Prognostic Value of Red Blood Cell Distribution Width for Mortality in Critically Ill Patients with Acute Pancreatitis

Prognostic Value of Red Blood Cell Distribution Width for Mortality in Critically Ill Patients with Acute Pancreatitis | 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 Red Blood Cell Distribution Width for Mortality in Critically Ill Patients with Acute Pancreatitis Qian Xiao, Yin Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7723696/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Acute pancreatitis (AP) is a common gastrointestinal emergency with unpredictable progression and high mortality in severe cases. Traditional prognostic scores such as APACHE II, BISAP, and SOFA are limited by complexity and delayed applicability. Red blood cell distribution width (RDW), a simple and universally available biomarker, has emerged as a potential prognostic indicator. This study assessed the predictive value of RDW for short- and long-term mortality in critically ill patients with AP. Methods We conducted a retrospective cohort study using the MIMIC-IV database. Adult patients with a primary diagnosis of AP were included, with exclusions for repeat admissions, ICU stay < 48 h, hematologic malignancy, or end-stage renal disease. Baseline RDW at ICU admission was the primary exposure. Primary outcomes were 28-day and 90-day all-cause mortality. Associations were evaluated using Kaplan–Meier analysis, Cox regression, restricted cubic splines, and subgroup analyses. Incremental prognostic performance was assessed with AUC, net reclassification index (NRI), and decision curve analysis (DCA). Results A total of 450 patients met inclusion criteria. The overall 28-day and 90-day mortality rates were 8.7% and 12.0%, respectively. Patients with elevated RDW (> 14.5%) had significantly higher mortality at both 28 days (13.4% vs. 4.3%, p < 0.001) and 90 days (18.7% vs. 6.5%, p < 0.001). In fully adjusted Cox models, RDW remained an independent predictor of mortality (28-day: HR = 1.31, 95% CI: 1.15–1.50; 90-day: HR = 1.28, 95% CI: 1.16–1.41). RDW demonstrated strong discriminatory ability (AUC: 0.837 for 28-day, 0.807 for 90-day mortality). Incorporating RDW into baseline models improved predictive accuracy (ΔAUC + 0.06; NRI = 0.21, p < 0.001), and DCA showed greater net clinical benefit. Conclusion RDW is a low-cost, widely accessible biomarker that independently predicts short- and long-term mortality in critically ill patients with AP. Its inclusion in conventional prognostic models enhances risk stratification and may support earlier, tailored clinical decision-making. Prospective multicenter validation is warranted. Acute pancreatitis red blood cell distribution width biomarker mortality prognosis MIMIC-IV Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 INTRODUCTION Acute pancreatitis (AP) is a common and potentially life-threatening gastrointestinal emergency with a global incidence of 13–45 cases per 100,000 individuals annually [(Song et al., 2025; Gravito-Soares et al., 2018)]. While the majority of patients experience a mild, self-limited course, up to 20% progress to severe disease, characterized by persistent organ failure, systemic inflammatory response syndrome (SIRS), and sepsis. In these cases, mortality can reach 30–40% despite advances in intensive care medicine [(Parsi et al., 2025; Carnovale et al., 2005)]. The unpredictable trajectory of AP complicates timely clinical decision-making, highlighting the urgent need for reliable early prognostic tools. Over the past decades, several scoring systems and biomarkers have been developed to predict outcomes in AP. Ranson’s criteria, the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Bedside Index of Severity in Acute Pancreatitis (BISAP), and the Sequential Organ Failure Assessment (SOFA) score remain the most widely used models [(Hagjer et al., 2018; Gao et al., 2015; Tee et al., 2018)]. While clinically informative, these scores are hindered by practical limitations: they require numerous laboratory parameters, complex calculations, and in some cases prolonged 48-hour observation periods, thereby delaying risk stratification [(Chen et al., 2013; Dancu et al., 2021)]. Furthermore, their predictive accuracy is inconsistent across populations and healthcare systems, reducing their utility as universal tools [(Goyal et al., 2017; WJG Surgery Review, 2020)]. As a result, there is growing interest in simple, rapid, and inexpensive biomarkers that can accurately identify high-risk patients at the point of care. Red blood cell distribution width (RDW), an automated measure of variability in erythrocyte size, has traditionally been used in the differential diagnosis of anemia. More recently, it has emerged as a promising biomarker of systemic inflammation and physiological stress [(Patel et al., 2009; Hunziker et al., 2012)]. Elevated RDW reflects inflammatory cytokine activity, oxidative stress, nutritional deficiencies, and impaired erythropoiesis, all of which are relevant to the pathogenesis of AP and its systemic complications [(Braun et al., 2011; Makhoul et al., 2013)]. Importantly, RDW is universally available from the complete blood count, incurs no additional cost, and can be obtained at admission, making it an attractive candidate for early prognostication in critically ill patients. Evidence linking RDW to adverse outcomes has accumulated across multiple clinical settings. Higher RDW is associated with increased mortality in sepsis, pneumonia, cardiovascular disease, and acute decompensated heart failure [(Hunziker et al., 2012; Braun et al., 2011; Hong et al., 2012; Makhoul et al., 2013)]. In community-acquired pneumonia, for example, RDW outperformed several traditional risk factors as a predictor of poor outcome [(Braun et al., 2011)], while in critically ill populations it improved the discriminative ability of established severity scores [(Hunziker et al., 2012)]. These observations suggest that RDW functions as a global marker of systemic stress and inflammation, supporting its investigation in AP. In pancreatitis specifically, RDW has shown consistent associations with disease severity and mortality. Şenol and colleagues (2013) first reported that elevated RDW independently predicted mortality in patients with AP. A larger cohort study by Wang et al. (2015) confirmed this finding, demonstrating that higher RDW was strongly linked to in-hospital death. Subsequent research has extended these observations: He et al. (2023) showed in a propensity-matched analysis of U.S. intensive care patients that RDW independently predicted in-hospital mortality; Singh et al. (2020) found that RDW correlated strongly with established severity scores; and Song et al. (2025) reported that RDW was associated with both short- and long-term mortality in AP complicated by sepsis. Beyond RDW alone, several composite indices have been proposed to enhance predictive performance. Ratios such as RDW-to-albumin (RDW/Alb) and RDW-to-calcium (RDW:Ca) have demonstrated incremental prognostic value, reflecting combined effects of inflammation, nutritional status, and metabolic dysregulation [(Gravito-Soares et al., 2018; He et al., 2025; Acehan et al., 2024)]. Similarly, novel immune-inflammatory indices derived from blood counts—including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII)—have shown prognostic potential in AP [(Parsi et al., 2025; Dancu et al., 2021; Chauhan et al., 2021)]. However, their comparative and incremental value relative to RDW remains uncertain, particularly in critically ill cohorts where inflammatory activation and multiorgan dysfunction frequently coexist. Despite encouraging evidence, important gaps remain in the literature. Many prior studies were single-center, retrospective, or limited by small sample sizes [(Şenol et al., 2013; Singh et al., 2020)]. Others did not assess long-term outcomes such as 90-day mortality or failed to compare RDW directly with established prognostic scores [(Goyal et al., 2017; Gao et al., 2015)]. Furthermore, few studies have rigorously evaluated whether incorporating RDW into conventional risk models meaningfully improves prognostic accuracy through measures such as net reclassification index or decision curve analysis. These knowledge gaps underscore the need for large, high-resolution datasets and robust statistical methods to clarify the clinical utility of RDW in AP. The present study therefore aimed to investigate the prognostic significance of RDW in critically ill patients with acute pancreatitis using the MIMIC-IV database. Specifically, we evaluated the association between RDW and 28-day and 90-day all-cause mortality, examined its interaction with systemic inflammatory indices and metabolic derangements, and assessed its incremental prognostic value beyond conventional severity models. By leveraging a large real-world cohort and comprehensive analytical approaches, this study provides new insights into the potential role of RDW as an inexpensive, readily available biomarker for risk stratification in acute pancreatitis. MATERIALS AND METHODS Study Design and Data Source This retrospective cohort study was conducted using the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.2) database, a large, freely available resource containing de-identified clinical data from critically ill patients admitted to the Beth Israel Deaconess Medical Center in Boston, Massachusetts. The database includes demographic information, laboratory results, clinical diagnoses, interventions, and outcomes. Data extraction was performed by a certified investigator after completion of the required training and access approval. The study adhered to the principles of the Declaration of Helsinki (2013 revision). Because MIMIC-IV is fully anonymized and publicly accessible, additional institutional review board approval and individual informed consent were not required. Study Population Patients were eligible if they had a diagnosis of acute pancreatitis (AP) identified using International Classification of Diseases, Ninth and Tenth Revision (ICD-9/10) codes. Exclusion criteria were: ( 1 ) age younger than 18 years at the time of first admission; ( 2 ) multiple admissions for AP, in which case only the first admission was analyzed; ( 3 ) presence of end-stage renal disease or hematologic malignancies; ( 4 ) ICU stay shorter than 48 hours; and ( 5 ) missing baseline measurements of red blood cell distribution width (RDW), platelet count, lymphocyte count, neutrophil count, or monocyte count. After applying these criteria, 450 patients were included in the final cohort. Patients were categorized into high- and low-RDW groups based on the median value of baseline RDW. Data Collection Baseline demographic variables included age and sex. Clinical variables included hypertension and sepsis, with sepsis defined according to the Sepsis-3 criteria. Laboratory parameters recorded within the first 24 hours of ICU admission comprised red blood cell (RBC) count, white blood cell (WBC) count, hemoglobin, platelet count, absolute lymphocyte, neutrophil, and monocyte counts, RDW, prothrombin time (PT), partial thromboplastin time (PTT), international normalized ratio (INR), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), blood urea nitrogen (BUN), total bilirubin, creatinine, glucose, total serum calcium, and serum sodium. If duplicate measurements were available, the earliest result was used. To capture systemic inflammation, the following indices were calculated ( Supplementary Table S1 ): Neutrophil-to-lymphocyte ratio (NLR) Platelet-to-lymphocyte ratio (PLR) Lymphocyte-to-monocyte ratio (LMR) Systemic immune-inflammation index (SII) = Neutrophils × Platelets ÷ Lymphocytes Systemic inflammation response index (SIRI) = Neutrophils × Monocytes ÷ Lymphocytes Pan-immune-inflammation value (PIV) = Neutrophils × Platelets × Monocytes ÷ Lymphocytes RDW:Ca ratio = RDW (%) ÷ serum calcium (mg/dL) Organ dysfunction composites (renal, hepatic, coagulation, and glycemic stress scores) were derived using standardized z-scores Outcomes The primary outcomes were all-cause mortality at 28 days and 90 days following ICU admission. Secondary outcomes included ICU length of stay, hospital length of stay, and organ dysfunction burden as assessed by renal, hepatic, and coagulation indices. Handling of Missing Data Variables with more than 30% missingness were excluded from analysis. For variables with less than 30% missingness, multiple imputation by chained equations was performed. The proportion of missingness for each variable is summarized in Supplementary Table S2. Statistical Analysis Continuous variables were expressed as mean ± standard deviation or median (interquartile range), depending on distribution, which was assessed using the Shapiro–Wilk test. Categorical variables were presented as frequencies and percentages. Between-group comparisons were performed using Student’s t-test or the Mann–Whitney U test for continuous variables, and chi-square or Fisher’s exact tests for categorical variables. Survival differences were assessed using Kaplan–Meier analysis with log-rank testing. The discriminative ability of RDW was evaluated using receiver operating characteristic (ROC) curves, with area under the curve (AUC), optimal cut-off values, sensitivity, and specificity reported. Precision–recall curves and calibration plots were used to further assess diagnostic performance. Associations between RDW and mortality were examined using Cox proportional hazards regression models. RDW was entered into models as both a continuous variable (per 1% increase) and a categorical variable (high vs. low, defined by the median). Three models were specified: Model 1 (unadjusted), Model 2 (adjusted for age and sex), and Model 3 (further adjusted for demographics, comorbidities, laboratory values, and inflammatory indices). Binary logistic regression was performed as a complementary analysis to evaluate predictors of mortality. Restricted cubic spline regression with four knots was applied to investigate potential non-linear associations between RDW and mortality risk. Subgroup analyses were conducted according to sex, age (< 65 vs. ≥65 years), hypertension, and sepsis status, and likelihood ratio testing was used to assess potential interactions. Incremental prognostic utility of RDW was evaluated by calculating changes in AUC, net reclassification index (NRI), and integrated discrimination improvement (IDI) after adding RDW to baseline models. Decision curve analysis was applied to assess the net clinical benefit of incorporating RDW into prognostic models. Robustness of findings was tested using competing risk regression (Fine–Gray model), sensitivity analyses excluding septic patients and those with ICU stay shorter than 3 days, and leave-one-covariate-out approaches. Collinearity was assessed using variance inflation factors (VIF), with a threshold of < 5 indicating acceptable levels. Principal component analysis (PCA) was conducted to evaluate clustering of RDW with other clinical and laboratory markers. All statistical tests were two-sided, and a p-value < 0.05 was considered statistically significant. Analyses were performed using R software (version 4.4.3) and SPSS (version 27.0). RESULTS Baseline Characteristics A total of 450 patients with acute pancreatitis were included after applying the eligibility criteria (Fig. 1 A). The median red blood cell distribution width (RDW) at ICU admission was 14.7%, with optimal ROC-derived thresholds of 14.65% for 28-day and 14.75% for 90-day mortality (Fig. 1 B). Patients with high RDW were older, more frequently septic, and more likely to have hypertension than those with low RDW (Table 1 ). They also demonstrated lower hemoglobin and RBC counts, higher creatinine and urea nitrogen, elevated bilirubin, and prolonged coagulation indices (INR). Table 1 Demographic, Clinical, and Laboratory Characteristics by RDW Group Variable Overall (N = 450) Low RDW (n = 231) High RDW (n = 219) P value Demographics Age, years (mean ± SD) 55.08 ± 18.32 52.06 ± 18.66 58.26 ± 17.43 0.001 Male sex, n (%) 246 (54.7) 128 (55.4) 118 (53.9) 0.777 Female sex, n (%) 204 (45.3) 103 (44.6) 101 (46.1) — Comorbidities Sepsis, n (%) 111 (24.7) 36 (15.6) 75 (34.3) < 0.001 Hypertension, n (%) 208 (46.2) 95 (41.1) 113 (51.6) 0.030 Clinical outcomes Hospital LOS, days 11.79 ± 16.63 8.99 ± 13.58 14.74 ± 18.92 < 0.001 ICU LOS, days — — — — Hematology Hemoglobin, g/dL 11.64 ± 2.73 12.64 ± 2.51 10.58 ± 2.56 < 0.001 RBC (×10⁶/µL) 3.89 ± 0.88 4.14 ± 0.79 3.63 ± 0.97 < 0.001 RDW, % 14.69 ± 2.61 12.93 ± 0.59 16.56 ± 2.62 < 0.001 Platelet count, K/µL 225.74 ± 111.36 223.71 ± 85.08 227.88 ± 133.79 0.268 WBC, K/µL 12.45 ± 6.60 11.83 ± 5.39 13.11 ± 7.64 0.227 Lymphocyte count, K/µL 1.30 ± 1.01 1.38 ± 1.08 1.21 ± 0.93 0.003 Neutrophil count, K/µL 10.08 ± 6.22 9.41 ± 5.13 10.79 ± 7.13 0.146 Monocyte count, K/µL 0.81 ± 0.49 0.78 ± 0.41 0.84 ± 0.57 0.498 Biochemistry & coagulation Creatinine, mg/dL 1.33 ± 1.37 0.98 ± 0.65 1.70 ± 1.77 < 0.001 Urea nitrogen, mg/dL 20.93 ± 21.36 14.61 ± 9.75 27.59 ± 27.44 < 0.001 Total bilirubin, mg/dL 2.98 ± 5.81 1.85 ± 2.46 4.16 ± 7.78 0.024 INR 1.21 ± 0.99 1.06 ± 0.83 1.37 ± 1.11 < 0.001 Abbreviations: RDW, red blood cell distribution width; LOS, length of stay; RBC, red blood cells; WBC, white blood cells; INR, international normalized ratio. When stratified by 28-day survival, non-survivors were older, more often septic, and exhibited significantly worse laboratory parameters, including higher RDW, lower hemoglobin, and higher bilirubin and creatinine compared with survivors (Table 2 ). Boxplots confirmed higher RDW in both 28-day and 90-day non-survivors (Fig. 1 C–D). Table 2 Baseline Characteristics by 28-Day Survival Status Variable Survivors (n = 411) Non-survivors (n = 39) P value Age, years 54.49 ± 18.27 61.36 ± 17.85 0.032 Male sex, n (%) 225 (54.7) 21 (53.9) 0.914 Female sex, n (%) 186 (45.3) 18 (46.1) — Sepsis, n (%) 80 (19.5) 31 (79.5) < 0.001 Hypertension, n (%) 188 (45.7) 20 (51.3) 0.507 Hemoglobin, g/dL 11.82 ± 2.68 9.69 ± 2.56 < 0.001 RBC (×10⁶/µL) 3.97 ± 0.88 3.12 ± 0.99 < 0.001 RDW, % 14.37 ± 2.20 18.17 ± 3.83 < 0.001 Platelet count, K/µL 228.21 ± 107.13 199.69 ± 148.19 0.008 WBC, K/µL 11.92 ± 5.86 18.05 ± 10.45 < 0.001 Neutrophil count, K/µL 9.60 ± 5.65 15.15 ± 9.18 < 0.001 Lymphocyte count, K/µL 1.32 ± 1.02 1.12 ± 0.93 0.032 Creatinine, mg/dL 1.29 ± 1.26 2.30 ± 1.92 < 0.001 Urea nitrogen, mg/dL 18.81 ± 18.01 43.23 ± 36.52 < 0.001 Total bilirubin, mg/dL 2.36 ± 4.29 9.53 ± 12.37 < 0.001 INR 1.16 ± 0.99 1.72 ± 0.88 < 0.001 Hospital LOS, days 11.49 ± 17.22 14.87 ± 7.56 < 0.001 Abbreviations: RDW, red blood cell distribution width; RBC, red blood cells; WBC, white blood cells; LOS, length of stay; INR, international normalized ratio. Survival Analysis Kaplan–Meier analyses showed significantly lower survival among patients with high RDW at both 28 and 90 days (Fig. 2 A–B). Further stratification demonstrated stepwise increases in mortality with higher neutrophil-to-lymphocyte ratio (NLR) and with higher RDW:Ca ratio quartiles, reflecting the combined effects of systemic inflammation and hematologic–metabolic derangements (Fig. 2 C–D). Predictive Accuracy of RDW RDW exhibited strong discriminatory ability for mortality, with AUCs of 0.837 for 28-day and 0.807 for 90-day outcomes (Table 3 ; Fig. 3 A–B). Precision–recall curves supported its predictive value at clinically relevant thresholds (Fig. 3 C), and calibration plots indicated improved model performance when RDW was incorporated into predictive models (Fig. 3 D). Table 3 Diagnostic Performance of RDW for Mortality Prediction (ROC Analysis) Outcome AUC (95% CI) P value Optimal cut-off (%) Sensitivity (%) Specificity (%) 28-day mortality 0.837 (0.779–0.895) < 0.001 14.65 82.1 76.5 90-day mortality 0.807 (0.750–0.864) < 0.001 14.75 78.4 73.8 Abbreviations: RDW, red blood cell distribution width; ROC, receiver operating characteristic; AUC, area under the curve; CI, confidence interval. Dose–Response and Multivariable Analyses Restricted cubic spline models demonstrated a near-linear association between RDW and 28-day mortality (p for non-linearity = 0.137) and a significant non-linear association with 90-day. Interaction plots showed stronger RDW-related risk in older patients, and a heatmap revealed additive risk when RDW was elevated alongside systemic inflammation (Fig. 4 C–D). Cox regression confirmed RDW as an independent predictor of mortality. Each 1% increase in RDW was associated with approximately 30–36% higher 28-day mortality risk across models, with similar associations for 90-day mortality (Table 4 ). Forest plots illustrated RDW’s independent effect alongside age, sepsis, and organ dysfunction composites (Fig. 5 A–B). Incremental prognostic analyses showed significant improvements in AUC and net reclassification index (NRI) when RDW was added to baseline models, and decision curve analysis demonstrated greater net clinical benefit across a range of thresholds (Fig. 5 C–D). Table 4 Cox Proportional Hazards Analysis of RDW and Mortality Model RDW (continuous, per 1% ↑) HR (95% CI) P value High vs Low RDW HR (95% CI) P value 28-day mortality Model 1 (unadjusted) 1.323 (1.237–1.415) < 0.001 13.72 (4.23–44.55) 0.007 Model 2 (age/sex adjusted) 1.363 (1.262–1.473) < 0.001 12.76 (3.91–41.58) 0.001 Model 3 (fully adjusted) 1.308 (1.145–1.495) < 0.001 10.21 (2.72–38.36) 0.007 90-day mortality Model 1 (unadjusted) 1.300 (1.229–1.375) < 0.001 6.91 (3.53–13.53) < 0.001 Model 2 (age/sex adjusted) 1.327 (1.247–1.411) < 0.001 6.38 (3.25–12.54) < 0.001 Model 3 (fully adjusted) 1.276 (1.155–1.409) < 0.001 5.03 (2.31–10.98) < 0.001 Model 1 = unadjusted; Model 2 = adjusted for age and sex; Model 3 = fully adjusted for comorbidities, laboratory variables, and inflammatory indices. Abbreviations: HR, hazard ratio; CI, confidence interval; RDW, red blood cell distribution width. Subgroup Analyses The prognostic value of RDW was consistent across sex and sepsis subgroups (Table 5 ; Fig. 6 ). Associations were stronger in patients aged ≥ 65 years and in those without hypertension, with significant interaction p-values for hypertension, indicating effect modification. Table 5 Subgroup Analysis of RDW and Mortality Subgroup 28-day Mortality HR (95% CI) P interaction 90-day Mortality HR (95% CI) P interaction Sex Male 1.85 (1.29–2.66) 0.642 1.60 (1.30–1.97) 0.473 Female 1.43 (1.05–1.95) — 1.34 (1.10–1.63) — Age ≥ 65 years 5.11 (1.31–19.89) 0.055 1.78 (1.21–2.62) 0.192 < 65 years 1.37 (1.10–1.70) — 1.24 (1.07–1.44) — Hypertension Present 1.13 (0.88–1.45) 0.007 1.06 (0.85–1.33) 0.005 Absent 2.64 (1.61–4.35) — 1.82 (1.48–2.24) — Sepsis Present 1.67 (1.34–2.09) 0.731 1.21 (0.94–1.56) 0.929 Absent 0.15 (0.002–13.61) — 1.57 (1.32–1.87) — Hazard ratios from fully adjusted Cox models. Interaction P values test for effect modification. Abbreviations: HR, hazard ratio; CI, confidence interval; RDW, red blood cell distribution width. Resource Utilization and Organ Stress High RDW was associated with longer ICU and hospital stays (Fig. 7 A–B). Patients with elevated RDW demonstrated greater renal, hepatic, and coagulation stress, as reflected in creatinine, bilirubin, and INR distributions (Fig. 7 C), and RDW showed a positive correlation with hospital length of stay (Fig. 7 D). Robustness and Collinearity Analyses Competing risk analysis confirmed persistent associations between RDW and mortality after accounting for discharge alive as a competing outcome (Fig. 8 A). Sensitivity analyses excluding septic patients or those with very short hospitalizations (< 3 days) yielded consistent results (Fig. 8 B–C), and leave-one-covariate-out analyses confirmed the stability of RDW’s effect (Fig. 8 D). Correlation analysis showed clustering of RDW with inflammatory and organ dysfunction markers (Fig. 9 A), but variance inflation factors were all < 5, indicating no concerning collinearity (Fig. 9 B). Principal component analysis further demonstrated RDW’s integration with the multivariable risk structure (Fig. 9 C). Supplementary Analyses Derived indices are summarized in Supplementary Table S1 . Missingness of variables was minimal (< 5% for most), except for PT, PTT, and INR (~ 20%), which were imputed ( Supplementary Table S2 ). Univariate logistic regression confirmed significant associations of RDW, sepsis, renal and hepatic markers, and inflammatory indices with mortality, supporting the Cox regression findings ( Supplementary Table S3 ). DISCUSSION Principal Findings In this large retrospective cohort of critically ill patients with acute pancreatitis (AP), we demonstrated that red blood cell distribution width (RDW) at intensive care unit (ICU) admission is a strong and independent predictor of short- and long-term mortality. Patients with elevated RDW had significantly worse biochemical profiles, longer ICU and hospital stays, and higher burden of renal, hepatic, and coagulation dysfunction. Importantly, RDW improved the discriminative performance of conventional prognostic models and provided incremental clinical benefit, as shown by net reclassification index (NRI) and decision curve analysis. These findings highlight RDW as a practical, low-cost, and readily available biomarker that captures systemic illness severity in AP. Comparison with Previous Studies Our findings are consistent with earlier reports linking RDW to adverse outcomes in AP. Şenol et al. (2013) first identified elevated RDW as a predictor of in-hospital mortality. Wang et al. (2015) subsequently confirmed this in a larger cohort, showing a dose–response relationship between RDW and mortality. More recent studies using intensive care populations have validated these results: He et al. (2023) found RDW independently predicted mortality even after propensity score matching, while Singh et al. (2020) demonstrated strong correlations between RDW and established severity scores. Song et al. (2025) extended these findings by showing that RDW predicted both short- and long-term mortality in patients with AP complicated by sepsis. Our study builds on this evidence by employing a high-resolution dataset, advanced statistical methods, and robust sensitivity analyses, confirming RDW’s value as a mortality predictor at both 28 and 90 days. Outside pancreatitis, RDW has emerged as a reliable prognostic marker across multiple conditions. In sepsis, elevated RDW correlates with organ dysfunction and mortality [(Bazick et al., 2011; Jo et al., 2013; Song et al., 2025)]. In cardiovascular disease, RDW is an independent predictor of poor outcomes [(Patel et al., 2009; Braun et al., 2011; Makhoul et al., 2013)], and in pneumonia, it predicts early death with accuracy comparable to traditional risk scores [(Braun et al., 2011; Hong et al., 2012)]. During the COVID-19 pandemic, RDW was widely validated as a marker of mortality risk [(Foy et al., 2020)]. These consistent findings across diverse disease states suggest that RDW reflects systemic pathophysiological processes relevant to acute critical illness. Our study also complements work on RDW-derived ratios. Gravito-Soares et al. (2018) reported that the RDW-to-calcium ratio (RDW:Ca) was superior to RDW alone in predicting AP severity. Acehan et al. (2024) and He et al. (2025) proposed RDW-to-albumin (RDW/Alb) as a composite index with higher prognostic accuracy. Similarly, our results indicate that RDW interacts synergistically with inflammatory indices, suggesting that future models integrating RDW with systemic inflammation markers may offer optimal predictive performance. Potential Mechanisms Several mechanisms may underlie the observed association between elevated RDW and mortality in AP. First, RDW reflects anisocytosis due to inflammation-induced impairment of erythropoiesis. Proinflammatory cytokines such as interleukin-6 and tumor necrosis factor-α disrupt iron metabolism and suppress erythropoietin activity, resulting in heterogeneous red blood cell size [(Lippi et al., 2009; Salvagno et al., 2015)]. Second, oxidative stress—a central feature of AP—damages erythrocyte membranes and reduces their lifespan, thereby increasing RDW [(Bazick et al., 2011; Braun et al., 2011)]. Third, metabolic disturbances, particularly hypocalcemia and hypoalbuminemia, exacerbate anisocytosis, which may explain why RDW:Ca and RDW/Alb ratios are powerful prognostic markers [(Gravito-Soares et al., 2018; He et al., 2025)]. Finally, elevated RDW has been linked to endothelial dysfunction, impaired microcirculation, and multiorgan failure, which are key determinants of adverse outcomes in severe AP [(Makhoul et al., 2013; Braun et al., 2011)]. Together, these mechanisms underscore RDW as an integrative biomarker reflecting the interplay between inflammation, metabolic imbalance, and organ dysfunction. Clinical Implications Our findings carry several clinical implications. First, RDW represents a simple, universally available biomarker that can be rapidly incorporated into prognostic assessment at the time of ICU admission. Unlike scoring systems such as APACHE II, BISAP, or SOFA, which require numerous variables and time for calculation, RDW can be obtained from routine blood counts at no additional cost [(Hagjer et al., 2018; Gao et al., 2015; Tee et al., 2018)]. In resource-limited settings, this makes RDW particularly attractive as a first-line risk stratification tool. Second, RDW could serve as a triage marker for early identification of high-risk patients. Those with elevated RDW might benefit from closer monitoring, more aggressive supportive care, and early transfer to higher-level facilities. Third, RDW may complement existing scores rather than replace them. Our study showed that incorporating RDW into conventional models improved both discrimination and clinical decision benefit. This suggests that hybrid models integrating RDW with clinical indices and inflammatory markers could provide the most accurate prognostic information. Finally, RDW could aid in risk communication with patients and families. Because it is an easily understandable parameter routinely reported in laboratory results, clinicians may use RDW to communicate prognosis in a straightforward manner. Future Directions Future research should address several important questions. Prospective multicenter studies are needed to validate RDW as a prognostic marker across diverse healthcare systems and populations, including low-resource settings where simple markers are most valuable. Serial measurement of RDW over the course of hospitalization may provide dynamic prognostic information, as has been demonstrated in sepsis and COVID-19 [(Jo et al., 2013; Foy et al., 2020)]. Further work should also evaluate the role of RDW-derived ratios, such as RDW:Ca and RDW/Alb, and test whether they outperform RDW alone in risk prediction. Integration of RDW into machine learning–based predictive models is another promising avenue. Recent studies using artificial intelligence have shown that combining hematologic indices with clinical and biochemical parameters improves prognostic performance in AP [(Surgery Open Science, 2024)]. Exploring whether RDW can enhance these algorithms could advance precision medicine approaches in pancreatitis. Finally, mechanistic studies are warranted to clarify whether interventions targeting inflammation, oxidative stress, or metabolic imbalance can modulate RDW and thereby improve outcomes. Strengths and Limitations The major strengths of this study include its use of the large and validated MIMIC-IV database, comprehensive statistical modeling, and extensive sensitivity analyses. By assessing both short- and long-term outcomes, our analysis provides a nuanced understanding of RDW’s prognostic role. Nonetheless, several limitations should be acknowledged. First, as a retrospective study, residual confounding cannot be excluded despite careful adjustment. Second, RDW is influenced by multiple conditions, including anemia, nutritional deficiencies, and hematologic disorders, which were not fully captured in our dataset [(Patel et al., 2009; Lippi et al., 2009)]. Third, because RDW is a nonspecific marker, it may reflect general systemic illness rather than pancreatitis-specific pathology. Fourth, our cohort was derived from a single U.S. tertiary care center, limiting external generalizability. Finally, dynamic changes in RDW during hospitalization were not evaluated, though they may provide additional prognostic insight. Conclusion In conclusion, this study demonstrates that elevated RDW at ICU admission is a strong and independent predictor of both short- and long-term mortality in patients with acute pancreatitis. RDW enhances risk prediction when added to conventional scoring systems and reflects the integrated burden of systemic inflammation, oxidative stress, and metabolic derangements. Given its availability, cost-effectiveness, and simplicity, RDW has considerable potential as a practical biomarker for early risk stratification. Prospective, multicenter studies and mechanistic investigations are warranted to confirm these findings and to explore whether integrating RDW into clinical pathways can improve outcomes in acute pancreatitis. Declarations Consent for Publication Not applicable. Ethics Approval and Consent to Participate This study was conducted in accordance with the ethical principles of the Declaration of Helsinki (revised 2013) and was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology. Access to the MIMIC-IV database was granted to the investigators after successful completion of the National Institutes of Health (NIH) online training course and certification in human research participant protection. As the MIMIC-IV database is de-identified and publicly available, additional institutional ethical approval and informed consent were not required. Competing Interests The authors declare that they have no competing interests. Funding This study did not receive any financial support. Author Contribution Author ContributionsQian Xiao: Conceptualization, study design, data extraction, statistical analysis, interpretation of results, manuscript drafting, and critical revision.Yin Chen: Data collection, literature review, validation, visualization, and manuscript editing.Both authors read and approved the final version of the manuscript. Acknowledgements Not applicable. Data Availability Data available into the manuscript References Acehan, F., Yıldırım, M., Akbaş, A., Öztürk, A., Kocaman, O., & Çolak, Y. (2024). The red cell distribution width-to-albumin ratio as a prognostic marker in acute pancreatitis. 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10:06:54","extension":"xml","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170153,"visible":true,"origin":"","legend":"","description":"","filename":"d143d105e644464d86718173bda427361structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/53254e4c1c6e80257b5e27d7.xml"},{"id":95189916,"identity":"6ba11fd9-36e1-411d-a38b-f7f631e4aec3","added_by":"auto","created_at":"2025-11-05 10:06:53","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185463,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/947419d88715291e9efcb67c.html"},{"id":95228601,"identity":"e68ac4d1-520f-4f36-a003-fe777790172a","added_by":"auto","created_at":"2025-11-05 16:33:59","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":783525,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCohort profile and baseline RDW signal in acute pancreatitis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) \u003cem\u003eStudy workflow summarizing the design, population selection, data collection, outcomes, statistical analyses, and key findings of the retrospective cohort study on the prognostic value of red blood cell distribution width (RDW) in critically ill patients with acute pancreatitis.\u003cbr\u003e\n \u003c/em\u003e(B) Distribution of red blood cell distribution width (RDW) at ICU admission with median (red dashed line) and optimal ROC cut-offs for 28-day (green dotted line) and 90-day mortality (purple dotted line).\u003cbr\u003e\n(C) Boxplot of RDW stratified by 28-day survival status.\u003cbr\u003e\n(D) Boxplot of RDW stratified by 90-day survival status.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: RDW, red blood cell distribution width; ROC, receiver operating characteristic.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/6b219c018f94b72d6c8b8159.jpeg"},{"id":95189892,"identity":"0fcfdf06-32ed-4e32-bdd7-211afec13cb5","added_by":"auto","created_at":"2025-11-05 10:06:53","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":538446,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival by RDW and inflammatory/metabolic milieu in acute pancreatitis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Kaplan–Meier survival curves for 28-day all-cause mortality, showing significantly lower survival in patients with high RDW compared to low RDW (\u003cem\u003elog-rank\u003c/em\u003e P \u0026lt; 0.001).\u003cbr\u003e\n(B) Kaplan–Meier survival curves for 90-day all-cause mortality, with persistent separation between high- and low-RDW groups (\u003cem\u003elog-rank\u003c/em\u003e P \u0026lt; 0.001).\u003cbr\u003e\n(C) Kaplan–Meier survival curves stratified by tertiles of the neutrophil-to-lymphocyte ratio (NLR), illustrating stepwise worsening survival with higher systemic inflammation.\u003cbr\u003e\n(D) Kaplan–Meier survival curves stratified by quartiles of the RDW:Ca ratio, demonstrating increased mortality risk across combined hematologic–metabolic derangements.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: RDW, red blood cell distribution width; NLR, neutrophil-to-lymphocyte ratio; RDW:Ca, ratio of RDW (%) to serum calcium (mg/dL)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/c2da40fd6088fb794f870119.jpeg"},{"id":95189893,"identity":"d97c2da0-4cd6-44cb-bcfa-c82f9839fcd2","added_by":"auto","created_at":"2025-11-05 10:06:53","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":573345,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic discrimination and calibration of RDW for mortality in acute pancreatitis\u003c/strong\u003e\u003cbr\u003e\n(A) Receiver operating characteristic (ROC) curve for RDW predicting 28-day all-cause mortality, showing an area under the curve (AUC) of 0.837 with an optimal cut-off of 14.65%.\u003cbr\u003e\n(B) ROC curve for RDW predicting 90-day mortality, with an AUC of 0.807 and optimal cut-off of 14.75%.\u003cbr\u003e\n(C) Precision–recall curves for RDW in predicting 28-day and 90-day mortality, highlighting improved recall at clinically relevant thresholds.\u003cbr\u003e\n(D) Calibration plots comparing predicted versus observed mortality across deciles of risk in models with and without RDW, demonstrating better calibration when RDW is included.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: RDW, red blood cell distribution width; ROC, receiver operating characteristic; AUC, area under the curve.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/f3f00aa66f6bca422a99e44d.jpeg"},{"id":95229196,"identity":"ba3064b5-809f-4f53-904c-36b8b43d0229","added_by":"auto","created_at":"2025-11-05 16:34:35","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":553850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDose–response relationship and interactions of RDW with mortality risk in acute pancreatitis\u003c/strong\u003e\u003cbr\u003e\n(A) Restricted cubic spline (RCS) model of the association between RDW and 28-day mortality, showing a near-linear increase in risk across the RDW distribution (non-linearity \u003cem\u003eP\u003c/em\u003e = 0.137).\u003cbr\u003e\n(B) RCS model for 90-day mortality, indicating a significant non-linear association (\u003cem\u003eP\u003c/em\u003e = 0.015).\u003cbr\u003e\n(C) Interaction plot of RDW and age strata (≤65 vs \u0026gt;65 years), demonstrating stronger risk effects in older patients (interaction \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003cbr\u003e\n(D) Heatmap of adjusted hazard ratios across the joint distribution of RDW and NLR, highlighting combined hematologic and inflammatory risk.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: RDW, red blood cell distribution width; RCS, restricted cubic spline; NLR, neutrophil-to-lymphocyte ratio; HR, hazard ratio.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/b330c11636a4285c78193051.jpeg"},{"id":95189900,"identity":"6ad5889e-e1ad-4219-a346-b496329fed1b","added_by":"auto","created_at":"2025-11-05 10:06:53","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":609139,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultivariable models and incremental prognostic value of RDW in acute pancreatitis\u003c/strong\u003e\u003cbr\u003e\n(A) Forest plot from the fully adjusted multivariable Cox model (Model 3) showing hazard ratios for RDW (continuous) and major covariates (age, sepsis, renal, hepatic, and coagulation composites).\u003cbr\u003e\n(B) Forest plot comparing mortality risk in patients with high versus low RDW, demonstrating significantly increased hazard in the high-RDW group.\u003cbr\u003e\n(C) Incremental prognostic performance metrics when RDW is added to the baseline model, showing improvement in area under the curve (ΔAUC) and net reclassification index (NRI).\u003cbr\u003e\n(D) Decision curve analysis comparing models with and without RDW, indicating greater net clinical benefit across a range of threshold probabilities when RDW is included.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: RDW, red blood cell distribution width; HR, hazard ratio; CI, confidence interval; ΔAUC, change in area under the curve; NRI, net reclassification index.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/1fc10bc79e8f36b35140cd11.jpeg"},{"id":95228996,"identity":"d851eb4c-a950-46b6-8bd2-63f46964641a","added_by":"auto","created_at":"2025-11-05 16:34:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88066,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analyses of RDW and mortality risk in acute pancreatitis\u003c/strong\u003e\u003cbr\u003e\nForest plot of RDW-associated hazard ratios for 28-day (circles) and 90-day (squares) mortality across key subgroups. Elevated RDW was consistently associated with higher mortality, with stronger effects observed in patients \u0026gt;65 years and those with sepsis, while the association was attenuated in patients with hypertension.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: RDW, red blood cell distribution width; HR, hazard ratio; CI, confidence interval.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/251ec269441955338262c7d8.png"},{"id":95189914,"identity":"663ce30d-1d84-4d01-9d25-087852ecc214","added_by":"auto","created_at":"2025-11-05 10:06:53","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":746947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResource utilization and organ stress profiles by RDW group in acute pancreatitis\u003c/strong\u003e\u003cbr\u003e\n(A) Intensive care unit (ICU) length of stay (LOS) by RDW group, showing prolonged ICU stay among patients with high RDW.\u003cbr\u003e\n(B) Total hospital LOS by RDW group, demonstrating a clear gradient toward longer hospitalization in the high-RDW group.\u003cbr\u003e\n(C) Density plots of creatinine, bilirubin, and INR distributions stratified by RDW group, indicating greater renal, hepatic, and coagulation stress in patients with high RDW.\u003cbr\u003e\n(D) Scatterplot of RDW versus hospital LOS with polynomial trend line, illustrating a positive association between RDW levels and length of hospitalization.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: RDW, red blood cell distribution width; LOS, length of stay; INR, international normalized ratio.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/5ddcc40f3383d0db5c9b8001.jpeg"},{"id":95189911,"identity":"2702953f-1e4b-4b6e-958d-9b5185c16ab7","added_by":"auto","created_at":"2025-11-05 10:06:53","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":478518,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCompeting risks and robustness analyses of RDW and mortality in acute pancreatitis\u003c/strong\u003e\u003cbr\u003e\n(A) Cumulative incidence functions for in-hospital death versus discharge alive, demonstrating the competing nature of early outcomes.\u003cbr\u003e\n(B) Sensitivity analysis excluding patients with sepsis at baseline, showing consistent associations of RDW with mortality risk.\u003cbr\u003e\n(C) Sensitivity analysis excluding patients with hospital length of stay (LOS) \u0026lt; 3 days, confirming robustness of RDW–mortality associations.\u003cbr\u003e\n(D) Leave-one-covariate-out analysis showing the change in RDW hazard ratio (ΔHR) when individual covariates are removed from the fully adjusted model, indicating stability of the RDW effect.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: RDW, red blood cell distribution width; LOS, length of stay; HR, hazard ratio; CI, confidence interval.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/26b5cfc590d11c4d1f3a8a6a.jpeg"},{"id":95189912,"identity":"9e786985-3f6b-4359-90cb-f8ff3ed40e97","added_by":"auto","created_at":"2025-11-05 10:06:53","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":726878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation structure, collinearity, and variable relationships in acute pancreatitis\u003c/strong\u003e\u003cbr\u003e\n(A) Spearman correlation heatmap among RDW, inflammatory indices (NLR, PLR, LMR, PIV, SII, SIRI), and laboratory variables (creatinine, BUN, bilirubin, INR, platelets, WBC), highlighting clusters of related markers.\u003cbr\u003e\n(B) Variance inflation factors (VIF) for covariates included in the multivariable model, demonstrating acceptable collinearity (all VIF \u0026lt; 5).\u003cbr\u003e\n(C) Principal component analysis (PCA) biplot illustrating grouping of inflammatory versus organ dysfunction markers, with RDW loading highlighted within the multivariable structure.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: RDW, red blood cell distribution width; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; PIV, pan-immune-inflammation value; SII, systemic immune-inflammation index; SIRI, systemic inflammatory response index; BUN, blood urea nitrogen; INR, international normalized ratio; VIF, variance inflation factor; PCA, principal component analysis.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/a4f9c63169faccb76de18327.jpeg"},{"id":95315085,"identity":"935e7845-edb7-4619-87db-c655ce5e4c82","added_by":"auto","created_at":"2025-11-06 15:53:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6596231,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/b4ccd604-9e60-4d4f-afe7-b5460aad44ca.pdf"},{"id":95189890,"identity":"a0a1fef8-eec9-45ab-8ab3-8903efedbc16","added_by":"auto","created_at":"2025-11-05 10:06:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19168,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7723696/v1/033ab00a2224515e9093d2ad.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value of Red Blood Cell Distribution Width for Mortality in Critically Ill Patients with Acute Pancreatitis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAcute pancreatitis (AP) is a common and potentially life-threatening gastrointestinal emergency with a global incidence of 13\u0026ndash;45 cases per 100,000 individuals annually [(Song et al., 2025; Gravito-Soares et al., 2018)]. While the majority of patients experience a mild, self-limited course, up to 20% progress to severe disease, characterized by persistent organ failure, systemic inflammatory response syndrome (SIRS), and sepsis. In these cases, mortality can reach 30\u0026ndash;40% despite advances in intensive care medicine [(Parsi et al., 2025; Carnovale et al., 2005)]. The unpredictable trajectory of AP complicates timely clinical decision-making, highlighting the urgent need for reliable early prognostic tools.\u003c/p\u003e\u003cp\u003eOver the past decades, several scoring systems and biomarkers have been developed to predict outcomes in AP. Ranson\u0026rsquo;s criteria, the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Bedside Index of Severity in Acute Pancreatitis (BISAP), and the Sequential Organ Failure Assessment (SOFA) score remain the most widely used models [(Hagjer et al., 2018; Gao et al., 2015; Tee et al., 2018)]. While clinically informative, these scores are hindered by practical limitations: they require numerous laboratory parameters, complex calculations, and in some cases prolonged 48-hour observation periods, thereby delaying risk stratification [(Chen et al., 2013; Dancu et al., 2021)]. Furthermore, their predictive accuracy is inconsistent across populations and healthcare systems, reducing their utility as universal tools [(Goyal et al., 2017; WJG Surgery Review, 2020)]. As a result, there is growing interest in simple, rapid, and inexpensive biomarkers that can accurately identify high-risk patients at the point of care.\u003c/p\u003e\u003cp\u003eRed blood cell distribution width (RDW), an automated measure of variability in erythrocyte size, has traditionally been used in the differential diagnosis of anemia. More recently, it has emerged as a promising biomarker of systemic inflammation and physiological stress [(Patel et al., 2009; Hunziker et al., 2012)]. Elevated RDW reflects inflammatory cytokine activity, oxidative stress, nutritional deficiencies, and impaired erythropoiesis, all of which are relevant to the pathogenesis of AP and its systemic complications [(Braun et al., 2011; Makhoul et al., 2013)]. Importantly, RDW is universally available from the complete blood count, incurs no additional cost, and can be obtained at admission, making it an attractive candidate for early prognostication in critically ill patients.\u003c/p\u003e\u003cp\u003eEvidence linking RDW to adverse outcomes has accumulated across multiple clinical settings. Higher RDW is associated with increased mortality in sepsis, pneumonia, cardiovascular disease, and acute decompensated heart failure [(Hunziker et al., 2012; Braun et al., 2011; Hong et al., 2012; Makhoul et al., 2013)]. In community-acquired pneumonia, for example, RDW outperformed several traditional risk factors as a predictor of poor outcome [(Braun et al., 2011)], while in critically ill populations it improved the discriminative ability of established severity scores [(Hunziker et al., 2012)]. These observations suggest that RDW functions as a global marker of systemic stress and inflammation, supporting its investigation in AP.\u003c/p\u003e\u003cp\u003eIn pancreatitis specifically, RDW has shown consistent associations with disease severity and mortality. Şenol and colleagues (2013) first reported that elevated RDW independently predicted mortality in patients with AP. A larger cohort study by Wang et al. (2015) confirmed this finding, demonstrating that higher RDW was strongly linked to in-hospital death. Subsequent research has extended these observations: He et al. (2023) showed in a propensity-matched analysis of U.S. intensive care patients that RDW independently predicted in-hospital mortality; Singh et al. (2020) found that RDW correlated strongly with established severity scores; and Song et al. (2025) reported that RDW was associated with both short- and long-term mortality in AP complicated by sepsis.\u003c/p\u003e\u003cp\u003eBeyond RDW alone, several composite indices have been proposed to enhance predictive performance. Ratios such as RDW-to-albumin (RDW/Alb) and RDW-to-calcium (RDW:Ca) have demonstrated incremental prognostic value, reflecting combined effects of inflammation, nutritional status, and metabolic dysregulation [(Gravito-Soares et al., 2018; He et al., 2025; Acehan et al., 2024)]. Similarly, novel immune-inflammatory indices derived from blood counts\u0026mdash;including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII)\u0026mdash;have shown prognostic potential in AP [(Parsi et al., 2025; Dancu et al., 2021; Chauhan et al., 2021)]. However, their comparative and incremental value relative to RDW remains uncertain, particularly in critically ill cohorts where inflammatory activation and multiorgan dysfunction frequently coexist.\u003c/p\u003e\u003cp\u003eDespite encouraging evidence, important gaps remain in the literature. Many prior studies were single-center, retrospective, or limited by small sample sizes [(Şenol et al., 2013; Singh et al., 2020)]. Others did not assess long-term outcomes such as 90-day mortality or failed to compare RDW directly with established prognostic scores [(Goyal et al., 2017; Gao et al., 2015)]. Furthermore, few studies have rigorously evaluated whether incorporating RDW into conventional risk models meaningfully improves prognostic accuracy through measures such as net reclassification index or decision curve analysis. These knowledge gaps underscore the need for large, high-resolution datasets and robust statistical methods to clarify the clinical utility of RDW in AP.\u003c/p\u003e\u003cp\u003eThe present study therefore aimed to investigate the prognostic significance of RDW in critically ill patients with acute pancreatitis using the MIMIC-IV database. Specifically, we evaluated the association between RDW and 28-day and 90-day all-cause mortality, examined its interaction with systemic inflammatory indices and metabolic derangements, and assessed its incremental prognostic value beyond conventional severity models. By leveraging a large real-world cohort and comprehensive analytical approaches, this study provides new insights into the potential role of RDW as an inexpensive, readily available biomarker for risk stratification in acute pancreatitis.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Data Source\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study was conducted using the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.2) database, a large, freely available resource containing de-identified clinical data from critically ill patients admitted to the Beth Israel Deaconess Medical Center in Boston, Massachusetts. The database includes demographic information, laboratory results, clinical diagnoses, interventions, and outcomes. Data extraction was performed by a certified investigator after completion of the required training and access approval. The study adhered to the principles of the Declaration of Helsinki (2013 revision). Because MIMIC-IV is fully anonymized and publicly accessible, additional institutional review board approval and individual informed consent were not required.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003ePatients were eligible if they had a diagnosis of acute pancreatitis (AP) identified using International Classification of Diseases, Ninth and Tenth Revision (ICD-9/10) codes. Exclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age younger than 18 years at the time of first admission; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) multiple admissions for AP, in which case only the first admission was analyzed; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) presence of end-stage renal disease or hematologic malignancies; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) ICU stay shorter than 48 hours; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) missing baseline measurements of red blood cell distribution width (RDW), platelet count, lymphocyte count, neutrophil count, or monocyte count. After applying these criteria, 450 patients were included in the final cohort. Patients were categorized into high- and low-RDW groups based on the median value of baseline RDW.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eBaseline demographic variables included age and sex. Clinical variables included hypertension and sepsis, with sepsis defined according to the Sepsis-3 criteria. Laboratory parameters recorded within the first 24 hours of ICU admission comprised red blood cell (RBC) count, white blood cell (WBC) count, hemoglobin, platelet count, absolute lymphocyte, neutrophil, and monocyte counts, RDW, prothrombin time (PT), partial thromboplastin time (PTT), international normalized ratio (INR), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), blood urea nitrogen (BUN), total bilirubin, creatinine, glucose, total serum calcium, and serum sodium. If duplicate measurements were available, the earliest result was used.\u003c/p\u003e\u003cp\u003eTo capture systemic inflammation, the following indices were calculated (\u003cb\u003eSupplementary Table S1\u003c/b\u003e):\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eNeutrophil-to-lymphocyte ratio (NLR)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePlatelet-to-lymphocyte ratio (PLR)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLymphocyte-to-monocyte ratio (LMR)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSystemic immune-inflammation index (SII)\u0026thinsp;=\u0026thinsp;Neutrophils \u0026times; Platelets\u0026thinsp;\u0026divide;\u0026thinsp;Lymphocytes\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSystemic inflammation response index (SIRI)\u0026thinsp;=\u0026thinsp;Neutrophils \u0026times; Monocytes\u0026thinsp;\u0026divide;\u0026thinsp;Lymphocytes\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePan-immune-inflammation value (PIV)\u0026thinsp;=\u0026thinsp;Neutrophils \u0026times; Platelets \u0026times; Monocytes\u0026thinsp;\u0026divide;\u0026thinsp;Lymphocytes\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRDW:Ca ratio\u0026thinsp;=\u0026thinsp;RDW (%)\u0026thinsp;\u0026divide;\u0026thinsp;serum calcium (mg/dL)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eOrgan dysfunction composites (renal, hepatic, coagulation, and glycemic stress scores) were derived using standardized z-scores\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcomes were all-cause mortality at 28 days and 90 days following ICU admission. Secondary outcomes included ICU length of stay, hospital length of stay, and organ dysfunction burden as assessed by renal, hepatic, and coagulation indices.\u003c/p\u003e\n\u003ch3\u003eHandling of Missing Data\u003c/h3\u003e\n\u003cp\u003eVariables with more than 30% missingness were excluded from analysis. For variables with less than 30% missingness, multiple imputation by chained equations was performed. The proportion of missingness for each variable is summarized in Supplementary Table S2.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range), depending on distribution, which was assessed using the Shapiro\u0026ndash;Wilk test. Categorical variables were presented as frequencies and percentages. Between-group comparisons were performed using Student\u0026rsquo;s t-test or the Mann\u0026ndash;Whitney U test for continuous variables, and chi-square or Fisher\u0026rsquo;s exact tests for categorical variables.\u003c/p\u003e\u003cp\u003eSurvival differences were assessed using Kaplan\u0026ndash;Meier analysis with log-rank testing. The discriminative ability of RDW was evaluated using receiver operating characteristic (ROC) curves, with area under the curve (AUC), optimal cut-off values, sensitivity, and specificity reported. Precision\u0026ndash;recall curves and calibration plots were used to further assess diagnostic performance.\u003c/p\u003e\u003cp\u003eAssociations between RDW and mortality were examined using Cox proportional hazards regression models. RDW was entered into models as both a continuous variable (per 1% increase) and a categorical variable (high vs. low, defined by the median). Three models were specified: Model 1 (unadjusted), Model 2 (adjusted for age and sex), and Model 3 (further adjusted for demographics, comorbidities, laboratory values, and inflammatory indices). Binary logistic regression was performed as a complementary analysis to evaluate predictors of mortality.\u003c/p\u003e\u003cp\u003eRestricted cubic spline regression with four knots was applied to investigate potential non-linear associations between RDW and mortality risk. Subgroup analyses were conducted according to sex, age (\u0026lt;\u0026thinsp;65 vs. \u0026ge;65 years), hypertension, and sepsis status, and likelihood ratio testing was used to assess potential interactions.\u003c/p\u003e\u003cp\u003eIncremental prognostic utility of RDW was evaluated by calculating changes in AUC, net reclassification index (NRI), and integrated discrimination improvement (IDI) after adding RDW to baseline models. Decision curve analysis was applied to assess the net clinical benefit of incorporating RDW into prognostic models.\u003c/p\u003e\u003cp\u003eRobustness of findings was tested using competing risk regression (Fine\u0026ndash;Gray model), sensitivity analyses excluding septic patients and those with ICU stay shorter than 3 days, and leave-one-covariate-out approaches. Collinearity was assessed using variance inflation factors (VIF), with a threshold of \u0026lt;\u0026thinsp;5 indicating acceptable levels. Principal component analysis (PCA) was conducted to evaluate clustering of RDW with other clinical and laboratory markers.\u003c/p\u003e\u003cp\u003eAll statistical tests were two-sided, and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Analyses were performed using R software (version 4.4.3) and SPSS (version 27.0).\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\u003cp\u003eA total of 450 patients with acute pancreatitis were included after applying the eligibility criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The median red blood cell distribution width (RDW) at ICU admission was 14.7%, with optimal ROC-derived thresholds of 14.65% for 28-day and 14.75% for 90-day mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Patients with high RDW were older, more frequently septic, and more likely to have hypertension than those with low RDW (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). They also demonstrated lower hemoglobin and RBC counts, higher creatinine and urea nitrogen, elevated bilirubin, and prolonged coagulation indices (INR).\u003c/p\u003e\u003cp\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\u003eDemographic, Clinical, and Laboratory Characteristics by RDW Group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;450)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow RDW (n\u0026thinsp;=\u0026thinsp;231)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh RDW (n\u0026thinsp;=\u0026thinsp;219)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.08\u0026thinsp;\u0026plusmn;\u0026thinsp;18.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.06\u0026thinsp;\u0026plusmn;\u0026thinsp;18.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.26\u0026thinsp;\u0026plusmn;\u0026thinsp;17.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale sex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e246 (54.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128 (55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118 (53.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale sex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e204 (45.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103 (44.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101 (46.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSepsis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111 (24.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (15.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75 (34.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e208 (46.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95 (41.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (51.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClinical outcomes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital LOS, days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.79\u0026thinsp;\u0026plusmn;\u0026thinsp;16.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.99\u0026thinsp;\u0026plusmn;\u0026thinsp;13.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.74\u0026thinsp;\u0026plusmn;\u0026thinsp;18.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU LOS, days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHematology\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC (\u0026times;10⁶/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count, K/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e225.74\u0026thinsp;\u0026plusmn;\u0026thinsp;111.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e223.71\u0026thinsp;\u0026plusmn;\u0026thinsp;85.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e227.88\u0026thinsp;\u0026plusmn;\u0026thinsp;133.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, K/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.45\u0026thinsp;\u0026plusmn;\u0026thinsp;6.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.11\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.227\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte count, K/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil count, K/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.08\u0026thinsp;\u0026plusmn;\u0026thinsp;6.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.41\u0026thinsp;\u0026plusmn;\u0026thinsp;5.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.79\u0026thinsp;\u0026plusmn;\u0026thinsp;7.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte count, K/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBiochemistry \u0026amp; coagulation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea nitrogen, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.93\u0026thinsp;\u0026plusmn;\u0026thinsp;21.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.61\u0026thinsp;\u0026plusmn;\u0026thinsp;9.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.59\u0026thinsp;\u0026plusmn;\u0026thinsp;27.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal bilirubin, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;5.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations: RDW, red blood cell distribution width; LOS, length of stay; RBC, red blood cells; WBC, white blood cells; INR, international normalized ratio.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen stratified by 28-day survival, non-survivors were older, more often septic, and exhibited significantly worse laboratory parameters, including higher RDW, lower hemoglobin, and higher bilirubin and creatinine compared with survivors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Boxplots confirmed higher RDW in both 28-day and 90-day non-survivors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u0026ndash;D).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics by 28-Day Survival Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurvivors (n\u0026thinsp;=\u0026thinsp;411)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-survivors (n\u0026thinsp;=\u0026thinsp;39)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.49\u0026thinsp;\u0026plusmn;\u0026thinsp;18.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.36\u0026thinsp;\u0026plusmn;\u0026thinsp;17.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale sex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e225 (54.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (53.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale sex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e186 (45.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (46.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\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\u003e80 (19.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (79.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e188 (45.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (51.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.507\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\u003e11.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC (\u0026times;10⁶/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count, K/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e228.21\u0026thinsp;\u0026plusmn;\u0026thinsp;107.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e199.69\u0026thinsp;\u0026plusmn;\u0026thinsp;148.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, K/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.92\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.05\u0026thinsp;\u0026plusmn;\u0026thinsp;10.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil count, K/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.60\u0026thinsp;\u0026plusmn;\u0026thinsp;5.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.15\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte count, K/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.032\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\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea nitrogen, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.81\u0026thinsp;\u0026plusmn;\u0026thinsp;18.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.23\u0026thinsp;\u0026plusmn;\u0026thinsp;36.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal bilirubin, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.53\u0026thinsp;\u0026plusmn;\u0026thinsp;12.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital LOS, days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.49\u0026thinsp;\u0026plusmn;\u0026thinsp;17.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.87\u0026thinsp;\u0026plusmn;\u0026thinsp;7.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eAbbreviations: RDW, red blood cell distribution width; RBC, red blood cells; WBC, white blood cells; LOS, length of stay; INR, international normalized ratio.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSurvival Analysis\u003c/h2\u003e\u003cp\u003eKaplan\u0026ndash;Meier analyses showed significantly lower survival among patients with high RDW at both 28 and 90 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;B). Further stratification demonstrated stepwise increases in mortality with higher neutrophil-to-lymphocyte ratio (NLR) and with higher RDW:Ca ratio quartiles, reflecting the combined effects of systemic inflammation and hematologic\u0026ndash;metabolic derangements (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u0026ndash;D).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePredictive Accuracy of RDW\u003c/h2\u003e\u003cp\u003eRDW exhibited strong discriminatory ability for mortality, with AUCs of 0.837 for 28-day and 0.807 for 90-day outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;B). Precision\u0026ndash;recall curves supported its predictive value at clinically relevant thresholds (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and calibration plots indicated improved model performance when RDW was incorporated into predictive models (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\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\u003eDiagnostic Performance of RDW for Mortality Prediction (ROC Analysis)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOptimal cut-off (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28-day mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.837 (0.779\u0026ndash;0.895)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e76.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e90-day mortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.807 (0.750\u0026ndash;0.864)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e78.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e73.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eAbbreviations: RDW, red blood cell distribution width; ROC, receiver operating characteristic; AUC, area under the curve; CI, confidence interval.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eDose\u0026ndash;Response and Multivariable Analyses\u003c/h2\u003e\u003cp\u003eRestricted cubic spline models demonstrated a near-linear association between RDW and 28-day mortality (p for non-linearity\u0026thinsp;=\u0026thinsp;0.137) and a significant non-linear association with 90-day. Interaction plots showed stronger RDW-related risk in older patients, and a heatmap revealed additive risk when RDW was elevated alongside systemic inflammation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;D).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCox regression confirmed RDW as an independent predictor of mortality. Each 1% increase in RDW was associated with approximately 30\u0026ndash;36% higher 28-day mortality risk across models, with similar associations for 90-day mortality (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Forest plots illustrated RDW\u0026rsquo;s independent effect alongside age, sepsis, and organ dysfunction composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;B). Incremental prognostic analyses showed significant improvements in AUC and net reclassification index (NRI) when RDW was added to baseline models, and decision curve analysis demonstrated greater net clinical benefit across a range of thresholds (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u0026ndash;D).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCox Proportional Hazards Analysis of RDW and Mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRDW (continuous, per 1% \u0026uarr;) HR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh vs Low RDW HR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e28-day mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1 (unadjusted)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.323 (1.237\u0026ndash;1.415)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.72 (4.23\u0026ndash;44.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2 (age/sex adjusted)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.363 (1.262\u0026ndash;1.473)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.76 (3.91\u0026ndash;41.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3 (fully adjusted)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.308 (1.145\u0026ndash;1.495)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.21 (2.72\u0026ndash;38.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e90-day mortality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1 (unadjusted)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.300 (1.229\u0026ndash;1.375)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.91 (3.53\u0026ndash;13.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2 (age/sex adjusted)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.327 (1.247\u0026ndash;1.411)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.38 (3.25\u0026ndash;12.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3 (fully adjusted)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.276 (1.155\u0026ndash;1.409)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.03 (2.31\u0026ndash;10.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eModel 1\u0026thinsp;=\u0026thinsp;unadjusted; Model 2\u0026thinsp;=\u0026thinsp;adjusted for age and sex; Model 3\u0026thinsp;=\u0026thinsp;fully adjusted for comorbidities, laboratory variables, and inflammatory indices. Abbreviations: HR, hazard ratio; CI, confidence interval; RDW, red blood cell distribution width.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup Analyses\u003c/h2\u003e\u003cp\u003eThe prognostic value of RDW was consistent across sex and sepsis subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Associations were stronger in patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years and in those without hypertension, with significant interaction p-values for hypertension, indicating effect modification.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSubgroup Analysis of RDW and Mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubgroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28-day Mortality HR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP interaction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90-day Mortality HR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.85 (1.29\u0026ndash;2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.60 (1.30\u0026ndash;1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.43 (1.05\u0026ndash;1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.34 (1.10\u0026ndash;1.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;65 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.11 (1.31\u0026ndash;19.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.78 (1.21\u0026ndash;2.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;65 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.37 (1.10\u0026ndash;1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.24 (1.07\u0026ndash;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.13 (0.88\u0026ndash;1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06 (0.85\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.64 (1.61\u0026ndash;4.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.82 (1.48\u0026ndash;2.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSepsis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.67 (1.34\u0026ndash;2.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.21 (0.94\u0026ndash;1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.15 (0.002\u0026ndash;13.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.57 (1.32\u0026ndash;1.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eHazard ratios from fully adjusted Cox models. Interaction P values test for effect modification. Abbreviations: HR, hazard ratio; CI, confidence interval; RDW, red blood cell distribution width.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eResource Utilization and Organ Stress\u003c/h2\u003e\u003cp\u003eHigh RDW was associated with longer ICU and hospital stays (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u0026ndash;B). Patients with elevated RDW demonstrated greater renal, hepatic, and coagulation stress, as reflected in creatinine, bilirubin, and INR distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC), and RDW showed a positive correlation with hospital length of stay (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eRobustness and Collinearity Analyses\u003c/h2\u003e\u003cp\u003eCompeting risk analysis confirmed persistent associations between RDW and mortality after accounting for discharge alive as a competing outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Sensitivity analyses excluding septic patients or those with very short hospitalizations (\u0026lt;\u0026thinsp;3 days) yielded consistent results (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB\u0026ndash;C), and leave-one-covariate-out analyses confirmed the stability of RDW\u0026rsquo;s effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCorrelation analysis showed clustering of RDW with inflammatory and organ dysfunction markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA), but variance inflation factors were all \u0026lt;\u0026thinsp;5, indicating no concerning collinearity (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Principal component analysis further demonstrated RDW\u0026rsquo;s integration with the multivariable risk structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSupplementary Analyses\u003c/h2\u003e\u003cp\u003eDerived indices are summarized in \u003cb\u003eSupplementary Table S1\u003c/b\u003e. Missingness of variables was minimal (\u0026lt;\u0026thinsp;5% for most), except for PT, PTT, and INR (~\u0026thinsp;20%), which were imputed (\u003cb\u003eSupplementary Table S2\u003c/b\u003e). Univariate logistic regression confirmed significant associations of RDW, sepsis, renal and hepatic markers, and inflammatory indices with mortality, supporting the Cox regression findings (\u003cb\u003eSupplementary Table S3\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePrincipal Findings\u003c/h2\u003e\u003cp\u003eIn this large retrospective cohort of critically ill patients with acute pancreatitis (AP), we demonstrated that red blood cell distribution width (RDW) at intensive care unit (ICU) admission is a strong and independent predictor of short- and long-term mortality. Patients with elevated RDW had significantly worse biochemical profiles, longer ICU and hospital stays, and higher burden of renal, hepatic, and coagulation dysfunction. Importantly, RDW improved the discriminative performance of conventional prognostic models and provided incremental clinical benefit, as shown by net reclassification index (NRI) and decision curve analysis. These findings highlight RDW as a practical, low-cost, and readily available biomarker that captures systemic illness severity in AP.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eComparison with Previous Studies\u003c/h2\u003e\u003cp\u003eOur findings are consistent with earlier reports linking RDW to adverse outcomes in AP. Şenol et al. (2013) first identified elevated RDW as a predictor of in-hospital mortality. Wang et al. (2015) subsequently confirmed this in a larger cohort, showing a dose\u0026ndash;response relationship between RDW and mortality. More recent studies using intensive care populations have validated these results: He et al. (2023) found RDW independently predicted mortality even after propensity score matching, while Singh et al. (2020) demonstrated strong correlations between RDW and established severity scores. Song et al. (2025) extended these findings by showing that RDW predicted both short- and long-term mortality in patients with AP complicated by sepsis. Our study builds on this evidence by employing a high-resolution dataset, advanced statistical methods, and robust sensitivity analyses, confirming RDW\u0026rsquo;s value as a mortality predictor at both 28 and 90 days.\u003c/p\u003e\u003cp\u003eOutside pancreatitis, RDW has emerged as a reliable prognostic marker across multiple conditions. In sepsis, elevated RDW correlates with organ dysfunction and mortality [(Bazick et al., 2011; Jo et al., 2013; Song et al., 2025)]. In cardiovascular disease, RDW is an independent predictor of poor outcomes [(Patel et al., 2009; Braun et al., 2011; Makhoul et al., 2013)], and in pneumonia, it predicts early death with accuracy comparable to traditional risk scores [(Braun et al., 2011; Hong et al., 2012)]. During the COVID-19 pandemic, RDW was widely validated as a marker of mortality risk [(Foy et al., 2020)]. These consistent findings across diverse disease states suggest that RDW reflects systemic pathophysiological processes relevant to acute critical illness.\u003c/p\u003e\u003cp\u003eOur study also complements work on RDW-derived ratios. Gravito-Soares et al. (2018) reported that the RDW-to-calcium ratio (RDW:Ca) was superior to RDW alone in predicting AP severity. Acehan et al. (2024) and He et al. (2025) proposed RDW-to-albumin (RDW/Alb) as a composite index with higher prognostic accuracy. Similarly, our results indicate that RDW interacts synergistically with inflammatory indices, suggesting that future models integrating RDW with systemic inflammation markers may offer optimal predictive performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003ePotential Mechanisms\u003c/h2\u003e\u003cp\u003eSeveral mechanisms may underlie the observed association between elevated RDW and mortality in AP. First, RDW reflects anisocytosis due to inflammation-induced impairment of erythropoiesis. Proinflammatory cytokines such as interleukin-6 and tumor necrosis factor-α disrupt iron metabolism and suppress erythropoietin activity, resulting in heterogeneous red blood cell size [(Lippi et al., 2009; Salvagno et al., 2015)]. Second, oxidative stress\u0026mdash;a central feature of AP\u0026mdash;damages erythrocyte membranes and reduces their lifespan, thereby increasing RDW [(Bazick et al., 2011; Braun et al., 2011)]. Third, metabolic disturbances, particularly hypocalcemia and hypoalbuminemia, exacerbate anisocytosis, which may explain why RDW:Ca and RDW/Alb ratios are powerful prognostic markers [(Gravito-Soares et al., 2018; He et al., 2025)]. Finally, elevated RDW has been linked to endothelial dysfunction, impaired microcirculation, and multiorgan failure, which are key determinants of adverse outcomes in severe AP [(Makhoul et al., 2013; Braun et al., 2011)]. Together, these mechanisms underscore RDW as an integrative biomarker reflecting the interplay between inflammation, metabolic imbalance, and organ dysfunction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implications\u003c/h2\u003e\u003cp\u003eOur findings carry several clinical implications. First, RDW represents a simple, universally available biomarker that can be rapidly incorporated into prognostic assessment at the time of ICU admission. Unlike scoring systems such as APACHE II, BISAP, or SOFA, which require numerous variables and time for calculation, RDW can be obtained from routine blood counts at no additional cost [(Hagjer et al., 2018; Gao et al., 2015; Tee et al., 2018)]. In resource-limited settings, this makes RDW particularly attractive as a first-line risk stratification tool.\u003c/p\u003e\u003cp\u003eSecond, RDW could serve as a triage marker for early identification of high-risk patients. Those with elevated RDW might benefit from closer monitoring, more aggressive supportive care, and early transfer to higher-level facilities. Third, RDW may complement existing scores rather than replace them. Our study showed that incorporating RDW into conventional models improved both discrimination and clinical decision benefit. This suggests that hybrid models integrating RDW with clinical indices and inflammatory markers could provide the most accurate prognostic information.\u003c/p\u003e\u003cp\u003eFinally, RDW could aid in risk communication with patients and families. Because it is an easily understandable parameter routinely reported in laboratory results, clinicians may use RDW to communicate prognosis in a straightforward manner.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eFuture Directions\u003c/h2\u003e\u003cp\u003eFuture research should address several important questions. Prospective multicenter studies are needed to validate RDW as a prognostic marker across diverse healthcare systems and populations, including low-resource settings where simple markers are most valuable. Serial measurement of RDW over the course of hospitalization may provide dynamic prognostic information, as has been demonstrated in sepsis and COVID-19 [(Jo et al., 2013; Foy et al., 2020)]. Further work should also evaluate the role of RDW-derived ratios, such as RDW:Ca and RDW/Alb, and test whether they outperform RDW alone in risk prediction.\u003c/p\u003e\u003cp\u003eIntegration of RDW into machine learning\u0026ndash;based predictive models is another promising avenue. Recent studies using artificial intelligence have shown that combining hematologic indices with clinical and biochemical parameters improves prognostic performance in AP [(Surgery Open Science, 2024)]. Exploring whether RDW can enhance these algorithms could advance precision medicine approaches in pancreatitis. Finally, mechanistic studies are warranted to clarify whether interventions targeting inflammation, oxidative stress, or metabolic imbalance can modulate RDW and thereby improve outcomes.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eThe major strengths of this study include its use of the large and validated MIMIC-IV database, comprehensive statistical modeling, and extensive sensitivity analyses. By assessing both short- and long-term outcomes, our analysis provides a nuanced understanding of RDW\u0026rsquo;s prognostic role.\u003c/p\u003e\u003cp\u003eNonetheless, several limitations should be acknowledged. First, as a retrospective study, residual confounding cannot be excluded despite careful adjustment. Second, RDW is influenced by multiple conditions, including anemia, nutritional deficiencies, and hematologic disorders, which were not fully captured in our dataset [(Patel et al., 2009; Lippi et al., 2009)]. Third, because RDW is a nonspecific marker, it may reflect general systemic illness rather than pancreatitis-specific pathology. Fourth, our cohort was derived from a single U.S. tertiary care center, limiting external generalizability. Finally, dynamic changes in RDW during hospitalization were not evaluated, though they may provide additional prognostic insight.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study demonstrates that elevated RDW at ICU admission is a strong and independent predictor of both short- and long-term mortality in patients with acute pancreatitis. RDW enhances risk prediction when added to conventional scoring systems and reflects the integrated burden of systemic inflammation, oxidative stress, and metabolic derangements. Given its availability, cost-effectiveness, and simplicity, RDW has considerable potential as a practical biomarker for early risk stratification. Prospective, multicenter studies and mechanistic investigations are warranted to confirm these findings and to explore whether integrating RDW into clinical pathways can improve outcomes in acute pancreatitis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConsent for Publication\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics Approval and Consent to Participate\u003c/h2\u003e\u003cp\u003e This study was conducted in accordance with the ethical principles of the Declaration of Helsinki (revised 2013) and was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology. Access to the MIMIC-IV database was granted to the investigators after successful completion of the National Institutes of Health (NIH) online training course and certification in human research participant protection. As the MIMIC-IV database is de-identified and publicly available, additional institutional ethical approval and informed consent were not required.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study did not receive any financial support.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor ContributionsQian Xiao: Conceptualization, study design, data extraction, statistical analysis, interpretation of results, manuscript drafting, and critical revision.Yin Chen: Data collection, literature review, validation, visualization, and manuscript editing.Both authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData available into the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcehan, F., Yıldırım, M., Akbaş, A., \u0026Ouml;zt\u0026uuml;rk, A., Kocaman, O., \u0026amp; \u0026Ccedil;olak, Y. 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Ideal scoring system for acute pancreatitis: Need for early accurate prediction. \u003cem\u003eWorld Journal of Gastrointestinal Surgery, 11\u003c/em\u003e(3), 198\u0026ndash;209. https://doi.org/10.4240/wjgs.v11.i3.198\u003c/li\u003e\n\u003c/ol\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute pancreatitis, red blood cell distribution width, biomarker, mortality, prognosis, MIMIC-IV","lastPublishedDoi":"10.21203/rs.3.rs-7723696/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7723696/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAcute pancreatitis (AP) is a common gastrointestinal emergency with unpredictable progression and high mortality in severe cases. Traditional prognostic scores such as APACHE II, BISAP, and SOFA are limited by complexity and delayed applicability. Red blood cell distribution width (RDW), a simple and universally available biomarker, has emerged as a potential prognostic indicator. This study assessed the predictive value of RDW for short- and long-term mortality in critically ill patients with AP.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study using the MIMIC-IV database. Adult patients with a primary diagnosis of AP were included, with exclusions for repeat admissions, ICU stay\u0026thinsp;\u0026lt;\u0026thinsp;48 h, hematologic malignancy, or end-stage renal disease. Baseline RDW at ICU admission was the primary exposure. Primary outcomes were 28-day and 90-day all-cause mortality. Associations were evaluated using Kaplan\u0026ndash;Meier analysis, Cox regression, restricted cubic splines, and subgroup analyses. Incremental prognostic performance was assessed with AUC, net reclassification index (NRI), and decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 450 patients met inclusion criteria. The overall 28-day and 90-day mortality rates were 8.7% and 12.0%, respectively. Patients with elevated RDW (\u0026gt;\u0026thinsp;14.5%) had significantly higher mortality at both 28 days (13.4% vs. 4.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 90 days (18.7% vs. 6.5%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In fully adjusted Cox models, RDW remained an independent predictor of mortality (28-day: HR\u0026thinsp;=\u0026thinsp;1.31, 95% CI: 1.15\u0026ndash;1.50; 90-day: HR\u0026thinsp;=\u0026thinsp;1.28, 95% CI: 1.16\u0026ndash;1.41). RDW demonstrated strong discriminatory ability (AUC: 0.837 for 28-day, 0.807 for 90-day mortality). Incorporating RDW into baseline models improved predictive accuracy (ΔAUC\u0026thinsp;+\u0026thinsp;0.06; NRI\u0026thinsp;=\u0026thinsp;0.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and DCA showed greater net clinical benefit.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eRDW is a low-cost, widely accessible biomarker that independently predicts short- and long-term mortality in critically ill patients with AP. Its inclusion in conventional prognostic models enhances risk stratification and may support earlier, tailored clinical decision-making. Prospective multicenter validation is warranted.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of Red Blood Cell Distribution Width for Mortality in Critically Ill Patients with Acute Pancreatitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 10:06:48","doi":"10.21203/rs.3.rs-7723696/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6101663a-6615-4cf7-bbdc-1c6edc85b778","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-06T13:08:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 10:06:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7723696","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7723696","identity":"rs-7723696","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}