Association between estimated glucose disposal rate and 28-day mortality in patients with sepsis: analysis of the MIMIC‑IV database

Association between estimated glucose disposal rate and 28-day mortality in patients with sepsis: analysis of the MIMIC‑IV database | 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 Association between estimated glucose disposal rate and 28-day mortality in patients with sepsis: analysis of the MIMIC‑IV database Yuanqing Li, Cuiping Hao, Meiqi Liu, Xuehui Zhang, Anhao Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6816578/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background The aim of the study was to examine the correlation between estimated glucose disposal rate (eGDR) and 28-day mortality in septic patients. Methods Using information from the MIMIC IV 2.1 database, we conducted a retrospective cohort analysis. We gathered patient demographics and used the eGDR to characterize insulin resistance. Participants were stratified into tertiles based on eGDR values and constructed Cox regression models with 28-day mortality in sepsis patients as the primary outcome measure. In order to clearly depict the relationship between eGDR and 28-day mortality, we used Kaplan–Meier (KM) curves and curve fitting. These models were designed to actively test for a potential possible correlation between eGDR and 28-day mortality in sepsis patients. Subgroup analysis was performed to explore the stability of the primary results. Results Enrolling a total of 672 patients, 58.8% were male and the 28-day mortality was 17.4%. An association was found between higher eGDR and higher 28-day mortality using multivariate cox regression analysis. Hazard ratio (HR) for 28-day mortality per unit increase in eGDR was 1.48[95% CI 1.16–1.89]. Compared to patients in the lowest tertile of eGDR, those in the highest tertile exhibited a higher 28-day mortality rate (HR, 2.53 [95% CI 1.49–4.37], adjusting for confounding factors). Subgroup analyses demonstrated that the effect of eGDR on mortality was robust across subgroups. Conclusion The eGDR is correlated with the28-day mortality in sepsis patients. Larger prospective studies are required in the future to further validate this finding. estimated glucose disposal rate Sepsis 28-day mortality MIMIC-IV database Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Sepsis, arising from the host's disordered response to infection, induces a spectrum of physiological abnormalities and can result in multiple organ dysfunction and mortality 1 . Sepsis is one of the main causes of death among critically ill patients, with a mortality rate as high as 20–50%, imposing a huge burden on society and the economy 2 .In sepsis, the body's stress response, including increased sympathetic nerve stimulation and elevated levels of adrenal cortical hormones, promotes glucose metabolism. This may lead to glucose metabolic disorders and insulin resistance (IR), further increasing the mortality rate of patients 3–5 . Timely and stringent is crucial for significantly improving the prognosis of patients with sepsis 6 . Rigorous blood glucose control can significantly reduce morbidity, mortality, and hospital stays 7 . The estimated glucose disposal rate (eGDR) is a clinical measure that assesses the severity of IR using readily available data, including BMI, hypertension, and glycosylated hemoglobin (HbA1c) 8 . Low eGDR is associated with increased microvascular and large-vessel complications in individuals with type 1 diabetes mellitus (T1DM) 9, 10 . Recently, it has been shown that poorly controlled eGDR levels correlate with an elevated risk of stroke among middle-aged and elderly individuals 11 .Despite its potential implications, few studies have investigated the correlation between eGDR and sepsis prognosis. Consequently, we conducted a retrospective cohort study to examine the significance of eGDR in assessing sepsis patient outcomes. Methods Data selection This research utilized a retrospective observational design, drawing upon data from the publicly accessible MIMIC-IV database ( https://mimic.mit.edu ) 12 . To access the database, an author of this study acquired the necessary certifications and then extracted the pertinent variables (authentication number: 12203293) for our research purposes. 13 . As the database contains only anonymised patient health information, individual patient consent and Clinical trial number were not required. The approach to identifying patients with sepsis who fulfilled the Sepsis-3.0 diagnostic criteria from the MIMIC database aligns with the methodology detailed in a previously reported study. 14 . Patients who had been in the ICU for less than 24 hours were excluded from the study. Furthermore, patients with insufficient data were excluded, for example, those with missing glycated haemoglobin and BMI measurements. The final study cohort consisted of 672 patients, who were divided into three groups based on the tertiles of the calculated eGDR (Fig. 1 ). Data collection The collection of patient baseline characteristics was carried out utilizing SQL and PostgreSQL database system (version 14.2).The characteristics extracted included patient demographic data (age, sex, race,and vital signs), laboratory test results, and coexisting conditions, all obtained from the MIMIC-IV database.The eGDR index was calculated using the following formula: eGDR = 19.02 - [0.22×BMI (kg/m 2 )] - [3.26×hypertension (presence)] - [0.61×glycated haemoglobin (%)],with hypertension (yes = 1, no = 0) 8, 15 .Multiple interpolation, was employed to enhance statistical power and reduce bias resulting from missing data.To minimize the bias arising from multiple imputation, we also conducted sensitivity analyses. We performed descriptive analyses and multivariate Cox regression analyses using different multiple imputation methods (Supplementary Tables 1 and 2) and found that the results were relatively stable . Primary outcome The primary endpoint of this study in patients with sepsis was 28-day mortality following ICU admission. Statistical analysis The Kolmogorov-Smirnov test was used to assess variable distributions for normality. Continuous variables with normal distribution were depicted as mean ± standard deviation, and median (interquartile range [IQR]) was used for those not normally distributed. Group differences were analyzed using the Kruskal-Wallis H test for skewed variables, one-way ANOVA for normally distributed variables, and the Chi-square test for categorical variables. 16 . Multivariate Cox regression models were utilized to compute hazard ratios (HR) and 95% confidence intervals (95% CI) to quantify the effect of eGDR on these outcomes, excluding participants lost to follow-up at this time point. The 28-day mortality outcome was assessed using Kaplan-Meier survival curves for eGDR tertiles and evaluated with log-rank tests. We selected these confounders based on expertise, prior scientific literature, and all significant covariates identified in univariate analyses. We performed sensitivity analyses by constructing multiple Cox regression models with incremental adjustments to ensure the robustness of the research results. The eGDR was included in the models both as a categorical variable (tertiles) and as a continuous variable.Four models were constructed: Model 1 included only eGDR (unadjusted), Model 2 adjusted for age and sex, and Model 3 further adjusted for age, sex, race/ethnicity, sodium, chloride, bicarbonate, red blood cell distribution width (RDW), lactate (LAC) and c-reactive protein (CRP). In model 4,besides the variables included in Model 3, the Sequential Organ Failure Assessment (SOFA) score, charlson comorbidity index (CCI), cerebrovascular disease, diabetes status,and cancer were incorporated. In addition, as a form of sensitivity analysis, we conducted stratified analyses on different subgroups. This analysis yielded hazard ratio (HR) and their corresponding 95% Confidence intervals (95%CIs). No significant interactions were observed in these subgroup. The effect sizes and corresponding p-values derived from these models were documented and contrasted. R software (version 4.2.2, http://www.R-project.org , The R Foundation) and the Free Statistics analytic platform (version 1.9, Beijing, China, http://www.clinicalscientists.cn/freestatistics ) were used for all statistical analyses. Results Patient characteristics and outcome Of the total 50,955 patients, 672 were included in the analysis. We excluded 49,619 patients due to missing glycosylated hemoglobin (HbA1c) data. An additional 652 patients were excluded because of incomplete body mass index (BMI) records, and ultimately, 12 patients were excluded due to ICU stays shorter than 24 hours (Fig. 1 ). Table 1 shows the baseline characteristics of participants stratified by eGDR. The 28-day mortalitywew 17.4%. We enrolled 672 patients with a mean age of 65.6 ± 15.4 years and 58.8% male. The eGDR varied significantly across tertiles: T1 (5.1 ± 1.6 mg/kg/min), T2 (8.4 ± 0.7 mg/kg/min), and T3 (10.2 ± 0.6 mg/kg/min) (P < 0.001). The SOFA score was elevated in T3 (3.4 ± 2.0) compared to both T1 (3.1 ± 1.4) (P < 0.05). BMI was peaking in T1 at 32.8 ± 8.3. HbA1c and glucose levels were inversely related to eGDR, with T1 showing higher mean of 7.9% and 173.8 mg/dL, respectively, compared to T3's 5.8% and 138.0 mg/dL (P < 0.001) . Potassium levels varied by eGDR tertile, with T1 showing a lower mean of 4.2 mEq/L (P<0.05) . To reduce selection bias, sensitivity analysis using multiple imputation of missing datasets corroborated these findings (see Supplementary Table 3). Table 1 Baseline characteristics according to eGDR Tri-sectional Quantile Characteristics Total (n = 672) T1 (N = 224) T2 (N = 223) T13(N = 225) P-value Demographic Sex, n (%) 0.201 Female 277 (41.2) 102 (45.5) 83(37.2) 92 (40.9) Male 395 (58.8) 122 (54.5) 140 (62.8) 133 (59.1) Race/ethnicity,n (%) 0.253 Asian people 93 (13.8) 25 (11.2) 40 (17.9) 28 (12.4) Black people 188 (28.0) 61 (27.2) 62 (27.8) 65 (28.9) White people 391 (58.2) 138 (61.6) 121 (54.3) 132 (58.7) Age (years) 65.6 ± 15.4 65.3 ± 14.9 64.7 ± 15.8 66.7 ± 15.5 0.389 Heart rate (times) 104.9 ± 20.7 105.1 ± 20.9 102.2 ± 19.0 107.2 ± 21.9 0.038 Systolic blood pressure (mmHg) 94.9 ± 17.2 95.2 ± 16.9 95.7 ± 17.6 93.6 ± 17.2 0.529 Resp rate (times) 28.1 ± 6.2 27.6 ± 5.8 27.5 ± 6.1 29.4 ± 6.4 0.001 BMI 28.8 ± 6.9 32.8 ± 8.3 29.4 ± 4.8 24.2 ± 3.7 < 0.001 SOFA score 3.2 ± 1.7 3.1 ± 1.4 3.0 ± 1.6 3.4 ± 2.0 0.028 APSIII score 53.2 ± 22.9 52.8 ± 20.9 52.4 ± 23.3 54.3 ± 24.3 0.654 SAPII score 37.2 ± 12.8 36.3 ± 12.7 36.9 ± 12.0 38.3 ± 13.6 0.233 OASIS score 35.1 ± 8.7 35.0 ± 8.4 34.4 ± 8.6 35.8 ± 9.1 0.205 Charlson comorbidity index 6.2 ± 2.7 6.5 ± 2.5 6.2 ± 2.6 6.1 ± 2.9 0.23 28-day mortality, n (%) 117 (17.4) 20 (8.9) 48 (21.5) 49 (21.8) < 0.001 Laboratory tests eGDR(mg/kg/min) 7.9 ± 2.4 5.1 ± 1.6 8.4 ± 0.7 10.2 ± 0.6 < 0.001 HbA1c(%) 6.7 ± 2.0 7.9 ± 2.8 6.3 ± 1.2 5.8 ± 0.7 < 0.001 Glucose (mg/dL) 154.2 ± 56.5 173.8 ± 62.6 151.0 ± 56.1 138.0 ± 43.3 < 0.001 Sodium(mEq/L) 139.0 ± 5.1 138.5 ± 5.5 139.2 ± 4.7 139.2 ± 5.0 0.190 Potassium(mEq/L) 4.2 ± 0.8 4.1 ± 0.7 4.3 ± 0.8 4.2 ± 0.7 0.021 Chloride(mg/L) 103.6 ± 5.9 103.0 ± 5.9 104.2 ± 5.8 103.5 ± 5.9 0.117 Bicarbonate(mmol/L) 22.7 ± 4.6 22.8 ± 4.9 22.4 ± 4.7 22.8 ± 4.0 0.610 AG(mmol/L) 15.6 ± 4.1 16.1 ± 4.3 15.4 ± 4.3 15.2 ± 3.6 0.047 BE -1.3 ± 4.9 -1.3 ± 5.6 -1.4 ± 5.1 -1.1 ± 4.1 0.875 MCV (fl) 91.0 ± 7.2 89.8 ± 7.0 91.1 ± 7.2 92.0 ± 7.3 0.006 MCHC(g/L) 32.9 ± 1.6 33.1 ± 1.7 32.7 ± 1.5 32.9 ± 1.7 0.058 LAC(mmol/L) 2.0 ± 1.4 2.0 ± 1.3 2.2 ± 1.8 1.9 ± 1.2 0.121 CRP(mg/L) 81.9 ± 103.7 84.6 ± 109.9 70.0 ± 85.4 91.1 ± 112.8 0.087 RDW(%) 14.6 ± 2.0 14.5 ± 2.0 14.6 ± 1.9 14.7 ± 2.1 0.370 WBC (×10^9/L) 11.2 (8.6, 15.4) 10.9 (8.6, 14.9) 11.4 (9.1, 15.5) 11.2 (8.2, 15.7) 0.576 Medical History Hypertension, n (%) 158 (23.5) 127 (56.7) 29 (13) 2 (0.9) < 0.001 Diabetes, n (%) 216 (32.1) 115 (51.3) 59 (26.5) 42 (18.7) < 0.001 Cerebrovascular disease, n (%) 259 (38.5) 73 (32.6) 99 (44.2) 87 (38.8) 0.037 Cancer, n (%) 39 ( 5.8) 12 (5.4) 13 (5.9) 14 (6.2) 0.926 Note: Values are expressed as mean ± SD, median (interquartile range), or n (%). BMI, body mass index; SOFA, sequential organ failure assessment; APSIII, acute physiology and chronic health evaluation III; SAPII, simplified acute physiology score II; OASIA, oxford acute severity of illness score; eGDR, estimated glucose disposal rate; AG, anion gap; BE, base excess; MCV, ,mean corpuscular volume; MCHC, mean corpuscular hemoglobin concentration; LAC, lactate; CRP, c-reactive protein; RDW, red cell distribution width; WBC, white blood cell count. eGDR and 28‑day mortality Multiple regression analysis findings are shown in Table 2 , which indicates that in unadjusted models, there is a significant correlation between eGDR and 28-day mortality (HR = 1.51, 95% CI: 1.20–1.89). The mortality incidence increased by 51% for every unit rise in eGDR. In the multivariate regression model, after adjusting for age and gender, the HR was 1.48 (95% CI: 1.18–1.86). After adjusting for age, sex, race/ethnicity, sodium, chloride, bicarbonate, RDW, LAC, and CRP, the HR was 1.53 (95% CI: 1.21–1.94). After further adjusting for age, sex, race/ethnicity, sodium, chloride, bicarbonate, RDW, LAC, CRP, SOFA score, harlson comorbidity index (CCI), cerebrovascular disease, diabetes status and cancer, the HR was 1.50 (95% CI: 1.17–1.92). Compared with the lowest tertile of eGDR index, the HR of 28-day mortality in the highest tertile of eGDR index was 2.56 (95%CI: 1.48–4.06), independent of potential confounding factors. Sensitivity analyses demonstrated consistent associations between eGDR and 28-day mortality across various adjustment strategies. The Kaplan-Meier analysis plot showed the eGDR had a significant association with the 28-day mortality (log-rank test P = 0.00021 , Fig. 2 ).The survival rates of groups T1 was higher than those in T2 and T3 groups, and the T3 group described the lowest survival probability at the time point of 28 days. At 28 days, the survival rates of groups T1, T2 and T3 were 94.9%, 84.1% and 83.8% respectively. The curve-fitting plot (Fig. 3 ) demonstrated a positive correlation between eGDR and the risk of 28-day mortality. As eGDR increased,hazard Ratio for death within 28 days also increased, suggesting that eGDR may be an important predictor of the risk of death within 28 days. However, when eGDR reached a certain value (e.g., 8.435), the extent of risk reduction decreased significantly, and the curve tended to flatten.This suggests that there may be a threshold effect. Table 2 Multivariable cox regression to assess the association of eGDR with 28-day mortality eGDR(n = 672) Tertiles of eGDR (n = 672)* Q1(n = 224) Q2(n = 223) Q3(n = 225) HR(95%CI) P HR(95%CI) P HR(95%CI) P HR(95%CI) P Model 1 a 1.51 (1.2 ~ 1.89) < 0.001 1(Ref) 2.64 (1.57 ~ 4.46) <0.001 2.64 (1.57 ~ 4.44) < 0.001 Model 2 b 1.48 (1.18 ~ 1.86) 0.001 1(Ref) 2.68 (1.53 ~ 4.51) <0.001 2.68 (1.53 ~ 4.34) < 0.001 Model 3 c 1.53(1.21 ~ 1.94) < 0.001 1(Ref) 2.39 (1.41 ~ 4.05) 0.001 2.63 (1.56 ~ 4.45) < 0.001 Model 4 d 1.5 (1.17 ~ 1.92) 0.001 1(Ref) 2.37(1.38 ~ 4.06) 0.002 2.56(1.48 ~ 4.41) 0.001 * eGDR was entered as a continuous variable a. Model 1: unadjusted models b. Model 2: additionally adjusted for age,sex c. Model 3: adjusted as for model 2, additionally adjusted for race/ethnicity, sodium, chloride, bicarbonate,RDW, LAC and CRP d. Model 4: adjusted as for model 3, additionally adjusted for SOFA score, charlson comorbidity index (CCI), cerebrovascular disease, diabetes status and cancer Subgroup Analysis In the subgroup analysis of the relationship between eGDR and 28 - day sepsis mortality, despite non - significant interaction effects across age, gender, race, SOFA score, and diabetes subgroups (all P > 0.05). This is an indication that the relationship between eGDR and mortality is robust in these subgroups. In age subgroups, eGDR had a weaker predictive power for those < 60 years old (HR = 1.17, 95% CI: 0.95–1.44) (Fig. 4 ). In both genders, a positive eGDR-mortality association was observed. The analysis revealed that eGDR was consistently associated with an increased event rate in most subgroups, with statistical significance observed in both black and white racial subgroups, as well as in the non-diabetic subgroup, that is, eGDR was significantly associated with sepsis mortality. Considering thatHbA1c is usually measured in ICU patients only if there is a history of diabetes or hyperglycemia, a limitation that may affect the generalizability of the findings. We therefore performed subgroup analyses of patients with a history of diabetes or hyperglycemia and those without such a history to assess differences in results (Supplementary Table 4 ). Discussion The predictive value of eGDR for sepsis is under investigation for the first time in our study.The main findings could be summarized as: (1)higher eGFR was significantly associated with higher sepsis mortality.(2)A linear relationship was observed between sepsis mortality and eGDR.(3)The eGDR significantly enhanced the predictive power of basic models for adverse outcomes in sepsis. This is the first retrospective study conducted to investigate the correlation between eGDR and mortality in critical care sepsis. A total of 672 sepsis cases were included in the study.FramRY showed that insulin resistance is present in some septic patients and that the degree of insulin resistance correlates with morbidity and mortality 17 .There are many tools and methods for detecting insulin resistance, and the hyperinsulinemic euglycemic clamp (HEC) is recognized as the international gold standard for assessing insulin sensitivity in vivo 18 .Large-scale clinical trials are not appropriate for the hyperinsulinemic euglycemic clamp (HEC), despite the fact that it is currently the globally recognized gold standard for assessing insulin sensitivity in vivo. Therefore, one way to measure the degree of insulin resistance is to use the eGDR. Research has indicated that a low eGDR is associated with a poor prognosis for both those with non-alcoholic fatty liver disease and type 2 diabetes mellitus 19, 20 .Our results showed that high eGDR increased the risk of sepsis mortality This may be due to the composition of the formula, and the formula used to calculate eGDR includes BMI, glycated haemoglobin and hypertension status. It has been shown that overweight and obese septic patients tend to have a lower mortality rate, which is known as the‘obesity paradox’ 21–23 . A meta-analysis suggests that adjusted mortality is actually reduced in overweight or obese patients with sepsis or septic shock treated in the ICU 24 . This inverse epidemiological phenomenon of BMI in sepsis may be due to the fact that,obese patients are in a state of high glycolipid reserve over an extended period of time and the excess body fat can be used as a protective energy store to prevent muscle loss due to the high catabolic state in sepsis 25 . Glycosylated haemoglobin (HbA1c) is the product of a slow, non-enzymatic glycation reaction of haemoglobin in the presence of high blood glucose and its level reflects glycaemic control 8–12 weeks prior to testing 26 . It has been suggested that the presence of elevated blood glucose in the early phases of the condition is a protective and adaptive response by the body, and that U-shaped correlation exists between blood glucose levels and the risk of mortality 27 .Mild or moderate stress hyperglycaemia is a protective mechanism for critically ill patients. In adult T1DM patients in Sweden, with HbA1c > 7.0% and HbA1c < 6.1% were associated with higher sepsis risk 28 . The poor prognosis between hyperglycaemia and sepsis in previous studies may be due to the adverse events associated with hypoglycaemia as a result of intensive glycaemic control 29 . Furthermore, some studies have suggested that hyperglycaemia on admission is not associated with sepsis prognosis 30 . Hypertension as a contributing risk factor for sepsis 31 and it was found that hypertensive rats suffering from sepsis had a better final prognosis than controls in the early stages, although the values of haemodynamic parameters were higher than those of the control group 32, 33 .This may be because patients with a history of hypertension are taking antihypertensive medication. It has been suggested that antihypertensive drugs may affect the prognosis of patients with sepsis 34 , and results from animal studies have shown that Pretreatment with the β-blocker atenolol reduces sepsis mortality in mice mortality in mice 35 .Other commonly used antihypertensive drugs are calcium channel blockers (CCBs). Several clinical trials have shown that CCBs have anti-inflammatory effects, and previous use of CCBs has been associated with decreased mortality in sepsis patients or in intensive care patients with severe sepsis 36, 37 . Our research showed that in critically ill sepsis patients, a higher eGDR is associated with a higher risk of 28-day all-cause mortality. This suggests that eGDR can be used to predict the risk of adverse outcomes in patients with sepsis. It is important to note that this is an observational study and cannot establish causality. The observed association may be influenced by unmeasured or residual confounding factors. Therefore, our findings should be interpreted with caution.There are various restrictions on this study. First of all, because it is a retrospective study based on historical records, it could be skewed by information. Secondly, there was substantial missing data in the MIMIC database, which led to the inclusion of a limited number of cases, potentially introducing some bias. Thirdly, the calculation formula for the eGDR was mainly derived from the MIMIC - IV data, which predominantly represents the U.S. population.However, its generalizability to populations with different racial and body - mass index (BMI) distributions remains a crucial concern. Fourthly, during the data processing stage, a large amount of missing data was excluded, which might introduce selection bias. Future studies should consider these limitations and aim to address them through larger, prospective cohorts or randomized controlled trials. Conclusion High levels of eGDR (an indicator of insulin resistance) are associated with an increased risk of 28-day mortality in sepsis patients. Nonetheless, additional prospective studies are required to elucidate this relationship. Declarations Ethics approval and consent to participate As the database contains only anonymised patient health information, individual patient consent and Clinical trial number were not required. Clinical Trial Clinical trial number: not applicable Consent for publication We declare that this manuscript is original, has" not been' published before and is not currently being considered for publication elsewhere.We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. Availability of data and material The data that support the findings of this study are openly available in [MIMIC-IV database] at https://mimic.mit.edu. In addition, we submit the data used in the article as an attachment. Competing interests I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding No funds, grants, or other support was received. Authors' contributions All authors contributed to the study conception and design. 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O.; Bakker, S. J.; Oudemans-van Straaten, H. M.; Nijsten, M. W. Urinary creatinine excretion is related to short-term and long-term mortality in critically ill patients. Intensive Care Med 2018 , 44 (10), 1699-1708. DOI: 10.1007/s00134-018-5359-6 From NLM. Fram, R. Y.; Cree, M. G.; Wolfe, R. R.; Barr, D.; Herndon, D. N. Impaired glucose tolerance in pediatric burn patients at discharge from the acute hospital stay. J Burn Care Res 2010 , 31 (5), 728-733. DOI: 10.1097/BCR.0b013e3181eebe63 From NLM. Nishtala, R.; Kietsiriroje, N.; Karam, M.; Ajjan, R. A.; Pearson, S. Estimated glucose disposal rate demographics and clinical characteristics of young adults with type 1 diabetes mellitus: A cross-sectional pilot study. Diab Vasc Dis Res 2020 , 17 (5), 1479164120952321. DOI: 10.1177/1479164120952321 From NLM. Song, J.; Ma, R.; Yin, L. Associations between estimated glucose disposal rate and arterial stiffness and mortality among US adults with non-alcoholic fatty liver disease. Front Endocrinol (Lausanne) 2024 , 15 , 1398265. DOI: 10.3389/fendo.2024.1398265 From NLM. Nyström, T.; Holzmann, M. J.; Eliasson, B.; Svensson, A. M.; Kuhl, J.; Sartipy, U. Estimated glucose disposal rate and long-term survival in type 2 diabetes after coronary artery bypass grafting. Heart Vessels 2017 , 32 (3), 269-278. DOI: 10.1007/s00380-016-0875-1 From NLM. Hogue, C. W., Jr.; Stearns, J. D.; Colantuoni, E.; Robinson, K. A.; Stierer, T.; Mitter, N.; Pronovost, P. J.; Needham, D. M. The impact of obesity on outcomes after critical illness: a meta-analysis. Intensive Care Med 2009 , 35 (7), 1152-1170. DOI: 10.1007/s00134-009-1424-5 From NLM. Wurzinger, B.; Dünser, M. W.; Wohlmuth, C.; Deutinger, M. C.; Ulmer, H.; Torgersen, C.; Schmittinger, C. A.; Grander, W.; Hasibeder, W. R. The association between body-mass index and patient outcome in septic shock: a retrospective cohort study. Wien Klin Wochenschr 2010 , 122 (1-2), 31-36. DOI: 10.1007/s00508-009-1241-4 From NLM. Atamna, A.; Elis, A.; Gilady, E.; Gitter-Azulay, L.; Bishara, J. How obesity impacts outcomes of infectious diseases. Eur J Clin Microbiol Infect Dis 2017 , 36 (3), 585-591. DOI: 10.1007/s10096-016-2835-1 From NLM. Pepper, D. J.; Sun, J.; Welsh, J.; Cui, X.; Suffredini, A. F.; Eichacker, P. Q. Increased body mass index and adjusted mortality in ICU patients with sepsis or septic shock: a systematic review and meta-analysis. Crit Care 2016 , 20 (1), 181. DOI: 10.1186/s13054-016-1360-z From NLM. Ahn, Y. H.; Yoon, S. M.; Lee, J.; Lee, S. M.; Oh, D. K.; Lee, S. Y.; Park, M. H.; Lim, C. M.; Lee, H. Y. Early Sepsis-Associated Acute Kidney Injury and Obesity. JAMA Netw Open 2024 , 7 (2), e2354923. DOI: 10.1001/jamanetworkopen.2023.54923 From NLM. Campbell, L.; Pepper, T.; Shipman, K. HbA1c: a review of non-glycaemic variables. J Clin Pathol 2019 , 72 (1), 12-19. DOI: 10.1136/jclinpath-2017-204755 From NLM. Wang, W.; Chen, W.; Liu, Y.; Li, L.; Li, S.; Tan, J.; Sun, X. Blood Glucose Levels and Mortality in Patients With Sepsis: Dose-Response Analysis of Observational Studies. J Intensive Care Med 2021 , 36 (2), 182-190. DOI: 10.1177/0885066619889322 From NLM. Balintescu, A.; Mårtensson, J. Hemoglobin A1c and Permissive Hyperglycemia in Patients in the Intensive Care Unit with Diabetes. Crit Care Clin 2019 , 35 (2), 289-300. DOI: 10.1016/j.ccc.2018.11.010 From NLM. Reno, C. M.; Daphna-Iken, D.; Chen, Y. S.; VanderWeele, J.; Jethi, K.; Fisher, S. J. Severe hypoglycemia-induced lethal cardiac arrhythmias are mediated by sympathoadrenal activation. Diabetes 2013 , 62 (10), 3570-3581. DOI: 10.2337/db13-0216 From NLM. Yan, F.; Chen, X.; Quan, X.; Wang, L.; Wei, X.; Zhu, J. Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning. Cardiovasc Diabetol 2024 , 23 (1), 163. DOI: 10.1186/s12933-024-02265-4 From NLM. Wang, H. E.; Shapiro, N. I.; Griffin, R.; Safford, M. M.; Judd, S.; Howard, G. Chronic medical conditions and risk of sepsis. PLoS One 2012 , 7 (10), e48307. DOI: 10.1371/journal.pone.0048307 From NLM. JinXiaojie. Hemodynamic Alterations and Primary Mechanism in Spontaneously Hypertensive Rats with Sepsis. master, Nantong University, 2006. Tsujikawa, A.; Kiryu, J.; Yamashiro, K.; Nonaka, A.; Nishijima, K.; Honda, Y.; Ogura, Y. Interactions between blood cells and retinal endothelium in endotoxic sepsis. Hypertension 2000 , 36 (2), 250-258. DOI: 10.1161/01.hyp.36.2.250 From NLM. Kim, J.; Kim, Y. A.; Hwangbo, B.; Kim, M. J.; Cho, H.; Hwangbo, Y.; Lee, E. S. Effect of Antihypertensive Medications on Sepsis-Related Outcomes: A Population-Based Cohort Study. Crit Care Med 2019 , 47 (5), e386-e393. DOI: 10.1097/ccm.0000000000003654 From NLM. Hong, S. Y.; Lai, C. C.; Teng, N. C.; Chen, C. H.; Hsu, C. C.; Chan, N. J.; Wang, C. Y.; Wang, Y. H.; Lin, Y. S.; Chen, L. Premorbid use of selective beta-blockers improves sepsis incidence and course: Human cohort and animal model studies. Front Med (Lausanne) 2023 , 10 , 1105894. DOI: 10.3389/fmed.2023.1105894 From NLM. Wiewel, M. A.; van Vught, L. A.; Scicluna, B. P.; Hoogendijk, A. J.; Frencken, J. F.; Zwinderman, A. H.; Horn, J.; Cremer, O. L.; Bonten, M. J.; Schultz, M. J.; et al. Prior Use of Calcium Channel Blockers Is Associated With Decreased Mortality in Critically Ill Patients With Sepsis: A Prospective Observational Study. Crit Care Med 2017 , 45 (3), 454-463. DOI: 10.1097/ccm.0000000000002236 From NLM. Lee, C. C.; Lee, M. G.; Lee, W. C.; Lai, C. C.; Chao, C. C.; Hsu, W. H.; Chang, S. S.; Lee, M. Preadmission Use of Calcium Channel Blocking Agents Is Associated With Improved Outcomes in Patients With Sepsis: A Population-Based Propensity Score-Matched Cohort Study. Crit Care Med 2017 , 45 (9), 1500-1508. DOI: 10.1097/ccm.0000000000002550 From NLM. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial2.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 04 Jul, 2025 Editor invited by journal 11 Jun, 2025 Editor assigned by journal 09 Jun, 2025 Submission checks completed at journal 09 Jun, 2025 First submitted to journal 04 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6816578","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481608810,"identity":"9f211c97-dae8-4ebb-af1a-5d324ee664af","order_by":0,"name":"Yuanqing Li","email":"","orcid":"","institution":"Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanqing","middleName":"","lastName":"Li","suffix":""},{"id":481608811,"identity":"a31bbff0-ab4b-4e87-a6af-d0b066351255","order_by":1,"name":"Cuiping Hao","email":"","orcid":"","institution":"Affiliated Hospital of Jining Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cuiping","middleName":"","lastName":"Hao","suffix":""},{"id":481608812,"identity":"3005fb40-2fd4-4814-8b99-04e004258eb5","order_by":2,"name":"Meiqi Liu","email":"","orcid":"","institution":"Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meiqi","middleName":"","lastName":"Liu","suffix":""},{"id":481608815,"identity":"13cda37e-8979-497b-9030-0a3973499d1f","order_by":3,"name":"Xuehui Zhang","email":"","orcid":"","institution":"Jining Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuehui","middleName":"","lastName":"Zhang","suffix":""},{"id":481608817,"identity":"0c00678a-6e40-4e84-a26a-acad30d64537","order_by":4,"name":"Anhao Liu","email":"","orcid":"","institution":"Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Anhao","middleName":"","lastName":"Liu","suffix":""},{"id":481608819,"identity":"e454c1e9-0597-4889-8f57-35811fb3c9f5","order_by":5,"name":"Ningkang Lv","email":"","orcid":"","institution":"Jining Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ningkang","middleName":"","lastName":"Lv","suffix":""},{"id":481608821,"identity":"ba0ac6a0-e416-48e6-828f-285a6a91d157","order_by":6,"name":"Weijie Li","email":"","orcid":"","institution":"Jining Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weijie","middleName":"","lastName":"Li","suffix":""},{"id":481608823,"identity":"9ea60754-54e3-4708-a7e0-d46476e18ba0","order_by":7,"name":"Dongmei Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACPmbmhgMMbBDOgQ8GNnYEtbAxMyK0HJxRkJZMWAsDYwMDTAszz4dDIC4BLeyMjYcLymzy5MMOHzxsY3CAmYH98NENhBx2eMa5tGLD22kJh3MM7vAx8KSl3SCohbftcOLG2TkGQC3PmBkkeMyI1ZL/4bCFwWHGBqK1zJfOYTjMQLQWnnNpiRuk0wwO9hikJbMR8gs//+HDn3nKbBLnz05+/OHHHxs7fvbDx/BqgQODAzB7iVIOAvINRCsdBaNgFIyCkQYATudL/UAHgW0AAAAASUVORK5CYII=","orcid":"","institution":"Jining Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dongmei","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-06-04 05:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6816578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6816578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86388908,"identity":"6ef0d8f6-ad72-4fa1-a1e7-5d9d98cc514d","added_by":"auto","created_at":"2025-07-10 06:25:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64853,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of patient selection\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6816578/v1/a8a43a955b4b6b9068630775.png"},{"id":86389362,"identity":"9f67a2c6-b057-4e6d-99dc-d2ab36838949","added_by":"auto","created_at":"2025-07-10 06:33:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69377,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves for 28-day survival (with 95% CI) are presented. Different colors represent the three groups of eGDR, and visually show the survival rates of the three groups at different time points.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6816578/v1/d3594416b824af9b866b127c.png"},{"id":86388910,"identity":"7197cc73-ea9e-4ba7-a65c-86a1b843ad46","added_by":"auto","created_at":"2025-07-10 06:25:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49842,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between eGDR and 28-day mortality rate. Adjustment factors included age, gender,race/ethnicity, sodium, chloride, bicarbonate, RDW, LAC, SOFA score,CRP, CCI, cerebrovascular disease, diabetes status,and cancer.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6816578/v1/da27de418cac8edef07d186d.png"},{"id":86388917,"identity":"29175718-0911-4043-9515-fc841a70d882","added_by":"auto","created_at":"2025-07-10 06:25:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":102162,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses for the association of eGDR with 28-day mortality, CI (confidence interval), HR (hazard ratio)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6816578/v1/e16bf7503f00b6916b889caa.png"},{"id":86390554,"identity":"d388d9f6-c09f-4267-be8b-b4278ec336bb","added_by":"auto","created_at":"2025-07-10 06:49:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1179504,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6816578/v1/6ca49c68-05c9-4a52-a7da-ffa09fc82aee.pdf"},{"id":86390168,"identity":"46c093d4-1de6-40e4-a6d2-48238f71b61a","added_by":"auto","created_at":"2025-07-10 06:41:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":42136,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6816578/v1/85ce3fe0e0d50a2746348873.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between estimated glucose disposal rate and 28-day mortality in patients with sepsis: analysis of the MIMIC‑IV database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis, arising from the host's disordered response to infection, induces a spectrum of physiological abnormalities and can result in multiple organ dysfunction and mortality\u003csup\u003e1\u003c/sup\u003e. Sepsis is one of the main causes of death among critically ill patients, with a mortality rate as high as 20\u0026ndash;50%, imposing a huge burden on society and the economy\u003csup\u003e2\u003c/sup\u003e.In sepsis, the body's stress response, including increased sympathetic nerve stimulation and elevated levels of adrenal cortical hormones, promotes glucose metabolism. This may lead to glucose metabolic disorders and insulin resistance (IR), further increasing the mortality rate of patients\u003csup\u003e3\u0026ndash;5\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTimely and stringent is crucial for significantly improving the prognosis of patients with sepsis\u003csup\u003e6\u003c/sup\u003e. Rigorous blood glucose control can significantly reduce morbidity, mortality, and hospital stays\u003csup\u003e7\u003c/sup\u003e. The estimated glucose disposal rate (eGDR) is a clinical measure that assesses the severity of IR using readily available data, including BMI, hypertension, and glycosylated hemoglobin (HbA1c)\u003csup\u003e8\u003c/sup\u003e. Low eGDR is associated with increased microvascular and large-vessel complications in individuals with type 1 diabetes mellitus (T1DM)\u003csup\u003e9, 10\u003c/sup\u003e. Recently, it has been shown that poorly controlled eGDR levels correlate with an elevated risk of stroke among middle-aged and elderly individuals\u003csup\u003e11\u003c/sup\u003e.Despite its potential implications, few studies have investigated the correlation between eGDR and sepsis prognosis. Consequently, we conducted a retrospective cohort study to examine the significance of eGDR in assessing sepsis patient outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData selection\u003c/h2\u003e\u003cp\u003eThis research utilized a retrospective observational design, drawing upon data from the publicly accessible MIMIC-IV database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mimic.mit.edu\u003c/span\u003e\u003cspan address=\"https://mimic.mit.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e12\u003c/sup\u003e. To access the database, an author of this study acquired the necessary certifications and then extracted the pertinent variables (authentication number: 12203293) for our research purposes.\u003csup\u003e13\u003c/sup\u003e. As the database contains only anonymised patient health information, individual patient consent and Clinical trial number were not required. The approach to identifying patients with sepsis who fulfilled the Sepsis-3.0 diagnostic criteria from the MIMIC database aligns with the methodology detailed in a previously reported study.\u003csup\u003e14\u003c/sup\u003e. Patients who had been in the ICU for less than 24 hours were excluded from the study. Furthermore, patients with insufficient data were excluded, for example, those with missing glycated haemoglobin and BMI measurements. The final study cohort consisted of 672 patients, who were divided into three groups based on the tertiles of the calculated eGDR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe collection of patient baseline characteristics was carried out utilizing SQL and PostgreSQL database system (version 14.2).The characteristics extracted included patient demographic data (age, sex, race,and vital signs), laboratory test results, and coexisting conditions, all obtained from the MIMIC-IV database.The eGDR index was calculated using the following formula: eGDR\u0026thinsp;=\u0026thinsp;19.02 - [0.22\u0026times;BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)] - [3.26\u0026times;hypertension (presence)] - [0.61\u0026times;glycated haemoglobin (%)],with hypertension (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0)\u003csup\u003e8, 15\u003c/sup\u003e.Multiple interpolation, was employed to enhance statistical power and reduce bias resulting from missing data.To minimize the bias arising from multiple imputation, we also conducted sensitivity analyses. We performed descriptive analyses and multivariate Cox regression analyses using different multiple imputation methods (Supplementary Tables\u0026nbsp;1 and 2) and found that the results were relatively stable .\u003c/p\u003e\n\u003ch3\u003ePrimary outcome\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint of this study in patients with sepsis was 28-day mortality following ICU admission.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe Kolmogorov-Smirnov test was used to assess variable distributions for normality. Continuous variables with normal distribution were depicted as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and median (interquartile range [IQR]) was used for those not normally distributed. Group differences were analyzed using the Kruskal-Wallis H test for skewed variables, one-way ANOVA for normally distributed variables, and the Chi-square test for categorical variables.\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMultivariate Cox regression models were utilized to compute hazard ratios (HR) and 95% confidence intervals (95% CI) to quantify the effect of eGDR on these outcomes, excluding participants lost to follow-up at this time point. The 28-day mortality outcome was assessed using Kaplan-Meier survival curves for eGDR tertiles and evaluated with log-rank tests. We selected these confounders based on expertise, prior scientific literature, and all significant covariates identified in univariate analyses.\u003c/p\u003e\u003cp\u003eWe performed sensitivity analyses by constructing multiple Cox regression models with incremental adjustments to ensure the robustness of the research results. The eGDR was included in the models both as a categorical variable (tertiles) and as a continuous variable.Four models were constructed: Model 1 included only eGDR (unadjusted), Model 2 adjusted for age and sex, and Model 3 further adjusted for age, sex, race/ethnicity, sodium, chloride, bicarbonate, red blood cell distribution width (RDW), lactate (LAC) and c-reactive protein (CRP). In model 4,besides the variables included in Model 3, the Sequential Organ Failure Assessment (SOFA) score, charlson comorbidity index (CCI), cerebrovascular disease, diabetes status,and cancer were incorporated. In addition, as a form of sensitivity analysis, we conducted stratified analyses on different subgroups. This analysis yielded hazard ratio (HR) and their corresponding 95% Confidence intervals (95%CIs). No significant interactions were observed in these subgroup. The effect sizes and corresponding p-values derived from these models were documented and contrasted. R software (version 4.2.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, The R Foundation) and the Free Statistics analytic platform (version 1.9, Beijing, China, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.clinicalscientists.cn/freestatistics\u003c/span\u003e\u003cspan address=\"http://www.clinicalscientists.cn/freestatistics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used for all statistical analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003ePatient characteristics and outcome\u003c/h2\u003e\n \u003cp\u003eOf the total 50,955 patients, 672 were included in the analysis. We excluded 49,619 patients due to missing glycosylated hemoglobin (HbA1c) data. An additional 652 patients were excluded because of incomplete body mass index (BMI) records, and ultimately, 12 patients were excluded due to ICU stays shorter than 24 hours (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristics of participants stratified by eGDR. The 28-day mortalitywew 17.4%. We enrolled 672 patients with a mean age of 65.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4 years and 58.8% male. The eGDR varied significantly across tertiles: T1 (5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 mg/kg/min), T2 (8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 mg/kg/min), and T3 (10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 mg/kg/min) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The SOFA score was elevated in T3 (3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0) compared to both T1 (3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). BMI was peaking in T1 at 32.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3. HbA1c and glucose levels were inversely related to eGDR, with T1 showing higher mean of 7.9% and 173.8 mg/dL, respectively, compared to T3\u0026apos;s 5.8% and 138.0 mg/dL \u003cem\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/em\u003e. Potassium levels varied by eGDR tertile, with T1 showing a lower mean of 4.2 mEq/L\u0026nbsp;\u003cem\u003e(P\u0026lt;0.05)\u003c/em\u003e. To reduce selection bias, sensitivity analysis using multiple imputation of missing datasets corroborated these findings (see Supplementary Table 3).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics according to eGDR Tri-sectional Quantile\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;672)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT1 (N\u0026thinsp;=\u0026thinsp;224)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT2 (N\u0026thinsp;=\u0026thinsp;223)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT13(N\u0026thinsp;=\u0026thinsp;225)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e277 (41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83(37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e395 (58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133 (59.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace/ethnicity,n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e188 (28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite people\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e391 (58.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138 (61.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121 (54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeart rate (times)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.9\u0026thinsp;\u0026plusmn;\u0026thinsp;20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.1\u0026thinsp;\u0026plusmn;\u0026thinsp;20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107.2\u0026thinsp;\u0026plusmn;\u0026thinsp;21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResp rate (times)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOFA score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPSIII score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.2\u0026thinsp;\u0026plusmn;\u0026thinsp;22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.8\u0026thinsp;\u0026plusmn;\u0026thinsp;20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.4\u0026thinsp;\u0026plusmn;\u0026thinsp;23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.3\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSAPII score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOASIS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharlson comorbidity index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28-day mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaboratory tests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeGDR(mg/kg/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e154.2\u0026thinsp;\u0026plusmn;\u0026thinsp;56.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173.8\u0026thinsp;\u0026plusmn;\u0026thinsp;62.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151.0\u0026thinsp;\u0026plusmn;\u0026thinsp;56.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138.0\u0026thinsp;\u0026plusmn;\u0026thinsp;43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium(mEq/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium(mEq/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChloride(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBicarbonate(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAG(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCV (fl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCHC(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAC(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.9\u0026thinsp;\u0026plusmn;\u0026thinsp;103.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.6\u0026thinsp;\u0026plusmn;\u0026thinsp;109.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.0\u0026thinsp;\u0026plusmn;\u0026thinsp;85.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.1\u0026thinsp;\u0026plusmn;\u0026thinsp;112.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDW(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC (\u0026times;10^9/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.2 (8.6, 15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.9 (8.6, 14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.4 (9.1, 15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.2 (8.2, 15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158 (23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e216 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115 (51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCerebrovascular disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e259 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 ( 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: Values are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median (interquartile range), or n (%). BMI, body mass index; SOFA, sequential organ failure assessment; APSIII, acute physiology and chronic health evaluation III; SAPII, simplified acute physiology score II; OASIA, oxford acute severity of illness score; eGDR, estimated glucose disposal rate; AG, anion gap; BE, base excess; MCV, ,mean corpuscular volume; MCHC, mean corpuscular hemoglobin concentration; LAC, lactate; CRP, c-reactive protein; RDW, red cell distribution width; WBC, white blood cell count.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eeGDR and 28‑day mortality\u003c/h3\u003e\n\u003cp\u003eMultiple regression analysis findings are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, which indicates that in unadjusted models, there is a significant correlation between eGDR and 28-day mortality (HR\u0026thinsp;=\u0026thinsp;1.51, 95% CI: 1.20\u0026ndash;1.89). The mortality incidence increased by 51% for every unit rise in eGDR. In the multivariate regression model, after adjusting for age and gender, the HR was 1.48 (95% CI: 1.18\u0026ndash;1.86). After adjusting for age, sex, race/ethnicity, sodium, chloride, bicarbonate, RDW, LAC, and CRP, the HR was 1.53 (95% CI: 1.21\u0026ndash;1.94). After further adjusting for age, sex, race/ethnicity, sodium, chloride, bicarbonate, RDW, LAC, CRP, SOFA score, harlson comorbidity index (CCI), cerebrovascular disease, diabetes status and cancer, the HR was 1.50 (95% CI: 1.17\u0026ndash;1.92). Compared with the lowest tertile of eGDR index, the HR of 28-day mortality in the highest tertile of eGDR index was 2.56 (95%CI: 1.48\u0026ndash;4.06), independent of potential confounding factors. Sensitivity analyses demonstrated consistent associations between eGDR and 28-day mortality across various adjustment strategies.\u003c/p\u003e\n\u003cp\u003eThe Kaplan-Meier analysis plot showed the eGDR had a significant association with the 28-day mortality (log-rank test \u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.00021\u003c/em\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).The survival rates of groups T1 was higher than those in T2 and T3 groups, and the T3 group described the lowest survival probability at the time point of 28 days. At 28 days, the survival rates of groups T1, T2 and T3 were 94.9%, 84.1% and 83.8% respectively. The curve-fitting plot (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) demonstrated a positive correlation between eGDR and the risk of 28-day mortality. As eGDR increased,hazard Ratio for death within 28 days also increased, suggesting that eGDR may be an important predictor of the risk of death within 28 days. However, when eGDR reached a certain value (e.g., 8.435), the extent of risk reduction decreased significantly, and the curve tended to flatten.This suggests that there may be a threshold effect.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariable cox regression to assess the association of eGDR with 28-day mortality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eeGDR(n\u0026thinsp;=\u0026thinsp;672)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eTertiles of eGDR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;672)*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1(n\u0026thinsp;=\u0026thinsp;224)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2(n\u0026thinsp;=\u0026thinsp;223)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3(n\u0026thinsp;=\u0026thinsp;225)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.51 (1.2\u0026thinsp;~\u0026thinsp;1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.64 (1.57\u0026thinsp;~\u0026thinsp;4.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.64 (1.57\u0026thinsp;~\u0026thinsp;4.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48 (1.18\u0026thinsp;~\u0026thinsp;1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.68 (1.53\u0026thinsp;~\u0026thinsp;4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.68 (1.53\u0026thinsp;~\u0026thinsp;4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53(1.21\u0026thinsp;~\u0026thinsp;1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.39 (1.41\u0026thinsp;~\u0026thinsp;4.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.63 (1.56\u0026thinsp;~\u0026thinsp;4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 4\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5 (1.17\u0026thinsp;~\u0026thinsp;1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.37(1.38\u0026thinsp;~\u0026thinsp;4.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.56(1.48\u0026thinsp;~\u0026thinsp;4.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e* eGDR was entered as a continuous variable\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003ea. Model 1: unadjusted models\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eb. Model 2: additionally adjusted for age,sex\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003ec. Model 3: adjusted as for model 2, additionally adjusted for race/ethnicity, sodium, chloride, bicarbonate,RDW, LAC and CRP\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003ed. Model 4: adjusted as for model 3, additionally adjusted for SOFA score, charlson comorbidity index (CCI), cerebrovascular disease, diabetes status and cancer\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eSubgroup Analysis\u003c/h3\u003e\n\u003cp\u003eIn the subgroup analysis of the relationship between eGDR and 28 - day sepsis mortality, despite non - significant interaction effects across age, gender, race, SOFA score, and diabetes subgroups (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This is an indication that the relationship between eGDR and mortality is robust in these subgroups. In age subgroups, eGDR had a weaker predictive power for those\u0026thinsp;\u0026lt;\u0026thinsp;60 years old (HR\u0026thinsp;=\u0026thinsp;1.17, 95% CI: 0.95\u0026ndash;1.44) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In both genders, a positive eGDR-mortality association was observed. The analysis revealed that eGDR was consistently associated with an increased event rate in most subgroups, with statistical significance observed in both black and white racial subgroups, as well as in the non-diabetic subgroup, that is, eGDR was significantly associated with sepsis mortality. Considering thatHbA1c is usually measured in ICU patients only if there is a history of diabetes or hyperglycemia, a limitation that may affect the generalizability of the findings. We therefore performed subgroup analyses of patients with a history of diabetes or hyperglycemia and those without such a history to assess differences in results (Supplementary Table 4 ).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe predictive value of eGDR for sepsis is under investigation for the first time in our study.The main findings could be summarized as: (1)higher eGFR was significantly associated with higher sepsis mortality.(2)A linear relationship was observed between sepsis mortality and eGDR.(3)The eGDR significantly enhanced the predictive power of basic models for adverse outcomes in sepsis.\u003c/p\u003e\u003cp\u003eThis is the first retrospective study conducted to investigate the correlation between eGDR and mortality in critical care sepsis. A total of 672 sepsis cases were included in the study.FramRY showed that insulin resistance is present in some septic patients and that the degree of insulin resistance correlates with morbidity and mortality\u003csup\u003e17\u003c/sup\u003e.There are many tools and methods for detecting insulin resistance, and the hyperinsulinemic euglycemic clamp (HEC) is recognized as the international gold standard for assessing insulin sensitivity in vivo\u003csup\u003e18\u003c/sup\u003e.Large-scale clinical trials are not appropriate for the hyperinsulinemic euglycemic clamp (HEC), despite the fact that it is currently the globally recognized gold standard for assessing insulin sensitivity in vivo. Therefore, one way to measure the degree of insulin resistance is to use the eGDR. Research has indicated that a low eGDR is associated with a poor prognosis for both those with non-alcoholic fatty liver disease and type 2 diabetes mellitus\u003csup\u003e19, 20\u003c/sup\u003e.Our results showed that high eGDR increased the risk of sepsis mortality This may be due to the composition of the formula, and the formula used to calculate eGDR includes BMI, glycated haemoglobin and hypertension status.\u003c/p\u003e\u003cp\u003eIt has been shown that overweight and obese septic patients tend to have a lower mortality rate, which is known as the\u0026lsquo;obesity paradox\u0026rsquo;\u003csup\u003e21\u0026ndash;23\u003c/sup\u003e. A meta-analysis suggests that adjusted mortality is actually reduced in overweight or obese patients with sepsis or septic shock treated in the ICU\u003csup\u003e24\u003c/sup\u003e. This inverse epidemiological phenomenon of BMI in sepsis may be due to the fact that,obese patients are in a state of high glycolipid reserve over an extended period of time and the excess body fat can be used as a protective energy store to prevent muscle loss due to the high catabolic state in sepsis\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGlycosylated haemoglobin (HbA1c) is the product of a slow, non-enzymatic glycation reaction of haemoglobin in the presence of high blood glucose and its level reflects glycaemic control 8\u0026ndash;12 weeks prior to testing\u003csup\u003e26\u003c/sup\u003e. It has been suggested that the presence of elevated blood glucose in the early phases of the condition is a protective and adaptive response by the body, and that U-shaped correlation exists between blood glucose levels and the risk of mortality\u003csup\u003e27\u003c/sup\u003e.Mild or moderate stress hyperglycaemia is a protective mechanism for critically ill patients. In adult T1DM patients in Sweden, with HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;7.0% and HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;6.1% were associated with higher sepsis risk\u003csup\u003e28\u003c/sup\u003e. The poor prognosis between hyperglycaemia and sepsis in previous studies may be due to the adverse events associated with hypoglycaemia as a result of intensive glycaemic control\u003csup\u003e29\u003c/sup\u003e. Furthermore, some studies have suggested that hyperglycaemia on admission is not associated with sepsis prognosis\u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHypertension as a contributing risk factor for sepsis\u003csup\u003e31\u003c/sup\u003e and it was found that hypertensive rats suffering from sepsis had a better final prognosis than controls in the early stages, although the values of haemodynamic parameters were higher than those of the control group\u003csup\u003e32, 33\u003c/sup\u003e.This may be because patients with a history of hypertension are taking antihypertensive medication. It has been suggested that antihypertensive drugs may affect the prognosis of patients with sepsis\u003csup\u003e34\u003c/sup\u003e, and results from animal studies have shown that Pretreatment with the β-blocker atenolol reduces sepsis mortality in mice mortality in mice\u003csup\u003e35\u003c/sup\u003e.Other commonly used antihypertensive drugs are calcium channel blockers (CCBs). Several clinical trials have shown that CCBs have anti-inflammatory effects, and previous use of CCBs has been associated with decreased mortality in sepsis patients or in intensive care patients with severe sepsis\u003csup\u003e36, 37\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur research showed that in critically ill sepsis patients, a higher eGDR is associated with a higher risk of 28-day all-cause mortality. This suggests that eGDR can be used to predict the risk of adverse outcomes in patients with sepsis. It is important to note that this is an observational study and cannot establish causality. The observed association may be influenced by unmeasured or residual confounding factors. Therefore, our findings should be interpreted with caution.There are various restrictions on this study. First of all, because it is a retrospective study based on historical records, it could be skewed by information. Secondly, there was substantial missing data in the MIMIC database, which led to the inclusion of a limited number of cases, potentially introducing some bias. Thirdly, the calculation formula for the eGDR was mainly derived from the MIMIC - IV data, which predominantly represents the U.S. population.However, its generalizability to populations with different racial and body - mass index (BMI) distributions remains a crucial concern. Fourthly, during the data processing stage, a large amount of missing data was excluded, which might introduce selection bias. Future studies should consider these limitations and aim to address them through larger, prospective cohorts or randomized controlled trials.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHigh levels of eGDR (an indicator of insulin resistance) are associated with an increased risk of 28-day mortality in sepsis patients. Nonetheless, additional prospective studies are required to elucidate this relationship.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the database contains only anonymised patient health information, individual patient consent and Clinical trial number were not required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that this manuscript is original, has\u0026quot; not been\u0026apos; published before and is not currently being considered for publication elsewhere.We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available in [MIMIC-IV database] at https://mimic.mit.edu. In addition, we submit the data used in the article as an attachment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funds, grants, or other support was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. LYQ and HCP contributed to the study conception and design. Material preparation and data collection were performed by LMQ, ZXH and LAH. The first draft of the manuscript was written by LYQ. LNK, LWJ and WDM reviewed and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEvans, L.; Rhodes, A.; Alhazzani, W.; Antonelli, M.; Coopersmith, C. M.; French, C.; Machado, F. R.; McIntyre, L.; Ostermann, M.; Prescott, H. C.; et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. \u003cem\u003eIntensive Care Med \u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e47\u003c/em\u003e (11), 1181-1247. DOI: 10.1007/s00134-021-06506-y From NLM.\u003c/li\u003e\n\u003cli\u003eDarb\u0026agrave;, J.; Mars\u0026agrave;, A. 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Preadmission Use of Calcium Channel Blocking Agents Is Associated With Improved Outcomes in Patients With Sepsis: A Population-Based Propensity Score-Matched Cohort Study. \u003cem\u003eCrit Care Med \u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e45\u003c/em\u003e (9), 1500-1508. DOI: 10.1097/ccm.0000000000002550 From NLM.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"estimated glucose disposal rate, Sepsis, 28-day mortality, MIMIC-IV database","lastPublishedDoi":"10.21203/rs.3.rs-6816578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6816578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe aim of the study was to examine the correlation between estimated glucose disposal rate (eGDR) and 28-day mortality in septic patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing information from the MIMIC IV 2.1 database, we conducted a retrospective cohort analysis. We gathered patient demographics and used the eGDR to characterize insulin resistance. Participants were stratified into tertiles based on eGDR values and constructed Cox regression models with 28-day mortality in sepsis patients as the primary outcome measure. In order to clearly depict the relationship between eGDR and 28-day mortality, we used Kaplan\u0026ndash;Meier (KM) curves and curve fitting. These models were designed to actively test for a potential possible correlation between eGDR and 28-day mortality in sepsis patients. Subgroup analysis was performed to explore the stability of the primary results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eEnrolling a total of 672 patients, 58.8% were male and the 28-day mortality was 17.4%. An association was found between higher eGDR and higher 28-day mortality using multivariate cox regression analysis. Hazard ratio (HR) for 28-day mortality per unit increase in eGDR was 1.48[95% CI 1.16\u0026ndash;1.89]. Compared to patients in the lowest tertile of eGDR, those in the highest tertile exhibited a higher 28-day mortality rate (HR, 2.53 [95% CI 1.49\u0026ndash;4.37], adjusting for confounding factors). Subgroup analyses demonstrated that the effect of eGDR on mortality was robust across subgroups.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe eGDR is correlated with the28-day mortality in sepsis patients. Larger prospective studies are required in the future to further validate this finding.\u003c/p\u003e","manuscriptTitle":"Association between estimated glucose disposal rate and 28-day mortality in patients with sepsis: analysis of the MIMIC‑IV database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 06:25:05","doi":"10.21203/rs.3.rs-6816578/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-07-04T10:55:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-11T08:20:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T01:49:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-10T01:49:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-06-04T05:25:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c0cdd172-124a-4c60-a854-d92788879eb3","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-10T06:25:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-10 06:25:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6816578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6816578","identity":"rs-6816578","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}