Method. J Cardiovasc Dev Dis. 2022;9(11):400. Stavros V Konstantinides, Guy Meyer, Cecilia Becattini, Héctor Bueno, Geert-Jan Geersing, et al. 2019 ESC Guidelines for the diagnosis and management of acute pulmonary embolism developed in collaboration with the European Respiratory Society (ERS): The Task Force for the diagnosis and management of acute pulmonary embolism of the European Society of Cardiology (ESC). European Heart Journal. 2020;4(41):543-603. Huang X, Yuan S, Ling Y, et al. The Hemoglobin-to-Red Cell Distribution Width Ratio to Predict All-Cause Mortality in Patients with Sepsis-Associated Encephalopathy in the MIMIC-IV Database. Int J Clin Pract. 2022;2022:7141216. Sun P, Zhang F, Chen C, et al. The ratio of hemoglobin to red cell distribution width as a novel prognostic parameter in esophageal squamous cell carcinoma: a retrospective study from southern China. Oncotarget. 2016;7(27):42650-42660. Yoshida N, Horinouchi T, Eto K, et al. Prognostic Value of Pretreatment Red Blood Cell Distribution Width in Patients With Esophageal Cancer Who Underwent Esophagectomy: A Retrospective Study. Ann Surg Open. 2022;3(2):e153. Sangoi MB, Da Silva SH, da Silva JE, Moresco RN. Relation between red blood cell distribution width and mortality after acute myocardial infarction. Int J Cardiol. 2011;146(2):278-280. Dabbah S, Hammerman H, Markiewicz W, Aronson D. Relation between red cell distribution width and clinical outcomes after acute myocardial infarction. Am J Cardiol. 2010;105(3):312-317. Förhécz Z, Gombos T, Borgulya G, Pozsonyi Z, Prohászka Z, Jánoskuti L. Red cell distribution width: a powerful prognostic marker in heart failure. Eur J Heart Fail. 2010;12(4):415. Hampole CV, Mehrotra AK, Thenappan T, Gomberg-Maitland M, Shah SJ. Usefulness of red cell distribution width as a prognostic marker in pulmonary hypertension. Am J Cardiol. 2009;104(6):868-872. Zorlu A, Bektasoglu G, Guven FM, et al. Usefulness of admission red cell distribution width as a predictor of early mortality in patients with acute pulmonary embolism. Am J Cardiol. 2012;109(1):128-134. Liao J, Lu D, Zhang L, Wang M. Prognostic value of red blood cell distribution width in sepsis induced cardiomyopathy patients. Sci Rep. 2024;14(1):24483. Caimi G, Montana M, Canino B, Calandrino V, Lo Presti R, Hopps E. Erythrocyte deformability, plasma lipid peroxidation and plasma protein oxidation in a group of OSAS subjects. Clin Hemorheol Microcirc. 2016;64(1):7-14. Baldea I, Teacoe I, Olteanu DE, et al. Effects of different hypoxia degrees on endothelial cell cultures-Time course study. Mech Ageing Dev. 2018;172:45-50. doi:10.1016/j.mad.2017.11.003 Christou H, Khalil RA. Mechanisms of pulmonary vascular dysfunction in pulmonary hypertension and implications for novel therapies. Am J Physiol Heart Circ Physiol. 2022;322(5):H702-H724. Gomes MT, Bai Y, Potje SR, Zhang L, Lockett AD, Machado RF. Signal Transduction during Metabolic and Inflammatory Reprogramming in Pulmonary Vascular Remodeling. Int J Mol Sci. 2022;23(5):2410. Yu FT, Armstrong JK, Tripette J, Meiselman HJ, Cloutier G. A local increase in red blood cell aggregation can trigger deep vein thrombosis: evidence based on quantitative cellular ultrasound imaging. J Thromb Haemost. 2011;9(3):481-488. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6888191","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494627012,"identity":"50f66fa8-cbbd-413f-a8a6-52efbd356343","order_by":0,"name":"Jian Liao","email":"","orcid":"","institution":"Deyang People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Liao","suffix":""},{"id":494627013,"identity":"4feeb003-1b61-4463-ae3a-3234258252b4","order_by":1,"name":"Dingyu Lu","email":"","orcid":"","institution":"Deyang People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dingyu","middleName":"","lastName":"Lu","suffix":""},{"id":494627014,"identity":"9e5583fc-7345-4018-8017-f54fe3941a91","order_by":2,"name":"Maojuan Wang","email":"","orcid":"","institution":"Deyang People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Maojuan","middleName":"","lastName":"Wang","suffix":""},{"id":494627015,"identity":"7655d1e2-4e5a-4a1b-b450-29e2ab5965f8","order_by":3,"name":"Wei Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACNv72479//LCp72dmPkCcFj6JMwnSjD1pjDPb2RKI0yLHkGAgzcB2mHHDeR4DIh3GcCDBuIDnMLNkM8/HG28Y7OR0GwhpYW48kDzDIp2Nn5l3s+UchmRjswNE2HKAh8eaR7KZd5s0D8OBxG2EtSQYNvCwMUsYHOZ5RrQWY2YeNmcDoBY2IrVInAEGcE9agmQzm7HlHAMi/CLf336M4cMPmwR+/sMPb7ypsJMjqAUFSBAbNchaSNUxCkbBKBgFIwIAANVrPMjL5TnzAAAAAElFTkSuQmCC","orcid":"","institution":"Deyang People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-06-13 12:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6888191/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6888191/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88348448,"identity":"cc50cb12-3aed-42bd-aae8-0f7b6687727e","added_by":"auto","created_at":"2025-08-05 14:00:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22542,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study patients\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6888191/v1/e717c5279c4cf007152325d5.png"},{"id":88348464,"identity":"b587d390-cac0-4e4a-ac4f-bfc069ee8100","added_by":"auto","created_at":"2025-08-05 14:00:07","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66231,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curve of HRR with (a) 28-day mortality, (b) 90-day mortality. Q1:0.178 ≤ HRR \u0026lt; 0.548 ; Q2:0.548 ≤ HRR \u0026lt; 0.707; Q3:0.707 ≤ HRR \u0026lt; 0.88; Q4:0.88 ≤ HRR ≤ 1.33\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6888191/v1/235222f3dde687b802844c82.jpeg"},{"id":88348447,"identity":"6e32bf96-0ac2-469b-a9de-4a4d8fa0157e","added_by":"auto","created_at":"2025-08-05 14:00:05","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45782,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline analyses the association of HRR with (a) 28-day mortality, (b) 90-day mortality\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6888191/v1/8f61da7978d4a2af0c411613.jpeg"},{"id":88348463,"identity":"a2209301-447a-4bb6-a88d-e79a84fc76e4","added_by":"auto","created_at":"2025-08-05 14:00:07","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59429,"visible":true,"origin":"","legend":"\u003cp\u003eTime-dependent ROC curves of HRR, RDW and hemoglobin for predicting (a) 28-day mortality, (b) 90-day mortality\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6888191/v1/6d810f3e8245baa517003c99.jpeg"},{"id":88351714,"identity":"04cc5b11-5216-4136-90fc-b56153b3daac","added_by":"auto","created_at":"2025-08-05 14:24:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1332656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6888191/v1/524968f2-3c24-4dee-bc97-bbcff8e2a696.pdf"},{"id":88348467,"identity":"f6a7c448-f95d-4932-8089-25defa71c2e1","added_by":"auto","created_at":"2025-08-05 14:00:07","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":415523,"visible":true,"origin":"","legend":"","description":"","filename":"data.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6888191/v1/664f504fe770f5cebef36986.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic value of hemoglobin to red cell distribution width ratio in patients with pulmonary embolism","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePulmonary embolism (PE) is a common and potentially life-threatening cardiovascular disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], characterized by an obstruction in the pulmonary artery due to a clot, tumor, air or fat [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Deep vein thrombosis (DVT) constitutes the primary pathophysiological origin of pulmonary thromboembolism (PTE). DVT typically develops in the deep venous systems of the lower extremities or pelvic vasculature. Subsequent embolization through venous return transports dislodged thrombi into the pulmonary arterial circulation and its branches [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. PE affects between 39 and 115 out of every 100,000 people each year, which greatly impacts medical care, hospital stay lengths, and mortality rates [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite advances in diagnostic techniques and treatments, PE remains a significant cause of morbidity and mortality worldwide. Early identification of high-risk patients and accurate prognosis assessment are crucial for improving clinical outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recently, emerging evidence has suggested that inflammation and hypercoagulability play important roles in the pathogenesis and progression of PE [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Hemoglobin (Hb), a critical hematologic parameter in complete blood count (CBC) profiles, serves as a biomarker reflecting nutritional reserves (particularly iron metabolism and protein synthesis) and modulates immune competence through oxygen-dependent cellular functions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The red blood cell distribution width (RDW) is a readily available clinical indicator that reflects the heterogeneity in red blood cell size [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It has been documented that an elevated RDW may stem from various factors such as inflammation, oxidative stress, nutritional deficiencies, and renal dysfunction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Hemoglobin and red cell RDW are not only reflect the balance between hematopoietic function and red blood cell survival but also play a critical role in inflammation, oxidative stress, and the vascular\u003c/p\u003e\u003cp\u003einnate immune system [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The Hb/RDW ratio (HRR), a novel biomarker integrating two hematologic parameters, demonstrates greater specificity and sensitivity than either Hb or RDW alone [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This enables it to more accurately reflect patients' inflammation levels and better evaluate its correlation with disease progression across multiple conditions, such as stroke [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], acute decompensated heart failure [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], ischemic stroke [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and multiple cancers [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In patients with pulmonary embolism (PE), thrombus formation augments inflammatory responses, while excessive inflammation reciprocally promotes thrombosis. This bidirectional pathological interplay primarily stems from the activation of leukocytes, platelets, and endothelial cells [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, the relationship between HRR and mortality in patients with PE remains unclear. Additionally, it is also unclear whether HRR outperforms RDW as a predictor of mortality in this population. Therefore, our primary objective is to evaluate the association of both RDW and HRR with mortality in PE patients and to compare their predictive performance.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eEthical Statement and Data Source\u003c/b\u003e\u003c/p\u003e\u003cp\u003e This study utilized de-identified patient data extracted from Medical Information Mart for Intensive Care IV (MIMIC-IV v3.0), a publicly accessible critical care database jointly managed by Beth Israel Deaconess Medical Center (BIDMC) and Massachusetts Institute of Technology (MIT). All included patients were tracked for up to 1 year post-discharge. The institutional review boards (IRBs) of both BIDMC and MIT granted full approval for establishing and maintaining the MIMIC-IV database. Because the data is publicly available, both the statement regarding ethical approval and the necessity for informed consent were waived for this investigation. The research protocol adhered rigorously to the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003eThe research excluded individuals below the age of 18 during their initial admission, individuals who experienced multiple admissions to the ICU due to PE (only data from the first admission were considered). We excluded patients who met the following criteria: less than 18 years (n = 2), length of ICU stay \u0026lt; 24h (n = 941). The vital signs and laboratory variables were collected only within the first 24 hours after the patient’s admission. When multiple outcomes were present, the mean value was utilized. Finally, a total of 1658 patients formed the final study cohort and were divided into four groups based on the quartiles of the HHR observed on their first day in the ICU.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo conduct the data extraction, we utilized PostgresSQL software and pgAdmin 4 tool by employing Structured Query Language (SQL). The extraction process prioritized four distinct categories of potential variables: demographic factors, vital signs, laboratory tests, severity of illness score, comorbidities and treatment during ICU stay. All vital signs and laboratory tests data were measured for the first time within 24 hours of ICU admission.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe main outcome of this study was 28-day mortality after ICU admission. Secondary outcomes focused on 90-day mortality in hospital.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe patients were categorized into five groups based on their admission HRR levels: Quartile 1 (0.178 ≤ HRR \u0026lt; 0.548 ), Quartile 2 (0.548 ≤ HRR \u0026lt; 0.707), Quartile 3 (0.707 ≤ HRR \u0026lt; 0.88) and Quartile 4 (0.88 ≤ HRR ≤ 1.33). Continuous variables were presented as the mean ± SD or median and interquartile range (IQR). Categorical variables were expressed as numbers or percentages (%). ANOVA analysis, the Kruskal-Wallis test for continuous variables, or the chi-square test for categorical data, as applicable.\u003c/p\u003e\u003cp\u003eKaplan-Meier survival analysis was used to assess the incidence rate of primary outcome events in different stratified groups based on the HRR level. The log-rank test was employed to examine any observed disparities. Binary logistic regression analysis was conducted to evaluate factors influencing the risk of all-cause mortality.\u003c/p\u003e\u003cp\u003eCox regression analysis was conducted to investigate the relationship between RDW, HRR and 28-day mortality. The selection of confounders was based on clinical relevance, existing literature, and all signifcant covariates identifed in the univariate analysis. Model 1: unadjusted; Model 2 was adjusted for age, gender, BMI, smoke, atrial fibrillation, phlebothrombosis, pulmonary hypertension, sepsis, hypertension, diabetes, heart failure, acute myocardial infarction (AMI), acute kidney injury(AKI), vasoactive drugs, anticoagulant drugs, antiplatelet drugs, white blood cell (WBC), platelet, albumin, sodium, potassium, calciumtotal, glucose, creatinine, Sequential Organ Failure Assessment (SOFA), Acute Physiology Score III (APSIII), Oxford acute severity of illness score (OASIS), Charlson, continuous renal replacement therapy (CRRT), non-invasive ventilation, invasive ventilation. HRs were counted and the findings were presented with 95% confidence intervals (CI). The lowest quartile of the HHR was used as the baseline group in all four models.\u003c/p\u003e\u003cp\u003eWe also conducted an analysis to examine the linear relationship between the baseline HRR and the risk of mortality. This analysis was done using a restricted cubic spline regression model with five knots.\u003c/p\u003e\u003cp\u003eTo evaluate the dynamic prognostic performance of HRR, RDW and hemoglobin across temporal dimensions, we implemented time-dependent receiver operating characteristic (ROC) analysis using the timeROC package. The data analyses were conducted using R software (R version 4.2.2, R Foundation for Statistical Computing). For all analyses, a two-side P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study enrolled a total of 1658 critically ill patients with PE from the MIMIC-IV database, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe average age of the patients was 58\u0026thinsp;\u0026plusmn;\u0026thinsp;15 years, with males comprising 53.2% of the sample, the 28-day mortality rate was 13.8%, and the 90-day mortality rate was 19.6%. The median hospital length of stay was 11 days (IQR 6\u0026ndash;21), with an ICU length of stay was 3 days (IQR 2\u0026ndash;7). Patients in the Quartiles 4 demonstrated highest hemoglobin levels, lowest RDW values, the highest HRR levels and shortest hospital length of stay. However, no statistically differences were observed among the four groups in ICU length of stay. The increase in HHR was associated with decreased rate of 28-day mortality (18.1% vs. 13.5% vs. 11.8% vs. 11.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 90-day mortality (28.4% vs. 19.1% vs. 16.4% vs. 14.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrimary outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurvival analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Kaplan-Meier survival curve showed that the survival rate for 28-day increased for the higher HRR groups compared to the lower HRR groups (log-rank test p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, the 90-day survival curve demonstrated similar results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of participants stratified by HHR quartile levels\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\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eOverall Quartiles 1 Quartiles 2 Quartiles 3 Quartiles 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1658\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.178 \u0026le; HRR\u0026thinsp;\u0026lt;\u0026thinsp;0.548 \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;415\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.548\u0026thinsp;\u0026le;\u0026thinsp;HRR\u0026thinsp;\u0026lt;\u0026thinsp;0.707\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;414\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.707\u0026thinsp;\u0026le;\u0026thinsp;HRR\u0026thinsp;\u0026lt;\u0026thinsp;0.88\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;414\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.88\u0026thinsp;\u0026le;\u0026thinsp;HRR\u0026thinsp;\u0026le;\u0026thinsp;1.33 \u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;415\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e882 (53.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e180 (43.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e207 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e220 (53.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e275 (66.3%)\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\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33\u0026thinsp;\u0026plusmn;\u0026thinsp;11\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\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVital signs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature, ℃\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR, beats/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.010\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\u003e121\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e117\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e122\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e127\u0026thinsp;\u0026plusmn;\u0026thinsp;21\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\" colname=\"c1\"\u003e\u003cp\u003eDBP, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115\u0026thinsp;\u0026plusmn;\u0026thinsp;1,727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e249\u0026thinsp;\u0026plusmn;\u0026thinsp;3,460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRR, beats/min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.801\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\u003e96.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e96.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory tests\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, K/uL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (8, 16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (7, 17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (8, 16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (8, 16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12 (9, 16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.312\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet, K/uL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e222\u0026thinsp;\u0026plusmn;\u0026thinsp;120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e224\u0026thinsp;\u0026plusmn;\u0026thinsp;144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e234\u0026thinsp;\u0026plusmn;\u0026thinsp;139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e212\u0026thinsp;\u0026plusmn;\u0026thinsp;98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e219\u0026thinsp;\u0026plusmn;\u0026thinsp;86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\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\" colname=\"c1\"\u003e\u003cp\u003eRDW, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.08\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\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\" colname=\"c1\"\u003e\u003cp\u003eHRR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\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\" colname=\"c1\"\u003e\u003cp\u003eAlbumin, g/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\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\" colname=\"c1\"\u003e\u003cp\u003eSodium, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e138.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e138.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\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\" colname=\"c1\"\u003e\u003cp\u003ePotassium, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalciumtotal, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\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\" colname=\"c1\"\u003e\u003cp\u003eGlucose, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126 (105, 163)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e121 (101, 159)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124 (104, 165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e132 (109, 171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e128 (106, 161)\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\u003eCreatinine, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.90 (0.70, 1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (0.70, 1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.70, 1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.90 (0.70, 1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.90 (0.70, 1.20)\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\u003e\u003cb\u003eSeverity of illness score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\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\" colname=\"c1\"\u003e\u003cp\u003eAPSIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;22\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\" colname=\"c1\"\u003e\u003cp\u003eOASIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharlson\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\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\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e310 (18.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87 (21.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtrial fibrillation, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e362 (21.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91 (21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101 (24.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e86 (20.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.478\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhlebothrombosis, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e387 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110 (26.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e104 (25.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89 (21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulmonary hypertension, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106 (6.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSepsis, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e942 (56.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e258 (62.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e251 (60.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e223 (53.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e210 (50.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e587 (35.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128 (30.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e166 (40.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e162 (39.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e420 (25.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120 (28.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114 (27.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107 (25.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79 (19.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart failure, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e414 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114 (27.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123 (29.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e109 (26.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e68 (16.4%)\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\" colname=\"c1\"\u003e\u003cp\u003eAMI, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112 (6.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAKI, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,300 (78.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e326 (78.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e319 (77.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e324 (78.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e331 (79.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment during hospitalization\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVasoactive drugs, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e441 (26.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107 (25.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108 (26.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e109 (26.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e117 (28.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnticoagulant drugs, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e722 (43.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e182 (43.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e167 (40.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e184 (44.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e189 (45.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.465\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntiplatelet drugs, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e447 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122 (29.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 (24.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e122 (29.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRRT, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (6.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18 (4.3%)\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\" colname=\"c1\"\u003e\u003cp\u003eNon-invasive ventilation, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,193 (72.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e292 (70.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e296 (71.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e307 (74.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e298 (71.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.668\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInvasive ventilation, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e660 (39.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141 (34.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e170 (41.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e177 (42.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e172 (41.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28-day mortality, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e228 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (18.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (13.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49 (11.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48 (11.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e90-day mortality, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e325 (19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (28.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e60 (14.5%)\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\" colname=\"c1\"\u003e\u003cp\u003eLOS in hospital, days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (6, 21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (7, 25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (7, 19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (7, 19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9 (5, 20)\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\" colname=\"c1\"\u003e\u003cp\u003eLOS in ICU, days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (2, 7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (2, 6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (2, 6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (2, 7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3 (2, 8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBMI, body mass index; HR, heart rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; RR, respiratory rate; WBC, white blood cell; RDW, red cell distribution width; HRR, hemoglobin-to-red blood cell distribution width ratio; AMI, acute myocardial infarction; AKI, acute kidney injury; CRRT, continuous renal replacement therapy; LOS in hospital, length of stay in hospital; LOS in ICU, length of stay in ICU\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociations of HRR and RDW with the risk of 28-day mortality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the multivariable Cox proportional hazard model, RDW showed a positive association with increased risk of 28-day mortality (HR\u0026thinsp;=\u0026thinsp;1.15, 95% CI\u0026thinsp;=\u0026thinsp;1.09\u0026ndash;1.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) when expressed as a continuous variable in modle 1 (no adjusted), and the risk of mortality was elevated by 10% (HR\u0026thinsp;=\u0026thinsp;1.10, 95% CI\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;1.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in model 2 after adjusting for potential confounders. When HHR was considered as a continuous variable, HRR was inversely associated with the risk of 28-day mortality (HR\u0026thinsp;=\u0026thinsp;0.44, 95% CI\u0026thinsp;=\u0026thinsp;0.22\u0026ndash;0.86, p\u0026thinsp;=\u0026thinsp;0.017) after adjustment. When HHR was treated as a nominal variable, patients in the highest quartile group had a 0.64-fold lower risk of 28-day mortality (HR\u0026thinsp;=\u0026thinsp;0.64, 95% CI\u0026thinsp;=\u0026thinsp;0.42\u0026ndash;0.96, p\u0026thinsp;=\u0026thinsp;0.032) than those in the lowest quartile group after adjusting for potential confounders (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociations of HRR and RDW with the risk of 90-day mortality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe results showed that RDW also had a positive association with increased risk of 90-day mortality (HR\u0026thinsp;=\u0026thinsp;1.13, 95% CI\u0026thinsp;=\u0026thinsp;1.08\u0026ndash;1.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) when expressed as a continuous variable in modle 2 after adjusting for potential confounders. HRR was also inversely associated with the risk of 90-day mortality (HR\u0026thinsp;=\u0026thinsp;0.29, 95% CI\u0026thinsp;=\u0026thinsp;0.16\u0026ndash;0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in modle 2 when HHR was considered as a continuous variable. When HHR was treated as a nominal variable, patients in the highest quartile group had a 0.53-fold lower risk of 90-day mortality (HR\u0026thinsp;=\u0026thinsp;0.53, 95% CI\u0026thinsp;=\u0026thinsp;0.37\u0026ndash;0.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than those in the lowest quartile group after adjusting for potential confounders (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrated that higher HRR level was associated with progressively reduced mortality risks at both 28-day and 90-day follow-up intervals.\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\u003eThe associations of RDW and HRR with the risk of 28-day mortality\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\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\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eModel 2\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=\"c5\"\u003e\u003cp\u003eHR (95%CI)\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\u003eRDW per 1 unit increment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.15 (1.09\u0026thinsp;~\u0026thinsp;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.10 (1.04\u0026thinsp;~\u0026thinsp;1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRR per 1 unit increment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27 (0.14\u0026thinsp;~\u0026thinsp;0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.44 (0.22\u0026thinsp;~\u0026thinsp;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartiles\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\u003eQ1(0.178\u0026thinsp;\u0026le;\u0026thinsp;HRR\u0026thinsp;\u0026lt;\u0026thinsp;0.548)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\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\u003eQ2( 0.548\u0026thinsp;\u0026le;\u0026thinsp;HRR\u0026thinsp;\u0026lt;\u0026thinsp;0.707)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.74 (0.52\u0026thinsp;~\u0026thinsp;1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.76 (0.53\u0026thinsp;~\u0026thinsp;1.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3(0.707\u0026thinsp;\u0026le;\u0026thinsp;HRR\u0026thinsp;\u0026lt;\u0026thinsp;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55 (0.39\u0026thinsp;~\u0026thinsp;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66 (0.45\u0026thinsp;~\u0026thinsp;0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4(0.88\u0026thinsp;\u0026le;\u0026thinsp;HRR\u0026thinsp;\u0026le;\u0026thinsp;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.49 (0.34\u0026thinsp;~\u0026thinsp;0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.64 (0.42\u0026thinsp;~\u0026thinsp;0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eHR: Hazard Ratio, CI: Confidence Interval\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 1: no adjusted\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 2: adjusted for Age, Gender, BMI, Smoke, Atrial fibrillation, Phlebothrombosis, Pulmonary hypertension, Sepsis, Hypertension, Diabetes, Heart failure, AMI, AKI, Vasoactive drugs, Anticoagulant drugs, Antiplatelet drugs, WBC, Platelet, Albumin, Sodium, Potassium, Calciumtotal, Glucose, Creatinine, SOFA, APSIII, OASIS, Charlson, CRRT, Non-invasive ventilation, Invasive ventilation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe associations of RDW and HRR with the risk of 90-day mortality\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\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\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eModel 2\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=\"c5\"\u003e\u003cp\u003eHR (95%CI)\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\u003eRDW per 1 unit increment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17 (1.13\u0026thinsp;~\u0026thinsp;1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.13 (1.08\u0026thinsp;~\u0026thinsp;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHRR per 1 unit increment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.18 (0.11\u0026thinsp;~\u0026thinsp;0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.29 (0.16\u0026thinsp;~\u0026thinsp;0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartiles\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\u003eQ1(0.178\u0026thinsp;\u0026le;\u0026thinsp;HRR\u0026thinsp;\u0026lt;\u0026thinsp;0.548)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (Reference)\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\u003eQ2( 0.548\u0026thinsp;\u0026le;\u0026thinsp;HRR\u0026thinsp;\u0026lt;\u0026thinsp;0.707)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.68 (0.51\u0026thinsp;~\u0026thinsp;0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71 (0.53\u0026thinsp;~\u0026thinsp;0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3(0.707\u0026thinsp;\u0026le;\u0026thinsp;HRR\u0026thinsp;\u0026lt;\u0026thinsp;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.50 (0.37\u0026thinsp;~\u0026thinsp;0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.59 (0.43\u0026thinsp;~\u0026thinsp;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4(0.88\u0026thinsp;\u0026le;\u0026thinsp;HRR\u0026thinsp;\u0026le;\u0026thinsp;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.40 (0.29\u0026thinsp;~\u0026thinsp;0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.53 (0.37\u0026thinsp;~\u0026thinsp;0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eHR: Hazard Ratio, CI: Confidence Interval\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 1: no adjusted\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 2: adjusted for Age, Gender, BMI, Smoke, Atrial fibrillation, Phlebothrombosis, Pulmonary hypertension, Sepsis, Hypertension, Diabetes, Heart failure, AMI, AKI, Vasoactive drugs, Anticoagulant drugs, Antiplatelet drugs, WBC, Platelet, Albumin, Sodium, Potassium, Calciumtotal, Glucose, Creatinine, SOFA, APSIII, OASIS, Charlson, CRRT, Non-invasive ventilation, Invasive ventilation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe linear relationship between the HRR and the risk of mortality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study employed a restricted cubic splines regression model to investigate the relationship between HRR level and the risk of mortality. After adjusting for confounders in model 2, we found that there was a linear relationship between HRR and 28-day mortality (p for non-linearity\u0026thinsp;=\u0026thinsp;0.843) and 90-day mortality (p for non-linearity\u0026thinsp;=\u0026thinsp;0.666) in patients with PE (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePredictive value of HHR for 28-day and 90-day mortality\u003c/b\u003e\u003c/p\u003e\u003cp\u003eROC curves were performed to evaluate the predictive value of HRR, RDW and hemoglobin for mortality in patients with PE. The AUC of HRR, RDW and hemoglobin were 0.610, 0.558 and 0.513, respectively. Meanwhile, the AUC of HRR, RDW and hemoglobin were 0.641, 0.595 and 0.542, respectively. The results indicate that HRR appears to be a more reliable predictor, providing a more accurate assessment of both 28-day mortality and 90-day mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large retrospective cohort study using MIMIC-IV database from 2008 to 2022, we clearly revealed that HRR was independently associated with an increased risk of 28-day mortality and 90-day mortality in patients with PE. In our investigation, an linear correlation was identified between the HRR and mortality during hospitalization. Moreover, both RDW and HRR are effective predictors of all-cause mortality across different follow-up periods. However, HRR appears to exhibit superior predictive value compared to RDW. This indicates that the HRR has the potential to be a valuable tool in identifying individuals with a high risk of mortality in patients with PE.\u003c/p\u003e\u003cp\u003eHRR is calculated using hemoglobin and red blood cell distribution width, which reflect anemia and red blood cell heterogeneity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Anemia, indicated by low Hb, is linked to poor outcomes in patients with PE. It reduces oxygen-carrying capacity, exacerbating hypoxia and potentially leading to multiple organ dysfunction [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. RDW, a marker of red blood cell size variation, is associated with inflammation and oxidative stress. Elevated RDW levels may signify systemic inflammation, which plays a pivotal role in PE by promoting thrombogenesis and vascular damage [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. HRR integrates Hb and RDW, offering a comprehensive assessment of a patient\u0026rsquo;s inflammatory and nutritional status. A low HRR suggests either low Hb, high RDW, or both, indicating anemia, red blood cell heterogeneity, and underlying inflammation. In patients with PE, inflammation is a key driver of thrombus formation and complications [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The inflammatory response triggers the release of cytokines and adhesion molecules, promoting platelet activation and coagulation, thereby worsening PE [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Oxidative stress, often accompanying inflammation, can damage vascular endothelial cells, further disrupting hemostasis and aggravating PE [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent studies have confirmed that higher HRR was positively associated with a decreased risk of all-cause mortality in patients with different diseases, such as stroke,acute decompensated heart failure, ischemic stroke [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Huang et al. conducted a large retrospective cohort, which included a total of 8,853 critically ill patients with Sepsis-Associated Encephalopathy, and found a linear relationship between all-cause mortality and HRR in patients with SAE, with low HRR being inversely associated with increased all-cause mortality in patients with SAE [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, there are currently no studies investigating its relationship with PE. Building upon these findings, our study focused on patients with PE and found that when the HHR was considered as a continuous variable, HRR was inversely associated with the risk of 28-day mortality (HR\u0026thinsp;=\u0026thinsp;0.44, 95% CI\u0026thinsp;=\u0026thinsp;0.22\u0026ndash;0.86, p\u0026thinsp;=\u0026thinsp;0.017) and 90-day mortality (HR\u0026thinsp;=\u0026thinsp;0.29, 95% CI\u0026thinsp;=\u0026thinsp;0.16\u0026ndash;0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eThe Hemoglobin to RDW Ratio (HRR), initially proposed by Sun .et al as a novel biomarker demonstrating prognostic value in malignancies, exhibits significant associations with survival outcomes in esophageal squamous cell carcinoma (ESCC) patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Reductions in HRR result from either diminished Hb levels or elevated RDW values. Consequently, patients with lower HRR ratios clinically manifest as increased mortality risk and poorer prognosis, reflecting underlying systemic inflammation and nutritional depletion [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. RDW is a laboratory index used in the differential diagnosis of anemia. It is a simple laboratory test that evaluates the variability in the size and form of red blood cells. Recently, there have been several studies linking high RDW to increased mortality in various medical conditions such as coronary disease [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], heart failure [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], pulmonary hypertension [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], pulmonary embolism [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and sepsis induced cardiomyopathy [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Our study showed that HRR is linearly and negatively linked to 28-day and 90-day mortality. Higher HRR means lower risk of mortality. ROC curve analysis indicated that HRR is a better predictor of clinical outcomes in patients with PE than RDW and Hb. It may serve as a strong independent predictor for stratifying the risk of in-hospital and ICU mortality.\u003c/p\u003e\u003cp\u003eThe possible mechanism is that the inflammatory response triggers the secretion of large amounts of inflammatory cytokines, which damage the red blood cell membrane and reduce the deformability of red blood cells [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Chronic hypoxia causes peroxides to accumulate in the lungs, leading to cell apoptosis and disrupting the ventilation-perfusion ratio [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This triggers an inflammatory response, prompting the production of inflammatory mediators. These mediators act on vascular endothelial and smooth muscle cells, causing pulmonary vasoconstriction and vascular remodeling [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. As red blood cells pass through constricted vessels, they are damaged, increasing red blood cell heterogeneity. Additionally, inflammatory factors and oxidative stress stimulate erythropoiesis, alter the half-life of red blood cells, and reduce their lifespan. This results in the premature release of immature red blood cells into the bloodstream [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Increased red blood cell heterogeneity can raise blood viscosity, slow blood flow, and prolong cell-vessel wall contact time. This leads to platelet activation and fibrinogen action, promoting thrombosis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study utilized a large, publicly available critical-care database to assess the relationship between HRR and mortality risk in PE patients. However, the study has limitations. First, its retrospective cohort design may introduce selection bias, as researchers relied on pre-existing data and records, which can contain errors. Second, despite matching participants and controlling for variables, unadjusted confounders could still affect the results. Finally, the study did not evaluate long-term patient outcomes. Thus, caution is needed when using these findings to predict long-term prognosis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study reveals that HRR levels serve as a simple,novel, cost - effective and valuable biomarker, which is an independent indicator of unfavorable outcomes for patients with pulmonary embolism. However, further research is needed to clarify the underlying biological mechanisms and establish the clinical utility of HRR.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHRR \u0026nbsp;Hemoglobin to red cell distribution width ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePE \u0026nbsp; \u0026nbsp;Pulmonary embolism\u003c/p\u003e\n\u003cp\u003eDVT \u0026nbsp; Deep vein thrombosis\u003c/p\u003e\n\u003cp\u003ePTE \u0026nbsp; Pulmonary thromboembolism\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; Body mass index\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; Heart rate\u003c/p\u003e\n\u003cp\u003eSBP \u0026nbsp; \u0026nbsp;Systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP \u0026nbsp; \u0026nbsp;Diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eRR \u0026nbsp; \u0026nbsp;Respiratory rate\u003c/p\u003e\n\u003cp\u003eWBC \u0026nbsp;White blood cell\u003c/p\u003e\n\u003cp\u003eRDW \u0026nbsp; Red cell distribution width\u003c/p\u003e\n\u003cp\u003eAMI \u0026nbsp; \u0026nbsp;Acute myocardial infarction\u003c/p\u003e\n\u003cp\u003eAKI \u0026nbsp; Acute kidney injury\u003c/p\u003e\n\u003cp\u003eCRRT \u0026nbsp; Continuous renal replacement therapy\u003c/p\u003e\n\u003cp\u003eLOS in hospital \u0026nbsp;Length of stay in hospital\u003c/p\u003e\n\u003cp\u003eLOS in ICU \u0026nbsp;Length of stay in ICU\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; receiver operating characteristic curve\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause the data is publicly available, both the statement regarding ethical approval and the necessity for informed consent were waived for this investigation.\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: .JL., D.L. Collection and assembly of data: DL.,LZ. Data analysis and interpretation: JL,. DL.Critical revisions and supervision: LZ,.MW. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRali P, Gandhi V, Malik K. Pulmonary embolism. Crit Care Nurs Q. 2016; 39 (2): 131-138.\u003c/li\u003e\n\u003cli\u003eRali PM, Criner GJ. Submassive pulmonary embolism. Am J Respir Crit Care Med. 2018;198(5): 588-598.\u003c/li\u003e\n\u003cli\u003eKalaitzopoulos DR, Panagopoulos A, Samant S, et al. Management of venous thromboembolism in pregnancy. Thromb Res. 2022;211:106-113. \u003c/li\u003e\n\u003cli\u003eNguyen E, Caranfa JT, Lyman GH, et al. Clinical prediction rules for mortality in patients with pulmonary embolism and cancer to guide outpatient management: a meta- analysis. 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J Thromb Haemost. 2011;9(3):481-488. \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-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"pulmonary embolism, red cell distribution width, hemoglobin to red cell distribution width ratio (HRR), mortality, MIMIC- IV","lastPublishedDoi":"10.21203/rs.3.rs-6888191/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6888191/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehis study investigates the prognostic value of the hemoglobin to red cell distribution width ratio (HRR) in pulmonary embolism (PE), a life-threatening cardiovascular disease. While inflammation and hypercoagulability drive PE pathogenesis, the role of HRR remains unexplored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this retrospective cohort study, data from 1,658 critically ill PE patients (2008–2022) were extracted from the MIMIC-IV database. Patients were stratified by HRR quartiles (Q1–Q4). COX proportional hazards regression analysis, Kaplan- Meier survival curves and restricted cubic spline models were employed to investigate the association of RDW and HRR levels with mortality. Time-dependent receiver operating characteristic curve (ROC) analysis was conducted to evaluate the accuracy of RDW and HRR in predicting mortality in patients with PE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with a poor prognosis and mortality had significantly lower HRR levels at admission. When HHR was considered as a continuous variable, HRR was inversely associated with 28-day mortality (HR = 0.44, 95% CI = 0.22–0.86, p \u0026lt; 0.017) and 90-day mortality (HR = 0.29, 95% CI = 0.16–0.52, p \u0026lt; 0.001) after adjusting for various potential confounders. The Kaplan-Meier survival curve showed that the survival rate for 28-day increased for the higher HRR groups compared to the lower HRR groups (log-rank test p \u0026lt; 0.001). Moreover, the 90-day survival curve demonstrated similar results. Receiver-operating characteristic curve analysis demonstrated that HRR appears to be a more reliable predictor for both 28-day mortality ( The AUC is 0.610) and 90-day mortality ( The AUC is 0.641) than RDW and hemoglobin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHRR levels as a simple, novel, cost-effective, and valuable biomarker, are an independent predictor of poor prognosis for patients with pulmonary embolism. However, further research is necessary to elucidate the underlying biological mechanisms and confirm the clinical utility of HRR.\u003c/p\u003e","manuscriptTitle":"Prognostic value of hemoglobin to red cell distribution width ratio in patients with pulmonary embolism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-05 14:00:00","doi":"10.21203/rs.3.rs-6888191/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-07-31T08:36:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-23T10:50:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-03T09:10:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-03T09:01:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2025-07-03T08:57:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36c56760-440f-464e-bb7e-a5cd47b34549","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-05T14:00:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-05 14:00:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6888191","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6888191","identity":"rs-6888191","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}