Prognostic value of the red blood cell distribution width/albumin ratio in patients with diabetes mellitus combined with sepsis: a retrospective cohort study based on MIMIC-IV

Prognostic value of the red blood cell distribution width/albumin ratio in patients with diabetes mellitus combined with sepsis: a retrospective cohort study based on MIMIC-IV | 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 the red blood cell distribution width/albumin ratio in patients with diabetes mellitus combined with sepsis: a retrospective cohort study based on MIMIC-IV Wan-Qiu Chen, Tian-Ming Yang, Qing-Quan Liu, Xiao-Yan Duan, Yu-he Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6888159/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 OBJECTIVE: This study investigated the relationship between red cell distribution width (RDW)/albumin ratio (RAR) and 30-day and 360-day mortality in patients with diabetes mellitus combined with sepsis. METHODS: We selected patients with diabetes mellitus comorbid with sepsis from the Intensive Care Medical Information Marketplace MIMIC-IV (3.0) database and classified these patients by median RAR. The outcome measures were 30-day and 360-day mortality. General baseline patient information was described; Kaplan-Meier survival curves were plotted to determine the difference in mortality between the two RAR groups. The accuracy of RAR in predicting 30-day and 360-day mortality in patients with diabetes mellitus and sepsis was assessed by calculating receiver operating characteristic curves (ROC) and the area under the curve (AUC). The relationship between RAR and 30- and 360-day all-cause mortality in patients with diabetes mellitus with sepsis was assessed using Cox regression models and subgroup analyses. Cox proportional hazards models and subgroup survival analyses were used to determine the association between RAR and 30- and 360-day mortality in patients with diabetes mellitus with sepsis. RESULTS: A total of 2168 eligible patients with diabetes mellitus combined with sepsis between 2008 and 2022 were included in the study, with 30-day and 360-day mortality rates of 34.1% and 50.6%, respectively; the ROC curves comparing patients' 30- and 360-day mortality rates showed an area under the curve (AUC) of 0.643 (360-day mortality outcome) for RAR and 0.612 for SOFA; the area under the curve was higher for RAR than for SOFA scores (P<0.05); when RAR was combined with SOFA, the outcome was still higher than when SOFA was used alone; in the Cox proportional hazards model, after adjustment for confounders, RAR was an independent risk factor for patients with diabetes combined with sepsis (HR=1.085,95% CI:1.049-1.122, P<0.001), and patients in the high RAR level group had a 34.3% increased risk of 30-day mortality (HR=1.343,95% CI:1.138-1.586; P <0.001). The 360-day risk of death was increased by 42.3% (HR=1.423,95% CI: 1.243-1.629; P <0.001). All results were consistent in subsequent subgroups. CONCLUSIONS: High RAR is an independent risk factor for 30- and 360-day all-cause mortality in adults with diabetes mellitus combined with sepsis. RAR can be used as a prognostic indicator for this condition. Sepsis Diabetes Red blood cell distribution width to albumin ratio (RAR) Intensive care unit mortality MIMIC-IV Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION Sepsis is a life-threatening clinical syndrome caused by microbial infections, characterised by a dysregulated systemic inflammatory response that leads to progressive organ dysfunction 1 . Despite significant advancements in critical care, which have contributed to a gradual decline in sepsis-related morbidity and mortality, sepsis remains a substantial global health burden and one of the leading causes of death worldwide. In 2017 alone, sepsis was responsible for approximately 11 million deaths 2 . Diabetes mellitus (DM), a chronic metabolic disorder characterised by persistent hyperglycemia and systemic inflammation, is strongly associated with an increased risk of infections and sepsis development. Compared to non-diabetic patients, those with DM who develop sepsis exhibit a more pronounced inflammatory response 3 , 4 , more significant immune dysfunction, and a higher propensity for multi-organ failure, resulting in more severe clinical manifestations and increased mortality rates 4 , 5 . Given this heightened risk, early risk stratification and prognostic assessment are essential for optimising treatment strategies and improving clinical outcomes in this high-risk population 3 . Recent studies have underscored the predictive value of inflammatory and metabolic biomarkers in predicting sepsis outcomes 6 – 8 . Among these, red blood cell distribution width (RDW) and albumin levels have garnered increasing attention as potential disease severity and prognosis indicators 2 , 9 . RDW reflects the degree of heterogeneity in red cell volume, and high RDW is associated with systemic inflammation and is a predictor of poor prognosis in a wide range of diseases 10 – 13 . Previous data have shown that a 1% increase in RDW is associated with a 23% increase in all-cause mortality 14 . Albumin is the most abundant protein in human plasma and is essential in many physiological mechanisms, including osmolality regulation 15 . Increased microvascular permeability in inflammatory states alters the distribution of intravascular and extravascular albumin, leading to a decrease in serum albumin concentration in many critically ill patients 16 . The red blood cell distribution width/albumin ratio (RAR), an emerging composite marker that combines these two metrics, allows for a more comprehensive assessment of a patient's inflammatory state and nutritional status 17 . RAR's prognostic predictive value is high in various diseases, including renal disease, cancer, and burn patients 18 – 20 ; however, its prognostic value in patients with diabetic comorbidities of sepsis has not been adequately investigated. This study analysed the relationship between RAR and 30-day and 360-day mortality in patients with diabetes combined with sepsis. 2. METHODS 2.1 Data sources The data were obtained from the MIMIC-IV 3.0 database, an open and freely available database developed by the Computational Physiology Laboratory at the Massachusetts Institute of Technology (MIT) 21 . The database records clinical data (e.g., basic information, vital signs, additional tests, medications, and diagnoses) for patients admitted to the Intensive Care Unit at Beth Israel Deaconess Medical Centre from 2008 to 2022.MIMIC-IV 3.0 is an updated version of MIMIC-IV 2.2 that incorporates data from recent years and improves on many aspects of MIMIC-IV 2.2. The relevant authorities authorised access to the database for the present study (Certificate No. 39168475). 2.2 Selection criteria We included patients with a definite diagnosis of diabetes mellitus combined with sepsis (Sepsis-3). Additionally, for patients readmitted to the ICU, only those admitted to the ICU for the first time were included in this study. We also excluded patients who did not have red distribution width, and serum albumin results on the first day of admission to the ICU, as well as patients who were admitted to the ICU for less than 24 hours and those under 18 years of age. 2.3 Data Collection The following variables were extracted from the MIMIC-IV database: demographic characteristics (age, gender), basic vital signs of the patient on admission (heart rate, respiratory rate, mean arterial pressure, oxygen saturation, temperature), laboratory parameters within 24 hours of admission (red blood cells, white blood cells, platelets, haemoglobin, serum sodium, serum potassium, serum calcium, serum urea nitrogen, serum creatinine, glucose), serum urea nitrogen, serum chloride, comorbidities (congestive heart failure, chronic obstructive pulmonary disease, cerebrovascular disease, etc.), Simplified Acute Physiology Score II (SAPS II), Sequential Organ Failure Assessment (SOFA), and shock index. We extracted data from the database using the PostgreSQL structured query language via a mock codebase ( https://github.com/MIT-LCP/mimic-code ). 2.4 Statistical analysis Statistical data analysis was performed using R software (version 4.12). Continuous variables were expressed as mean ± standard deviation and categorical data as frequencies. The T-test and X 2 test were used to compare differences between groups. Kaplan-Meier survival curves were plotted based on the median RAR to show the survival of patients with diabetes mellitus combined with sepsis at 30 days and were compared using the log-rank test. Multivariate Cox regression models were used to estimate the association between RAR and all-cause mortality in patients with diabetes mellitus and sepsis. The results of Cox regression were expressed as risk ratios (HRs) and risk ratios with 95% confidence intervals (CIs). Model I was adjusted for age, sex, respiration, temperature, and blood pressure. Model II was adjusted for age, sex, respiration, body temperature, blood pressure, red blood cells, haemoglobin, platelets, serum potassium, blood creatinine, blood urea nitrogen, congestive heart failure, cerebrovascular disease, renal disease, shock index, SOFA, and SAPS II. The predictive power of SOFA, RAR, and SOFA + RAR was compared by ROC analysis. Comparisons of AUC between models were estimated using the DeLong test. Subgroup analyses assessed the association between RAR and 30-day all-cause mortality by age, sex, comorbidities, SOFA score, and SAPS II score. A two-tailed test was used, and a difference of P < 0.05 was considered statistically significant. 2.5 Outcome The outcomes are 30-day and 360-day mortality rates after ICU admission. 3. RESULTS 3.1 Population characteristics A total of 2168 patients with red blood cell distribution width and albumin data within 24 hours in the intensive care unit were included in this study. All patients fulfilled the diagnostic criteria for SEPSIS-3 and diabetes mellitus, and the process of data criteria selection is shown in Fig. 1. The baseline characteristics of the enrolled patients were grouped according to median RAR.2168 patients were divided into two groups. Compared with patients with low RAR (≤ 5.23), patients in the high RAR group had a higher proportion of males, faster heart rate and respiration, more comorbidities, higher relevant scores, higher white blood cell count, blood creatinine and blood urea nitrogen; and lower blood pressure, red blood cell count, haemoglobin, blood glucose and blood calcium levels. In addition, patients in the high RAR group had higher mortality rates at 30 and 360 days than those in the low RAR group. 3.2 Relationship between RAR and patient mortality To explore the non-linear relationship between the independent and dependent variables, we used the recursive segmented spline regression (RCS) method. The model included variables with P < 0.05 as confounders in the analysis. The RCS curves (Fig. 2) showed a U-shaped relationship between RAR and all-cause mortality in diabetes mellitus combined with sepsis. The HR curves are upward-sloping when RAR > 5.23, and the risk of all-cause mortality increases with increasing RAR values. In Fig. 3, we plotted the KM survival curves of different groups, and the KM survival rate of the high RAR group was significantly lower than that of the low RAR group. The log-rank test confirmed the difference between the curves ( P < 0.001). Table 2 shows a multifactorial Cox regression model adjusted for statistically significant confounders in one-way analyses ( P < 0.05). RAR was an independent risk factor for all-cause mortality and was positively associated with patient prognosis. Compared with the low RAR group, the high RAR group had an increased risk of in-hospital mortality, with increased 30-day mortality (HR = 1.343, 95% CI: 1.138–1.586, P < 0.001) and 360-day mortality (HR = 1.423, 95% CI: 1.243–1.629, P < 0.001). Overall, higher RAR levels on admission predicted an increased risk of death. Table 1 Characteristics of the study patients by RAR levels Characteristics RAR levels P Total (n = 2168) RAR ≤ 5.23 (n = 1081) RAR>5.23 (n = 1087) Age (years), Mean ± SD 66.0 ± 13.3 66.0 ± 13.4 66.0 ± 13.2 0.968 Gender, n (%) < 0.001 Female 843 (38.9) 381 (35.2) 462 (42.5) Male 1325 (61.1) 700 (64.8) 625 (57.5) Length of ICU stay, Mean ± SD 8.0 ± 7.6 7.9 ± 7.3 8.1 ± 7.8 0.538 Essential vital signs, Mean ± SD Heart rate (bpm) 87.7 ± 17.5 85.6 ± 17.1 89.8 ± 17.6 < 0.001 SBP (mmHg) 116.3 ± 16.0 119.1 ± 16.5 113.6 ± 15.1 < 0.001 DBP (mmHg) 60.8 ± 10.5 62.4 ± 10.7 59.3 ± 10.0 < 0.001 MAP (mmHg) 76.6 ± 10.1 78.5 ± 10.3 74.7 ± 9.5 < 0.001 Respiratory rate (bpm) 20.7 ± 4.2 20.5 ± 4.1 20.8 ± 4.3 0.153 Temperature (°C) 36.9 ± 0.7 37.0 ± 0.7 36.9 ± 0.7 0.007 SPO 2 (%) 97.0 ± 2.2 96.9 ± 2.0 97.0 ± 2.3 0.246 Laboratory parameters, Mean ± SD Red blood cell (10 9 /L) 3.5 ± 0.8 3.7 ± 0.8 3.3 ± 0.8 < 0.001 White blood cell (10 9 /µL) 14.5 ± 16.4 13.7 ± 14.2 15.3 ± 18.3 0.018 Hemoglobin (g/L) 10.3 ± 2.3 11.1 ± 2.2 9.5 ± 2.0 < 0.001 Platelet (10 9 /µL) 204.2 ± 110.9 207.3 ± 94.1 201.2 ± 125.3 0.204 Potassium (mEq/L) 4.4 ± 0.9 4.4 ± 0.9 4.4 ± 0.9 0.275 Sodium (mEq/L) 137.9 ± 6.4 137.8 ± 5.7 138.0 ± 7.0 0.4 Calcium(mEq/L) 8.3 ± 1.0 8.5 ± 0.9 8.1 ± 1.0 < 0.001 BUN (mg/dL) 39.8 ± 28.9 35.4 ± 26.6 44.2 ± 30.3 < 0.001 Creatinine(mg/dL) 2.1 ± 1.9 2.0 ± 1.8 2.3 ± 1.9 < 0.001 Glucose (mg/dL) 202.4 ± 118.8 217.7 ± 132.6 187.1 ± 101.1 < 0.001 Chloride(mg/dL) 102.6 ± 7.5 101.8 ± 6.8 103.4 ± 8.1 < 0.001 Comorbidities, n (%) Congestive heart failure 924 (42.6) 457 (42.3) 467 (43) 0.747 Cerebrovascular Disease 384 (17.7) 214 (19.8) 170 (15.6) 0.011 Peptic Ulcer Disease 92 (4.2) 28 (2.6) 64 (5.9) < 0.001 Renal Disease 838 (38.7) 380 (35.2) 458 (42.1) < 0.001 Chronic Pulmonary Disease 586 (27.0) 294 (27.2) 292 (26.9) 0.861 Scoring systems, Mean ± SD Shock Index 1.2 ± 0.3 1.1 ± 0.2 1.2 ± 0.3 < 0.001 SOFA 8.5 ± 4.0 7.7 ± 3.7 9.4 ± 4.2 < 0.001 SAPSII 46.3 ± 14.9 43.2 ± 14.1 49.5 ± 14.9 < 0.001 30-day mortality 739 (34.1) 289 (26.7) 450 (41.4) < 0.001 360-day mortality 1098 (50.6) 439 (40.6) 659 (60.6) < 0.001 SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; MAP: Mean Arterial Pressure; SOFA: Sequential Organ Failure Assessment; SAPS II: Simplified Acute Physiology Score II Table 2 HR (95% CIs) for all-cause mortality across groups of BAR level Factor Univariate model Model I Model II HR (95% CIs) P HR (95% CIs) P HR (95% CIs) P 30-day all-cause mortality 1.142(1.112 − 1.172) < 0.001 1.126(1.093 ~ 1.161) < 0.001 1.085(1.049 ~ 1.122) 5.23 1.714(1.478 − 1.987) < 0.001 1.594 (1.37 ~ 1.855) < 0.001 1.343(1.138 ~ 1.586) < 0.001 360-day all-cause mortality 1.165(1.140 − 1.191) < 0.001 1.156(1.128 ~ 1.185) < 0.001 1.116(1.085 ~ 1.148) 5.23 1.783(1.580 − 2.012) < 0 .001 1.687 (1.491 ~ 1.91) < 0.001 1.423(1.243 ~ 1.629) < 0.001 HR: hazard ratio; CIs: confidence intervals. Model I covariates were adjusted for age, sex, respiration, body temperature, systolic blood pressure, diastolic blood pressure, and mean arterial pressure. Model II covariates were adjusted for age, sex, respiration, body temperature, systolic blood pressure, diastolic blood pressure, mean arterial pressure, red blood cell, haemoglobin, platelets, potassium, blood creatinine, blood urea nitrogen, congestive heart failure, cerebrovascular disease, renal disease, shock index, SAPS II, and SOFA. 3.3 ROC curve analysis Figure 4 shows the predictive ability of RAR alone and RAR with SOFA for 30- and 360-day mortality in patients with sepsis combined with diabetes. The AUC (95% CI) for ROC when RAR was combined with SOFA at 30 days as a mortality outcome was 67.0% (64.6%-69.4%), higher than the predictive ability of SOA alone. The AUC (95% CI) for ROC when SOFA was combined with RAR at 360 days as a mortality outcome was 66.7% (64.4%-68.9%). When comparing RAR alone with SOFA, the predictive ability of RAR was better for the long-term outcome of death. (Fig. 2). 3.4 Subgroup Analysis To further explore whether RAR remains an independent prognostic factor in specific subgroups of sepsis patients, we performed exploratory subgroup analyses of age, gender, comorbidities, and severity scores. Forest plots showed that BAR was an independent prognostic factor in most subgroups where 30-day mortality was the outcome (Fig. 5A), except for the shock index. In addition, 360-day mortality showed similar results. Higher RAR still predicted higher mortality in all subgroups except for interaction in patients with cerebrovascular disease and renal disease (Fig. 5B) 4. DISCUSSION This retrospective study definitively found that RAR is an independent predictor of mortality in patients with diabetes mellitus combined with sepsis, filling an essential gap in prognostic biomarkers for this high-risk population. Higher RAR levels (> 5.23) were unequivocally associated with increased 30- and 360-day mortality ( P < 0.001), which is consistent with the role of RAR as a comprehensive marker of systemic inflammation and nutritional status. RAR had a higher prognostic value than RDW and albumin, and its predictive accuracy was higher when combined with SOFA. RAR's predictive power was superior to SOFA's in the 360-day long-term mortality outcome. These findings highlight the significant potential of RAR for early risk stratification of septic diabetic patients and support its inclusion in ICU prognostic models to improve clinical decision-making. Both high RDW and low serum albumin levels are closely associated with an enhanced systemic inflammatory response 22 . In patients with sepsis, the inflammatory state induces an increase in microvascular permeability, which disrupts the normal distribution of albumin within and outside the vasculature, decreasing serum albumin concentration in critically ill patients. In addition, tumour necrosis factor-α (TNF-α) and interleukin-1 (IL-1) exacerbate the decline in albumin levels by down-regulating the transcription of albumin genes 23 , 24 . Recent studies have shown that inflammation stimulates hepatocytes to secrete more pro-inflammatory cytokines, such as interleukin-6 (IL-6), a process that inhibits erythropoiesis, leading to the release of large numbers of immature red blood cells into the peripheral circulation, which directly contributes to increased RDW 25 . At the same time, the adverse effects of inflammation on bone marrow function and iron metabolism contribute to the production of large numbers of reticulocytes, which are also strongly associated with increased RDW 26 . Sepsis also alters red blood cell membrane glycoprotein composition and ion channel function, further contributing to abnormal red blood cell morphology. Patients with diabetes are often subjected to a chronic low-grade inflammatory state that not only impairs erythropoiesis, leading to premature release of immature red blood cells into the circulation and an increased RDW but may also inhibit albumin synthesis, resulting in a decrease in serum albumin levels 23 . Diabetes-induced microangiopathy may exacerbate red blood cell damage in the microcirculation, further increasing RDW 27 ; at the same time, microangiopathy may also affect renal function, promoting albumin loss and exacerbating the decline in albumin levels. Oxidative stress under prolonged hyperglycaemia may also damage the red blood cell membrane, altering red blood cell morphology and function and ultimately leading to an increase in RDW; oxidative stress may also trigger glycosylation modification of albumin, affecting its normal function and metabolism and lowering albumin levels 28 , 29 . The RAR as a composite index has shown a high predictive value in various diseases 30 , 31 . The present study shows for the first time that RAR is significantly associated with mortality and has good predictive power in patients with diabetes mellitus combined with sepsis. Our data suggest that RAR outperforms RDW or albumin alone in predicting mortality associated with diabetic-associated sepsis, highlighting its potential value as an early risk stratification tool in critically ill patients. These findings highlight RAR's clinical utility in identifying high-risk patients and provide strong support for its integration into existing ICU prognostic models.However, several limitations of this study remain. First, as a retrospective analysis based on the MIMIC-IV database, it is difficult to eliminate the effect of residual confounders, even with rigorous statistical adjustment measures. Second, public databases such as MIMIC-IV often suffer from missing data, untimely recording, and selection bias, which may affect the accuracy of laboratory indicators and outcome variables. Third, although several confounders were corrected in this study, unmeasured factors such as nutritional status, inflammatory biomarkers, and sepsis aetiology may still have influenced the observed associations. Finally, the lack of long-term continuous measurements of RAR limits its potential use as a real-time surveillance indicator. Future studies should focus on validating RAR's predictive value in prospective multicentre cohorts and exploring its integration into ICU prognostic models. In addition, systematic monitoring of RAR trends during hospitalisation may help further elucidate its potential role in guiding therapeutic interventions in managing diabetic comorbid sepsis. 5. CONCLUSIONS In conclusion, we observed a significant association between high RAR. We increased all-cause mortality in patients with diabetes mellitus combined with sepsis. We showed that RAR is a simple and effective biomarker in adults with diabetes mellitus combined with sepsis. Declarations Conflict of interest No conflict of interest has been declared by the authors. Ethics approval and consent to participate Not applicable. Funding statement This work was supported by the Research project 2024AH051258 of the Anhui Provincial Department of Education, project 2024ZD0057 of the Bengbu City Science and Technology Innovation Guidance Category, project 2023byzd123 of the Bengbu Medical University Natural Science Key Project and Research Project AHWJ2024Aa30453 of Anhui Provincial Health Commission . Author Contribution Dr. Wan-Qiu Chen contributed to the conception, design, writing, data analysis, and interpretation of the study. Dr. Wan-Qiu Chen contributed to the conception and design of the study, data collection, and manuscript revision. Dr. Qing-Quan Liu contributed to the data collection, organization, and revision of the manuscript. Dr. Xiao-Yan Duan contributed to data analysis and interpretation, and manuscript revision. Dr. Yu-he Wang designed the conception and framework and revised the manuscript. All authors approved the submitted version and agreed to be accountable for their contributions to ensure the accuracy and integrity of any part of the work. Acknowledgement none References Shankar-Hari M, Phillips GS, Levy ML, et al. Developing a New Definition and Assessing New Clinical Criteria for Septic Shock: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Jama . Feb 23 2016;315(8):775-87. doi:10.1001/jama.2016.0289 Barichello T, Generoso JS, Singer M, Dal-Pizzol F. Biomarkers for sepsis: more than just fever and leukocytosis-a narrative review. Critical care (London, England) . Jan 6 2022;26(1):14. doi:10.1186/s13054-021-03862-5 Khana TQ, Anwar KA. Detection of Inflammatory Biomarkers Among Patients with Sepsis of Gram-Negative Bacteria: A Cross-Sectional Study. International journal of general medicine . 2023;16:3963-3976. doi:10.2147/ijgm.S415200 Agnello L, Giglio RV, Bivona G, et al. 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Nov 1 2016;71(11):644-649. doi:10.6061/clinics/2016(11)05 Kumar HG, Kanakaraju K, Manikandan VAC, Patel V, Pranay C. The Relationship Between Serum Albumin Levels and Sepsis in Patients Admitted to a Tertiary Care Center in India. Cureus . Apr 2024;16(4):e59424. doi:10.7759/cureus.59424 Li H, Xu Y. Association between red blood cell distribution width-to-albumin ratio and prognosis of patients with acute myocardial infarction. BMC cardiovascular disorders . Feb 3 2023;23(1):66. doi:10.1186/s12872-023-03094-1 Ertekin B, Acar T. The Relationship Between Prognosis and Red Cell Distribution Width (RDW) and RDW-Albumin Ratio (RAR) in Patients with Severe COVID-19 Disease. International journal of general medicine . 2022;15:8637-8645. doi:10.2147/ijgm.S392453 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6888159","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":474897336,"identity":"86f0fa1e-04e6-40d8-9cd0-7f8263c210fa","order_by":0,"name":"Wan-Qiu Chen","email":"","orcid":"","institution":"Bijie Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wan-Qiu","middleName":"","lastName":"Chen","suffix":""},{"id":474897339,"identity":"69ecedb7-6738-43a0-8a72-cfa3da63039c","order_by":1,"name":"Tian-Ming Yang","email":"","orcid":"","institution":"Bijie Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tian-Ming","middleName":"","lastName":"Yang","suffix":""},{"id":474897340,"identity":"4fe22ed3-8996-42a4-9826-4930abc93bc4","order_by":2,"name":"Qing-Quan Liu","email":"","orcid":"","institution":"Bijie Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qing-Quan","middleName":"","lastName":"Liu","suffix":""},{"id":474897341,"identity":"a48d2c13-1336-47c1-bdb4-fdfc14407a63","order_by":3,"name":"Xiao-Yan Duan","email":"","orcid":"","institution":"Bijie Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Yan","middleName":"","lastName":"Duan","suffix":""},{"id":474897342,"identity":"48dbf777-2dbe-469b-a361-3e36bf5c86f5","order_by":4,"name":"Yu-he Wang","email":"data:image/png;base64,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","orcid":"","institution":"Bengbu Third People's Hospital, Bengbu Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yu-he","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-13 12:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6888159/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6888159/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85390715,"identity":"007792ed-e298-456a-85c3-3fe89358600f","added_by":"auto","created_at":"2025-06-25 10:25:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":85094,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study\u003c/p\u003e","description":"","filename":"figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6888159/v1/4d8a167163abf32ec4756958.jpg"},{"id":85390719,"identity":"a83e7eaa-3083-4ebe-9443-6daf0a0cd0bd","added_by":"auto","created_at":"2025-06-25 10:25:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156756,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curves. RAR is non-linearly related to 30d and 360d risk in patients with diabetes combined with sepsis.\u003c/p\u003e","description":"","filename":"figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6888159/v1/24e401422ea89fba3521cacf.jpg"},{"id":85391705,"identity":"d93ecbd5-0c7d-4cfb-afb9-165bf9ad8710","added_by":"auto","created_at":"2025-06-25 10:33:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":213400,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves showing the relationship between RAR groupings and mortality in patients with diabetes mellitus combined with sepsis.\u003c/p\u003e","description":"","filename":"figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6888159/v1/9ec1435d26baee54054a5f9b.jpg"},{"id":85390721,"identity":"2cb9761f-6a1f-4df2-adaf-fe6d4b5ab53d","added_by":"auto","created_at":"2025-06-25 10:25:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":382686,"visible":true,"origin":"","legend":"\u003cp\u003ethe predictive ability of RAR alone and RAR with SOFA for 30- and 360-day mortality in patients with sepsis combined with diabetes\u003c/p\u003e","description":"","filename":"figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6888159/v1/26124f8ed870076aef62082d.jpg"},{"id":85390727,"identity":"48cdad68-3482-4e9f-b0ad-a32f78594d4f","added_by":"auto","created_at":"2025-06-25 10:25:03","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":603912,"visible":true,"origin":"","legend":"\u003cp\u003esubgroup analyses of age, gender, comorbidities, and severity scores\u003c/p\u003e","description":"","filename":"figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6888159/v1/72444767da83b196a24437eb.jpg"},{"id":94474147,"identity":"32294fdc-c70e-4403-bf3c-cbd6d3c442df","added_by":"auto","created_at":"2025-10-27 15:47:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2201971,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6888159/v1/2b80c3e1-99ae-4601-ad37-9276742da6ca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic value of the red blood cell distribution width/albumin ratio in patients with diabetes mellitus combined with sepsis: a retrospective cohort study based on MIMIC-IV","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eSepsis is a life-threatening clinical syndrome caused by microbial infections, characterised by a dysregulated systemic inflammatory response that leads to progressive organ dysfunction \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Despite significant advancements in critical care, which have contributed to a gradual decline in sepsis-related morbidity and mortality, sepsis remains a substantial global health burden and one of the leading causes of death worldwide. In 2017 alone, sepsis was responsible for approximately 11\u0026nbsp;million deaths\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Diabetes mellitus (DM), a chronic metabolic disorder characterised by persistent hyperglycemia and systemic inflammation, is strongly associated with an increased risk of infections and sepsis development. Compared to non-diabetic patients, those with DM who develop sepsis exhibit a more pronounced inflammatory response\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, more significant immune dysfunction, and a higher propensity for multi-organ failure, resulting in more severe clinical manifestations and increased mortality rates\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Given this heightened risk, early risk stratification and prognostic assessment are essential for optimising treatment strategies and improving clinical outcomes in this high-risk population\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Recent studies have underscored the predictive value of inflammatory and metabolic biomarkers in predicting sepsis outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Among these, red blood cell distribution width (RDW) and albumin levels have garnered increasing attention as potential disease severity and prognosis indicators\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRDW reflects the degree of heterogeneity in red cell volume, and high RDW is associated with systemic inflammation and is a predictor of poor prognosis in a wide range of diseases \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Previous data have shown that a 1% increase in RDW is associated with a 23% increase in all-cause mortality \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Albumin is the most abundant protein in human plasma and is essential in many physiological mechanisms, including osmolality regulation\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Increased microvascular permeability in inflammatory states alters the distribution of intravascular and extravascular albumin, leading to a decrease in serum albumin concentration in many critically ill patients \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The red blood cell distribution width/albumin ratio (RAR), an emerging composite marker that combines these two metrics, allows for a more comprehensive assessment of a patient's inflammatory state and nutritional status\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. RAR's prognostic predictive value is high in various diseases, including renal disease, cancer, and burn patients \u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e; however, its prognostic value in patients with diabetic comorbidities of sepsis has not been adequately investigated.\u003c/p\u003e \u003cp\u003eThis study analysed the relationship between RAR and 30-day and 360-day mortality in patients with diabetes combined with sepsis.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data sources\u003c/h2\u003e \u003cp\u003eThe data were obtained from the MIMIC-IV 3.0 database, an open and freely available database developed by the Computational Physiology Laboratory at the Massachusetts Institute of Technology (MIT) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The database records clinical data (e.g., basic information, vital signs, additional tests, medications, and diagnoses) for patients admitted to the Intensive Care Unit at Beth Israel Deaconess Medical Centre from 2008 to 2022.MIMIC-IV 3.0 is an updated version of MIMIC-IV 2.2 that incorporates data from recent years and improves on many aspects of MIMIC-IV 2.2. The relevant authorities authorised access to the database for the present study (Certificate No. 39168475).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Selection criteria\u003c/h2\u003e \u003cp\u003eWe included patients with a definite diagnosis of diabetes mellitus combined with sepsis (Sepsis-3). Additionally, for patients readmitted to the ICU, only those admitted to the ICU for the first time were included in this study. We also excluded patients who did not have red distribution width, and serum albumin results on the first day of admission to the ICU, as well as patients who were admitted to the ICU for less than 24 hours and those under 18 years of age.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collection\u003c/h2\u003e \u003cp\u003eThe following variables were extracted from the MIMIC-IV database: demographic characteristics (age, gender), basic vital signs of the patient on admission (heart rate, respiratory rate, mean arterial pressure, oxygen saturation, temperature), laboratory parameters within 24 hours of admission (red blood cells, white blood cells, platelets, haemoglobin, serum sodium, serum potassium, serum calcium, serum urea nitrogen, serum creatinine, glucose), serum urea nitrogen, serum chloride, comorbidities (congestive heart failure, chronic obstructive pulmonary disease, cerebrovascular disease, etc.), Simplified Acute Physiology Score II (SAPS II), Sequential Organ Failure Assessment (SOFA), and shock index. We extracted data from the database using the PostgreSQL structured query language via a mock codebase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MIT-LCP/mimic-code\u003c/span\u003e\u003cspan address=\"https://github.com/MIT-LCP/mimic-code\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical data analysis was performed using R software (version 4.12). Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and categorical data as frequencies. The T-test and X\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e test were used to compare differences between groups. Kaplan-Meier survival curves were plotted based on the median RAR to show the survival of patients with diabetes mellitus combined with sepsis at 30 days and were compared using the log-rank test. Multivariate Cox regression models were used to estimate the association between RAR and all-cause mortality in patients with diabetes mellitus and sepsis. The results of Cox regression were expressed as risk ratios (HRs) and risk ratios with 95% confidence intervals (CIs). Model I was adjusted for age, sex, respiration, temperature, and blood pressure. Model II was adjusted for age, sex, respiration, body temperature, blood pressure, red blood cells, haemoglobin, platelets, serum potassium, blood creatinine, blood urea nitrogen, congestive heart failure, cerebrovascular disease, renal disease, shock index, SOFA, and SAPS II. The predictive power of SOFA, RAR, and SOFA\u0026thinsp;+\u0026thinsp;RAR was compared by ROC analysis. Comparisons of AUC between models were estimated using the DeLong test. Subgroup analyses assessed the association between RAR and 30-day all-cause mortality by age, sex, comorbidities, SOFA score, and SAPS II score. A two-tailed test was used, and a difference of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Outcome\u003c/h2\u003e \u003cp\u003eThe outcomes are 30-day and 360-day mortality rates after ICU admission.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Population characteristics\u003c/h2\u003e \u003cp\u003eA total of 2168 patients with red blood cell distribution width and albumin data within 24 hours in the intensive care unit were included in this study. All patients fulfilled the diagnostic criteria for SEPSIS-3 and diabetes mellitus, and the process of data criteria selection is shown in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eThe baseline characteristics of the enrolled patients were grouped according to median RAR.2168 patients were divided into two groups. Compared with patients with low RAR (\u0026le;\u0026thinsp;5.23), patients in the high RAR group had a higher proportion of males, faster heart rate and respiration, more comorbidities, higher relevant scores, higher white blood cell count, blood creatinine and blood urea nitrogen; and lower blood pressure, red blood cell count, haemoglobin, blood glucose and blood calcium levels. In addition, patients in the high RAR group had higher mortality rates at 30 and 360 days than those in the low RAR group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Relationship between RAR and patient mortality\u003c/h2\u003e \u003cp\u003eTo explore the non-linear relationship between the independent and dependent variables, we used the recursive segmented spline regression (RCS) method. The model included variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as confounders in the analysis. The RCS curves (Fig.\u0026nbsp;2) showed a U-shaped relationship between RAR and all-cause mortality in diabetes mellitus combined with sepsis. The HR curves are upward-sloping when RAR\u0026thinsp;\u0026gt;\u0026thinsp;5.23, and the risk of all-cause mortality increases with increasing RAR values. In Fig.\u0026nbsp;3, we plotted the KM survival curves of different groups, and the KM survival rate of the high RAR group was significantly lower than that of the low RAR group. The log-rank test confirmed the difference between the curves (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows a multifactorial Cox regression model adjusted for statistically significant confounders in one-way analyses (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). RAR was an independent risk factor for all-cause mortality and was positively associated with patient prognosis. Compared with the low RAR group, the high RAR group had an increased risk of in-hospital mortality, with increased 30-day mortality (HR\u0026thinsp;=\u0026thinsp;1.343, 95% CI: 1.138\u0026ndash;1.586, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 360-day mortality (HR\u0026thinsp;=\u0026thinsp;1.423, 95% CI: 1.243\u0026ndash;1.629, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Overall, higher RAR levels on admission predicted an increased risk of death.\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\u003eCharacteristics of the study patients by RAR levels\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRAR levels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;2168)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAR\u0026thinsp;\u0026le;\u0026thinsp;5.23 (n\u0026thinsp;=\u0026thinsp;1081)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAR\u0026gt;5.23 (n\u0026thinsp;=\u0026thinsp;1087)\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), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e843 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e462 (42.5)\u003c/p\u003e \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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1325 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e700 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e625 (57.5)\u003c/p\u003e \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\u003eLength of ICU stay, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eEssential vital signs, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\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\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\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\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\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\u003eMAP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\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\u003eRespiratory rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLaboratory parameters, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed blood cell (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\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\u003eWhite blood cell (10\u003csup\u003e9\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\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 (10\u003csup\u003e9\u003c/sup\u003e/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204.2\u0026thinsp;\u0026plusmn;\u0026thinsp;110.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207.3\u0026thinsp;\u0026plusmn;\u0026thinsp;94.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201.2\u0026thinsp;\u0026plusmn;\u0026thinsp;125.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium(mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\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\u003eBUN (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.8\u0026thinsp;\u0026plusmn;\u0026thinsp;28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.4\u0026thinsp;\u0026plusmn;\u0026thinsp;26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.2\u0026thinsp;\u0026plusmn;\u0026thinsp;30.3\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\u003eCreatinine(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\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\u003eGlucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202.4\u0026thinsp;\u0026plusmn;\u0026thinsp;118.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217.7\u0026thinsp;\u0026plusmn;\u0026thinsp;132.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187.1\u0026thinsp;\u0026plusmn;\u0026thinsp;101.1\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\u003eChloride(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\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\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eComorbidities, n (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e924 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e457 (42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e467 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptic Ulcer Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (5.9)\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\u003eRenal Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e838 (38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e458 (42.1)\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\u003eChronic Pulmonary Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e586 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e294 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e292 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScoring systems, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\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\u003eShock Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\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\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\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\u003eSAPSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\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\u003e30-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e739 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e289 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e450 (41.4)\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\u003e360-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1098 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e439 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e659 (60.6)\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\"\u003eSBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; MAP: Mean Arterial Pressure;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSOFA: Sequential Organ Failure Assessment; SAPS II: Simplified Acute Physiology Score II\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHR (95% CIs) for all-cause mortality across groups of BAR level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CIs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CIs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CIs)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-day all-cause mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.142(1.112\u0026thinsp;\u0026minus;\u0026thinsp;1.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.126(1.093\u0026thinsp;~\u0026thinsp;1.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.085(1.049\u0026thinsp;~\u0026thinsp;1.122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAR\u0026thinsp;\u0026le;\u0026thinsp;5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAR\u0026thinsp;\u0026gt;\u0026thinsp;5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.714(1.478\u0026thinsp;\u0026minus;\u0026thinsp;1.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.594 (1.37\u0026thinsp;~\u0026thinsp;1.855)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.343(1.138\u0026thinsp;~\u0026thinsp;1.586)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e360-day all-cause mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.165(1.140\u0026thinsp;\u0026minus;\u0026thinsp;1.191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.156(1.128\u0026thinsp;~\u0026thinsp;1.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.116(1.085\u0026thinsp;~\u0026thinsp;1.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAR\u0026thinsp;\u0026le;\u0026thinsp;5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAR\u0026thinsp;\u0026gt;\u0026thinsp;5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.783(1.580\u0026thinsp;\u0026minus;\u0026thinsp;2.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0 .001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.687 (1.491\u0026thinsp;~\u0026thinsp;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.423(1.243\u0026thinsp;~\u0026thinsp;1.629)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eHR: hazard ratio; CIs: confidence intervals.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel I covariates were adjusted for age, sex, respiration, body temperature, systolic blood pressure, diastolic blood pressure, and mean arterial pressure.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel II covariates were adjusted for age, sex, respiration, body temperature, systolic blood pressure, diastolic blood pressure, mean arterial pressure, red blood cell, haemoglobin, platelets, potassium, blood creatinine, blood urea nitrogen, congestive heart failure, cerebrovascular disease, renal disease, shock index, SAPS II, and SOFA.\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\u003e3.3 ROC curve analysis\u003c/h2\u003e \u003cp\u003eFigure 4 shows the predictive ability of RAR alone and RAR with SOFA for 30- and 360-day mortality in patients with sepsis combined with diabetes. The AUC (95% CI) for ROC when RAR was combined with SOFA at 30 days as a mortality outcome was 67.0% (64.6%-69.4%), higher than the predictive ability of SOA alone. The AUC (95% CI) for ROC when SOFA was combined with RAR at 360 days as a mortality outcome was 66.7% (64.4%-68.9%). When comparing RAR alone with SOFA, the predictive ability of RAR was better for the long-term outcome of death. (Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.4 Subgroup Analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo further explore whether RAR remains an independent prognostic factor in specific subgroups of sepsis patients, we performed exploratory subgroup analyses of age, gender, comorbidities, and severity scores. Forest plots showed that BAR was an independent prognostic factor in most subgroups where 30-day mortality was the outcome (Fig.\u0026nbsp;5A), except for the shock index. In addition, 360-day mortality showed similar results. Higher RAR still predicted higher mortality in all subgroups except for interaction in patients with cerebrovascular disease and renal disease (Fig.\u0026nbsp;5B)\u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis retrospective study definitively found that RAR is an independent predictor of mortality in patients with diabetes mellitus combined with sepsis, filling an essential gap in prognostic biomarkers for this high-risk population. Higher RAR levels (\u0026gt;\u0026thinsp;5.23) were unequivocally associated with increased 30- and 360-day mortality (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which is consistent with the role of RAR as a comprehensive marker of systemic inflammation and nutritional status. RAR had a higher prognostic value than RDW and albumin, and its predictive accuracy was higher when combined with SOFA. RAR's predictive power was superior to SOFA's in the 360-day long-term mortality outcome. These findings highlight the significant potential of RAR for early risk stratification of septic diabetic patients and support its inclusion in ICU prognostic models to improve clinical decision-making.\u003c/p\u003e \u003cp\u003eBoth high RDW and low serum albumin levels are closely associated with an enhanced systemic inflammatory response\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In patients with sepsis, the inflammatory state induces an increase in microvascular permeability, which disrupts the normal distribution of albumin within and outside the vasculature, decreasing serum albumin concentration in critically ill patients. In addition, tumour necrosis factor-α (TNF-α) and interleukin-1 (IL-1) exacerbate the decline in albumin levels by down-regulating the transcription of albumin genes\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Recent studies have shown that inflammation stimulates hepatocytes to secrete more pro-inflammatory cytokines, such as interleukin-6 (IL-6), a process that inhibits erythropoiesis, leading to the release of large numbers of immature red blood cells into the peripheral circulation, which directly contributes to increased RDW\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. At the same time, the adverse effects of inflammation on bone marrow function and iron metabolism contribute to the production of large numbers of reticulocytes, which are also strongly associated with increased RDW\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Sepsis also alters red blood cell membrane glycoprotein composition and ion channel function, further contributing to abnormal red blood cell morphology. Patients with diabetes are often subjected to a chronic low-grade inflammatory state that not only impairs erythropoiesis, leading to premature release of immature red blood cells into the circulation and an increased RDW but may also inhibit albumin synthesis, resulting in a decrease in serum albumin levels\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Diabetes-induced microangiopathy may exacerbate red blood cell damage in the microcirculation, further increasing RDW\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e; at the same time, microangiopathy may also affect renal function, promoting albumin loss and exacerbating the decline in albumin levels. Oxidative stress under prolonged hyperglycaemia may also damage the red blood cell membrane, altering red blood cell morphology and function and ultimately leading to an increase in RDW; oxidative stress may also trigger glycosylation modification of albumin, affecting its normal function and metabolism and lowering albumin levels\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe RAR as a composite index has shown a high predictive value in various diseases\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The present study shows for the first time that RAR is significantly associated with mortality and has good predictive power in patients with diabetes mellitus combined with sepsis. Our data suggest that RAR outperforms RDW or albumin alone in predicting mortality associated with diabetic-associated sepsis, highlighting its potential value as an early risk stratification tool in critically ill patients. These findings highlight RAR's clinical utility in identifying high-risk patients and provide strong support for its integration into existing ICU prognostic models.However, several limitations of this study remain. First, as a retrospective analysis based on the MIMIC-IV database, it is difficult to eliminate the effect of residual confounders, even with rigorous statistical adjustment measures. Second, public databases such as MIMIC-IV often suffer from missing data, untimely recording, and selection bias, which may affect the accuracy of laboratory indicators and outcome variables. Third, although several confounders were corrected in this study, unmeasured factors such as nutritional status, inflammatory biomarkers, and sepsis aetiology may still have influenced the observed associations. Finally, the lack of long-term continuous measurements of RAR limits its potential use as a real-time surveillance indicator.\u003c/p\u003e \u003cp\u003eFuture studies should focus on validating RAR's predictive value in prospective multicentre cohorts and exploring its integration into ICU prognostic models. In addition, systematic monitoring of RAR trends during hospitalisation may help further elucidate its potential role in guiding therapeutic interventions in managing diabetic comorbid sepsis.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eIn conclusion, we observed a significant association between high RAR. We increased all-cause mortality in patients with diabetes mellitus combined with sepsis. We showed that RAR is a simple and effective biomarker in adults with diabetes mellitus combined with sepsis.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eNo conflict of interest has been declared by the authors.\u003c/p\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eThis work was supported by the Research project 2024AH051258 of the Anhui Provincial Department of Education, project 2024ZD0057 of the Bengbu City Science and Technology Innovation Guidance Category, project 2023byzd123 of the Bengbu Medical University Natural Science Key Project and Research Project AHWJ2024Aa30453 of Anhui Provincial Health Commission .\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDr. Wan-Qiu Chen contributed to the conception, design, writing, data analysis, and interpretation of the study. Dr. Wan-Qiu Chen contributed to the conception and design of the study, data collection, and manuscript revision. Dr. Qing-Quan Liu contributed to the data collection, organization, and revision of the manuscript. Dr. Xiao-Yan Duan contributed to data analysis and interpretation, and manuscript revision. Dr. Yu-he Wang designed the conception and framework and revised the manuscript. All authors approved the submitted version and agreed to be accountable for their contributions to ensure the accuracy and integrity of any part of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003enone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShankar-Hari M, Phillips GS, Levy ML, et al. Developing a New Definition and Assessing New Clinical Criteria for Septic Shock: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). \u003cem\u003eJama\u003c/em\u003e. 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Exploring Albumin Functionality Assays: A Pilot Study on Sepsis Evaluation in Intensive Care Medicine. \u003cem\u003eInternational journal of molecular sciences\u003c/em\u003e. Aug 8 2023;24(16)doi:10.3390/ijms241612551\u003c/li\u003e\n\u003cli\u003eManzoli TF, Delgado AF, Troster EJ, et al. Lymphocyte count as a sign of immunoparalysis and its correlation with nutritional status in pediatric intensive care patients with sepsis: A pilot study. \u003cem\u003eClinics (Sao Paulo, Brazil)\u003c/em\u003e. Nov 1 2016;71(11):644-649. doi:10.6061/clinics/2016(11)05\u003c/li\u003e\n\u003cli\u003eKumar HG, Kanakaraju K, Manikandan VAC, Patel V, Pranay C. The Relationship Between Serum Albumin Levels and Sepsis in Patients Admitted to a Tertiary Care Center in India. \u003cem\u003eCureus\u003c/em\u003e. Apr 2024;16(4):e59424. doi:10.7759/cureus.59424\u003c/li\u003e\n\u003cli\u003eLi H, Xu Y. Association between red blood cell distribution width-to-albumin ratio and prognosis of patients with acute myocardial infarction. \u003cem\u003eBMC cardiovascular disorders\u003c/em\u003e. Feb 3 2023;23(1):66. doi:10.1186/s12872-023-03094-1\u003c/li\u003e\n\u003cli\u003eErtekin B, Acar T. The Relationship Between Prognosis and Red Cell Distribution Width (RDW) and RDW-Albumin Ratio (RAR) in Patients with Severe COVID-19 Disease. \u003cem\u003eInternational journal of general medicine\u003c/em\u003e. 2022;15:8637-8645. doi:10.2147/ijgm.S392453\u003c/li\u003e\n\u003c/ol\u003e"}],"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":"Sepsis, Diabetes, Red blood cell distribution width to albumin ratio (RAR), Intensive care unit, mortality, MIMIC-IV","lastPublishedDoi":"10.21203/rs.3.rs-6888159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6888159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOBJECTIVE: This study investigated the relationship between red cell distribution width (RDW)/albumin ratio (RAR) and 30-day and 360-day mortality in patients with diabetes mellitus combined with sepsis.\u003c/p\u003e\n\u003cp\u003eMETHODS: We selected patients with diabetes mellitus comorbid with sepsis from the Intensive Care Medical Information Marketplace MIMIC-IV (3.0) database and classified these patients by median RAR. The outcome measures were 30-day and 360-day mortality. General baseline patient information was described; Kaplan-Meier survival curves were plotted to determine the difference in mortality between the two RAR groups. The accuracy of RAR in predicting 30-day and 360-day mortality in patients with diabetes mellitus and sepsis was assessed by calculating receiver operating characteristic curves (ROC) and the area under the curve (AUC). The relationship between RAR and 30- and 360-day all-cause mortality in patients with diabetes mellitus with sepsis was assessed using Cox regression models and subgroup analyses. Cox proportional hazards models and subgroup survival analyses were used to determine the association between RAR and 30- and 360-day mortality in patients with diabetes mellitus with sepsis.\u003c/p\u003e\n\u003cp\u003eRESULTS: A total of 2168 eligible patients with diabetes mellitus combined with sepsis between 2008 and 2022 were included in the study, with 30-day and 360-day mortality rates of 34.1% and 50.6%, respectively; the ROC curves comparing patients' 30- and 360-day mortality rates showed an area under the curve (AUC) of 0.643 (360-day mortality outcome) for RAR and 0.612 for SOFA; the area under the curve was higher for RAR than for SOFA scores (P\u0026lt;0.05); when RAR was combined with SOFA, the outcome was still higher than when SOFA was used alone; in the Cox proportional hazards model, after adjustment for confounders, RAR was an independent risk factor for patients with diabetes combined with sepsis (HR=1.085,95% CI:1.049-1.122, P\u0026lt;0.001), and patients in the high RAR level group had a 34.3% increased risk of 30-day mortality (HR=1.343,95% CI:1.138-1.586; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). The 360-day risk of death was increased by 42.3% (HR=1.423,95% CI: 1.243-1.629; \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). All results were consistent in subsequent subgroups.\u003c/p\u003e\n\u003cp\u003eCONCLUSIONS: High RAR is an independent risk factor for 30- and 360-day all-cause mortality in adults with diabetes mellitus combined with sepsis. RAR can be used as a prognostic indicator for this condition.\u003c/p\u003e","manuscriptTitle":"Prognostic value of the red blood cell distribution width/albumin ratio in patients with diabetes mellitus combined with sepsis: a retrospective cohort study based on MIMIC-IV","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 10:24:58","doi":"10.21203/rs.3.rs-6888159/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":"c90b6b57-f6c5-4b49-a0b7-ff610980e67c","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T14:30:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 10:24:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6888159","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6888159","identity":"rs-6888159","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}