Assessing the prognostic value of Age-adjusted EASIX for predicting mortality in critically ill surgical patients: a retrospective cohort study

Assessing the prognostic value of Age-adjusted EASIX for predicting mortality in critically ill surgical patients: a retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing the prognostic value of Age-adjusted EASIX for predicting mortality in critically ill surgical patients: a retrospective cohort study Zhitao Zhong, Qiong Long This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7364770/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Mar, 2026 Read the published version in European Journal of Medical Research → Version 1 posted 14 You are reading this latest preprint version Abstract Purpose: Critically ill surgical patients face high mortality rates, necessitating early and accurate risk stratification. The Age-Adjusted Endothelial Activation and Stress Index (aEASIX), a simple biomarker for endothelial dysfunction, has shown promise in other cohorts but remains uninvestigated in a broad surgical intensive care unit (SICU) population. This study aimed to evaluate the association between the aEASIX upon SICU admission and short-term mortality in critically ill surgical patients. Patients and methods: This retrospective cohort study utilized data from the MIMIC-IV database. We included 4,394 adult patients with a first-time admission to a SICU. Because the aEASIX distribution was markedly skewed, we applied a natural-log transformation (LnaEASIX). The primary outcome was 28-day ICU mortality, and the secondary outcome was 28-day in-hospital mortality. Multivariable logistic regression, restricted cubic splines, and receiver operating characteristic (ROC) curve analyses were performed. A mediation analysis explored the role of the SOFA score. Results: The overall 28-day ICU and 28-day in-hospital mortality rates were 18.0% and 17.09%, respectively. After adjusting for confounders, a higher LnaEASIX was independently associated with an increased risk of both 28-day ICU mortality (OR 1.348, 95% CI 1.259-1.445, P < 0.001) and 28-day in-hospital mortality (OR 1.367, 95% CI 1.275-1.465, P < 0.001). A non-linear, dose-response relationship was observed for both outcomes. In predictive performance analysis, aEASIX demonstrated significantly better discrimination than the SOFA score for both 28-day ICU mortality (AUC: 0.653 vs. 0.625) and 28-day in-hospital mortality (AUC: 0.649 vs. 0.614). Mediation analysis revealed that the SOFA score mediated 17.6% and 14.7% of the total effect of LnaEASIX on 28-day ICU and 28-day in-hospital mortality, respectively. Conclusion: The aEASIX is a significant and independent predictor of short-term mortality in a large, heterogeneous cohort of critically ill surgical patients. As a simple, readily available tool, aEASIX outperforms the SOFA score in predicting both ICU and in-hospital mortality and may serve as a valuable instrument for early bedside risk stratification in the SICU. Age-Adjusted Endothelial Activation and Stress Index surgical intensive care unit mortality risk MIMIC-IV database Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction With over 310 million major surgical procedures performed annually worldwide 1 , a growing subset of these patients requires intensive care, imposing a substantial burden on healthcare systems. The number of critically ill surgical patients is steadily increasing, now accounting for up to one-third of all intensive care unit (ICU) admissions in some reports 2 3 . Despite advances in perioperative medicine, this population continues to experience high mortality rates, which vary starkly from under 25% in high-income nations to as high as 50% in resource-limited settings 3 – 7 . This clinical challenge is compounded by a significant economic impact, with critical care consuming up to 1% of the gross domestic product in some developed countries 2 8 9 . Given the scale of this population, its high mortality, and immense cost, the need for early and accurate risk stratification is paramount to improving outcomes and optimizing resource allocation. In this high-stakes context, early and accurate risk stratification upon admission to the surgical intensive care unit (SICU) is paramount for optimizing treatment and allocating resources. While established scoring system like the Sequential Organ Failure Assessment (SOFA) is widely used, 10 they can be complex to calculate and may not fully capture the specific pathophysiological mechanisms driving adverse outcomes. 11 12 The Endothelial Activation and Stress Index (EASIX), a simple score derived from lactate dehydrogenase (LDH), platelet (PLT) count, and serum creatinine (Scr), was specifically developed to quantify endothelial damage. 13 – 15 However, the original EASIX score omits age, a powerful and independent predictor of mortality in nearly all critically ill populations. 16 17 Consequently, the Age-Adjusted EASIX (aEASIX) was developed to incorporate this crucial variable. The prognostic utility of aEASIX has been validated in other critically ill cohorts, such as patients with severe COVID-19, where it demonstrated superior predictive performance for ICU mortality compared to the SOFA score. 18 Despite this promising evidence, the prognostic value of aEASIX in a broad, unselected population of critically ill surgical patients remains largely uninvestigated. Therefore, this study aimed to investigate the association between the aEASIX and short-term mortality in a large cohort of critically ill surgical patients. Methods Data source This study is a retrospective cohort study based on data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v 3.1) database. MIMIC-IV is a large, publicly available database containing de-identified electronic health records for patients admitted to ICU at the Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, between 2008 and 2022. The establishment of the database was approved by the institutional review boards of both the Massachusetts Institute of Technology (MIT) and BIDMC. One of the authors (Z.Z., to be filled in) was granted access to the database after completing the required training (Certificate number: 47608458). As the database is de-identified, the requirement for individual patient consent was waived. Participant selection We initially identified 21,390 patients admitted to the Surgical ICU (SICU), Trauma Surgical ICU (TSICU), or Neurosurgical ICU (Neuro SICU) within the MIMIC-IV database. The inclusion criterion was adult patients (aged ≥ 18 years) with a first-time admission to one of the specified surgical ICUs. The exclusion criteria were as follows: (1) patients who were not on their first ICU admission during their first hospitalization; (2) patients with missing key data on the first day of ICU admission required for the calculation of the aEASIX (age, Scr, LDH, or PLT). Clinical outcomes The primary outcome of this study was 28-day ICU mortality. The secondary outcome was 28-day in-hospital mortality. Data extraction Data extractions were performed using the PostgreSQL software. We collected the following data from the first 24 hours of ICU admission: Baseline characteristics: Age, gender, race, and weight. Comorbidities and Complications: History of hypertension, diabetes, and the presence of acute kidney injury (AKI). Vital Signs: Heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP), respiratory rate, temperature, and pulse oxygen saturation (SpO₂). Laboratory Tests: laboratory parameters including hematological tests [white blood cell (WBC) count, platelet (PLT) count, hemoglobin, hematocrit, red blood cell count, and red cell distribution width (RDW)], renal and metabolic panels [blood urea nitrogen (BUN), serum creatinine (Scr), serum glucose, sodium, potassium, chloride, and calcium], coagulation profiles [prothrombin time (PT), partial thromboplastin time (PTT), and international normalized ratio (INR)], and liver function markers [lactate dehydrogenase (LDH), total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and albumin], and Sequential Organ Failure Assessment (SOFA) score. The primary exposure variable, the Age-Adjusted Endothelial Activation and Stress Index (aEASIX), was calculated using the formula: aEASIX = age (years) *[Scr (mg/dL) * LDH (IU/L) / PLT (10⁹/L)]. 19 Statistical analysis Continuous variables were presented as median (interquartile range, IQR) due to their skewed distribution, while categorical variables were expressed as numbers (percentages). Because the aEASIX distribution was markedly skewed, we applied a natural-log transformation (LnaEASIX) for all regression analyses. Patients were categorized into four groups based on the quartiles of their LnaEASIX. Differences in baseline characteristics across these quartiles were compared using the Kruskal-Wallis test for continuous variables and the chi-square test for categorical variables. To build the multivariable models, we first used univariate binary logistic regression to screen for potential confounders associated with mortality. Variables with a P-value 5. 21 Multivariable logistic regression analysis was performed to assess the association between LnaEASIX (as both a continuous variable and a categorical variable by quartiles) and the mortality outcomes. We constructed three models: Non-adjusted model: No covariates were adjusted. Model Ⅰ (Minimally-adjusted): Adjusted for weight. Model Ⅱ (Fully-adjusted): Adjusted for covariates included in Model I plus WBC, RDW, PTT, BUN, respiratory rate, and AKI. Results were presented as odds ratios (OR) with 95% confidence intervals (CI). To explore the potential non-linear relationship between LnaEASIX and mortality, we employed restricted cubic splines (RCS) with three knots. If a non-linear association was detected, a two-piecewise linear regression model was used to calculate the threshold effect and identify the inflection point. The predictive performance of aEASIX for mortality was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with the SOFA score. Kaplan-Meier survival curves were plotted to visualize survival differences among the LnaEASIX quartiles, and the log-rank test was used for comparison. Subgroup analyses were conducted across various strata, including age, gender, hypertension, diabetes, and AKI, to assess the robustness of our findings. Interaction tests were performed to examine whether the effect of LnaEASIX on mortality differed across these subgroups. Finally, a mediation analysis was conducted to explore whether the SOFA score mediated the relationship between LnaEASIX and mortality. Variables with missing data were assessed, and as the proportion of missingness was low for all included variables (all < 5%), multiple imputation was performed. All statistical analyses were conducted using R software (version 4.4.1). P-value < 0.05 was considered statistically significant. 22 Results Baseline characteristics Following the screening process detailed in the patient selection flowchart ( Figure 1 ), a total of 4,394 patients were included in the final analysis. The median age of the cohort was 64.0 years (IQR 52.0-76.0), with 57.5% being male. The overall 28-day ICU mortality and 28-day in-hospital mortality rates were 18.0% and 17.09%, respectively. Patients were stratified into four groups based on quartiles of their LnaEASIX. As detailed in Table 1, patients in the higher LnaEASIX quartiles exhibited a greater burden of critical illness. Specifically, with increasing LnaEASIX quartiles, there was a significant trend towards higher SOFA scores (median: 2.0 in Q1 vs. 8.0 in Q4), elevated levels of Scr, LDH, BUN, and PTT. Conversely, patients in the highest quartile (Q4) had significantly lower PLT (median: 272 K/µL in Q1 vs. 122 K/µL in Q4) and hemoglobin levels. Crucially, both 28-day ICU mortality (8.8% in Q1 to 29.1% in Q4) and 28-day in-hospital mortality (8.3% in Q1 to 27.0% in Q4) increased progressively and significantly across the quartiles ( P < 0.05). Association between LnaEASIX and mortality After multivariable adjustment for weight, WBC, RDW, PTT, BUN, respiratory rate, and AKI (Model II, Table 2), LnaEASIX remained an independent predictor of both 28-day ICU mortality (OR 1.348, 95% CI 1.259-1.445, P < 0.001) and 28-day in-hospital mortality (OR 1.367, 95% CI 1.275-1.465, P < 0.001). When analyzed by quartiles, a clear graded relationship was observed. Compared to the lowest quartile (Q1), patients in the highest quartile (Q4) had a significantly increased risk of 28-day ICU mortality (Model II: OR 2.927, 95% CI 2.222-3.877, P < 0.001) and 28-day in-hospital mortality (Model II: OR 3.083, 95% CI 2.327-4.111, P < 0.001) ( P for trend < 0.001). Dose-Response Relationship and Threshold Effect The RCS plots revealed a non-linear, monotonically increasing association for both 28-day ICU mortality ( P for non-linearity = 0.014) and 28-day in-hospital mortality ( P for non-linearity = 0.002) (Figure 2A and 2B). The risk of mortality increased steeply at lower levels of LnaEASIX and then began to plateau at higher levels. A two-piecewise linear regression model was fitted to identify the inflection points (Table 3). For 28-day ICU mortality, an inflection point was identified at an LnaEASIX value of 5.19. Below this threshold, each unit increase in LnaEASIX was associated with a sharp 63% rise in mortality risk (OR 1.63, 95% CI 1.37-1.93, P < 0.001). Above this point, the association remained significant but was attenuated (OR 1.19, 95% CI 1.04-1.35, P = 0.010). A similar pattern was observed for 28-day in-hospital mortality, with an inflection point at 5.14 ( P for likelihood test < 0.05). Subgroup and Survival Analysis To confirm the robustness of our findings, we performed subgroup analyses stratified by age, gender, hypertension, diabetes, and AKI. As shown in the forest plots (Figure 3A and 3B), the positive association between higher LnaEASIX and increased mortality was consistent across all examined subgroups for both 28-day ICU and in-hospital mortality. No significant interactions were detected (all P for interaction > 0.05). Kaplan-Meier survival analysis demonstrated a clear separation in survival probabilities among the LnaEASIX quartiles (Figure 4A and 4B). Patients in the highest quartile (Q4) had a significantly lower 28-day survival probability compared to those in the lower quartiles for both ICU and in-hospital survival, with the lowest quartile (Q1) exhibiting the best survival outcome (Log-rank test, P < 0.05). Predictive Performance The predictive utility of the composite LnaEASIX surpassed that of its individual components (age, LDH, Scr, and PLT). Furthermore, when benchmarked against the established SOFA score, LnaEASIX demonstrated significantly better discriminative ability for predicting both 28-day ICU mortality (AUC: 0.653 vs. 0.625) and 28-day in-hospital mortality (AUC: 0.649 vs. 0.614) (Figure 5A and 5B). Mediation Analysis For 28-day ICU mortality, the SOFA score was found to mediate 17.581% of the total effect of LnaEASIX on mortality. Similarly, it mediated 14.731% of the total effect for 28-day in-hospital mortality ( P < 0.05), suggesting that a notable portion of the prognostic impact of LnaEASIX is channeled through the development or severity of organ dysfunction as measured by SOFA (Figure 6). Discussion To our knowledge, this study is the first to demonstrate that the Age-Adjusted Endothelial Activation and Stress Index (aEASIX) is an independent predictor of short-term mortality in a broad population of critically ill surgical patients. After multivariable adjustment, we established that a higher aEASIX upon SICU admission is significantly linked to an increased risk of both 28-day ICU and in-hospital mortality. This association was underscored by a clear dose-response relationship and was robust across various subgroups. As a corollary, a higher aEASIX corresponded to a significantly lower survival probability. Of note, the predictive performance of aEASIX for mortality appeared to exceed that of the conventional SOFA score. Our findings are consistent with and significantly extend the results of previous research that validated aEASIX in more specific patient populations. Perez-Garcia et al. reported that after multivariable adjustment, a higher log2-aEASIX was associated with a significantly increased risk of 28-day mortality (Hazard Ratio [HR] = 1.61, P < 0.001) and demonstrated excellent discrimination with an AUC of 0.827. 19 Similarly, Jeong et al. found that log2-aEASIX was significantly associated with ICU mortality in a multivariable model (OR = 1.56, P < 0.001) and showed superior predictive performance compared to the SOFA score (AUC 0.730 vs. 0.660, P = 0.0016). 18 While these studies established the principle that a marker of endothelial injury is prognostically important in the context of a specific viral-induced endotheliopathy, 23 24 the unique contribution of our study lies in its application to a large, heterogeneous SICU cohort. This broadens the generalizability of aEASIX, suggesting its utility is not confined to specific viral or immunological conditions but reflects a fundamental pathophysiological process common to many forms of surgical critical illness. The prognostic power of aEASIX lies in its dual function: acting as a surrogate marker for systemic endothelial injury and dysfunction, 25 while simultaneously accounting for age-related vulnerability. This endothelial insult is a central pathological process in critically ill surgical patients, often triggered by insults like extensive tissue damage, ischemia-reperfusion, and systemic inflammation. 26 – 28 Physiological reserves differ across age groups, making age a critical independent determinant of outcomes in trauma; 29 therefore, by integrating age, aEASIX provides a more accurate, individualized risk assessment. Our mediation analysis, which found that the SOFA score partially mediates the effect of aEASIX on mortality, supports this biological rationale. This leads to our proposed causal pathway: aEASIX captures the initial, age-modulated endothelial damage, which precedes and directly contributes to the multi-organ failure syndrome, and these factors jointly promote the death of critically ill surgical patients. The primary advantage of aEASIX is its simplicity and immediate availability. 12 It is calculated from four parameters (age, LDH, Scr, PLT) that are routinely measured upon ICU admission, making it an easy-to-implement, cost-effective tool for rapid, bedside risk assessment. The subgroup analyses revealed a consistent association between aEASIX and mortality across different strata of age, gender, and comorbidities, underscoring its robustness. The identification of a clear dose-response relationship and a specific inflection point provides clinicians with a tangible threshold for identifying patients who may warrant more aggressive monitoring, earlier intervention, or consideration for advanced therapies. 30 This could be particularly valuable for optimizing resource allocation and facilitating more informed and timely prognostic discussions with patients and their families. Despite its strengths, our study has several limitations that must be acknowledged. First, its retrospective design, based on data from a single, albeit large, medical center in the United States (MIMIC-IV), inherently carries risks of selection bias and may limit the external generalizability of our findings to other healthcare systems or patient populations with different demographic or clinical profiles. Second, our cohort was heterogeneous, including patients from general, trauma, and neurosurgical ICUs. We did not stratify our analysis by the specific type or urgency (e.g., elective vs. emergency) of the surgical procedure, which could represent a source of unmeasured confounding, as the underlying pathophysiology and risk profiles can vary substantially. Third, while we adjusted for numerous potential confounders using multivariable regression, the risk of residual confounding from unmeasured variables, such as specific perioperative fluid management strategies or the use of certain medications, cannot be completely eliminated. Conclusion In this large retrospective cohort study, we have demonstrated that the aEASIX is a robust and independent predictor of 28-day ICU and 28-day in-hospital mortality among a diverse population of critically ill surgical patients. The strength of this association is underscored by a clear dose-response relationship and its consistency across various patient subgroups. Notably, aEASIX exhibited superior predictive discrimination compared to the widely used SOFA score. While these findings are promising, future prospective, multi-center studies are warranted to validate its utility across different healthcare settings and surgical populations, and to explore its potential role in guiding therapeutic strategies. Abbreviations aEASIX: Age-Adjusted Endothelial Activation and Stress Index LnaEASIX: natural log-transformed Age-Adjusted Endothelial Activation and Stress Index AKI: acute kidney injury ALT: alanine aminotransferase AST: aspartate aminotransferase BUN: blood urea nitrogen DBP: diastolic blood pressure EASIX: Endothelial Activation and Stress Index ICU: intensive care unit INR: international normalized ratio LDH: lactate dehydrogenase MIMIC-IV: Medical Information Mart for Intensive Care IV MIT: Massachusetts Institute of Technology Neuro SICU: Neurosurgical ICU PLT: platelet PT: prothrombin time PTT: partial thromboplastin time RDW: red cell distribution width SBP: systolic blood pressure Scr: serum creatinine SICU: surgical intensive care unit SOFA: Sequential Organ Failure Assessment SpO₂: pulse oxygen saturation TSICU: Trauma Surgical ICU WBC: white blood cell Declarations Availability of data and materials The data were available on the MIMIC-IV website at https://mimic.physionet.org/. Ethics approval and consent to participate MIMIC-IV is an anonymized public database. The Institutional Review Board at BIDMC approved the use of data for research purposes without informed consent and authorized principal investigator (Z.Z.) to access the database (certificate number: 47608458). Competing interests The authors declare that they have no competing interests in this section. Author Contributions Conceptualization, Z.Z. and Q.L.; Data analysis, Z.Z.; Writing – Original Draft, Z.Z.; Writing – Review & Editing, Z.Z. and Q.L. All authors have read and agreed to the published version of the manuscript. Acknowledgment We acknowledge the contributions of all staff who participated in the construction and maintenance of the MIMIC-IV database. Clinical trial not applicable. Consent to Publish declaration not applicable. Funding This study was funded by Research project of Zigong City Science & Technology and Intellectual Property Right Bureau (2023-YGY-3-04). 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ANZ journal of surgery 2006;76(11):1010-6. doi: 10.1111/j.1445-2197.2006.03921.x [published Online First: 2006/10/24] Lord JM, Midwinter MJ, Chen YF, et al. The systemic immune response to trauma: an overview of pathophysiology and treatment. Lancet (London, England) 2014;384(9952):1455-65. doi: 10.1016/s0140-6736(14)60687-5 [published Online First: 2014/11/13] Eltzschig HK, Eckle T. Ischemia and reperfusion--from mechanism to translation. Nature medicine 2011;17(11):1391-401. doi: 10.1038/nm.2507 [published Online First: 2011/11/09] Hildebrand F, Pape HC, Horst K, et al. Impact of age on the clinical outcomes of major trauma. European journal of trauma and emergency surgery : official publication of the European Trauma Society 2016;42(3):317-32. doi: 10.1007/s00068-015-0557-1 [published Online First: 2015/08/09] Laaksonen M, Björkman J, Iirola T, et al. The effect of time of measurement on the discriminant ability for mortality in trauma of a pre-hospital shock index multiplied by age and divided by the Glasgow Coma Score: a registry study. BMC emergency medicine 2022;22(1):189. doi: 10.1186/s12873-022-00749-8 [published Online First: 2022/11/30] Tables Table 1 Baseline characteristics of the study cohort stratified by Ln aEASIX quartiles. Overall （ N=4394 ） Q1 (N=1099) Q2 (N=1098) Q3 (N=1098) Q4 (N=1099) P Baseline characteristics Gender Male 2528(57.5%) 543(49.4%) 613(55.8%) 672(61.2%) 700(63.7%) <0.001 Female 1866(42.5%) 556(50.6%) 485(44.2%) 426(38.8%) 399(36.3%) Age at admission (years) 64.0(52.0,76.0) 54.0(40.0,66.0) 66.0(54.0,77.0) 69.0(57.0,81.0) 66.0(55.0,77.0) <0.001 Race White 2654(60.4%) 678(61.7%) 663(60.4%) 676(61.6%) 637(58.0%) 0.248 Other 1740(39.6%) 421(38.3%) 435(39.6%) 422(38.4%) 462(42.0%) Weight (kg) 78.6(66.0,93.5) 75.5(63.1,90.0) 76.9(65.9,91.0) 79.2(67.2,93.7) 82.2(69.5,97.5) <0.001 Comorbidities Hypertension No 2558(58.2%) 672(61.1%) 547(49.8%) 601(54.7%) 738(67.2%) <0.001 Yes 1836(41.8%) 427(38.9%) 551(50.2%) 497(45.3%) 361(32.8%) Diabetes No 3304(75.2%) 927(84.3%) 860(78.3%) 768(69.9%) 749(68.2%) <0.001 Yes 1090(24.8%) 172(15.7%) 238(21.7%) 330(30.1%) 350(31.8%) Complications AKI No 888(20.2%) 355(32.3%) 258(23.5%) 170(15.5%) 105(9.6%) <0.001 Yes 3506(79.8%) 744(67.7%) 840(76.5%) 928(84.5%) 994(90.4%) Vital signs Heart rate (bpm) 90.0(77.0,105.0) 90.0(76.0,107.0) 89.0(75.0,103.0) 90.0(78.0,105.0) 92.0(78.0,106.0) 0.005 SBP (mmHg) 124.0(106.0,142.0) 123.0(108.0,139.0) 125.0(108.0,142.0) 125.0(106.0,146.0) 120.0(104.0,139.5) <0.001 DBP (mmHg) 69.0(58.0,83.0) 71.0(60.5,84.0) 70.0(60.0,83.0) 68.0(58.0,83.0) 68.0(56.0,81.0) <0.001 Respiratory rate (bpm) 19.0(15.0,22.0) 18.0(15.0,22.0) 18.0(15.0,22.0) 19.0(15.0,23.0) 20.0(16.0,23.0) <0.001 Temperature (℃) 36.8(36.5,37.2) 36.9(36.6,37.2) 36.9(36.5,37.2) 36.8(36.5,37.2) 36.8(36.5,37.2) <0.001 SpO₂ (%) 98.0(96.0,100.0) 98.0(96.0,100.0) 98.0(96.0,100.0) 98.0(96.0,100.0) 98.0(95.0,100.0) 0.208 Laboratory tests Scr(mg/dL) 0.9(0.7,1.4) 0.7(0.6,0.8) 0.8(0.7,1.1) 1.1(0.8,1.4) 1.7(1.2,3.2) <0.001 LDH (IU/L) 266.0(200.0,382.0) 199.0(163.0,252.0) 248.0(195.0,311.0) 283.0(223.0,383.0) 422.0(283.0,800.0) <0.001 PLT (K/µL) 189.0(132.0,263.0) 272.0(212.5,343.5) 206.0(162.0,264.0) 164.0(124.0,216.0) 122.0(77.0,176.0) <0.001 Ln aEASIX 4.5 (3.8,5.4) 3.4 (2.9,3.6) 4.2 (4.0,4.4) 4.9 (4.7,5.1) 6.1 (5.7,6.8) <0.001 WBC (K/µL) 11.2(7.7,15.7) 11.8(8.4,16.4) 11.1(8.0,14.9) 11.1(7.4,15.4) 10.9(7.0,15.8) 0.005 Hematocrit (%) 33.0(28.2,37.7) 33.7(29.3,38.2) 34.3(29.8,38.7) 32.8(28.2,37.5) 30.6(26.2,35.8) <0.001 Hemoglobin (g/L) 10.9(9.3,12.5) 11.2(9.6,12.8) 11.4(9.8,12.9) 10.8(9.4,12.4) 10.1(8.6,11.8) <0.001 Red blood count (m/uL) 3.6(3.1,4.2) 3.8(3.3,4.3) 3.8(3.3,4.3) 3.6(3.0,4.1) 3.3(2.8,3.9) <0.001 RDW (%) 14.5(13.4,16.1) 13.9(13.1,15.3) 14.2(13.3,15.5) 14.6(13.6,16.0) 15.3(14.1,17.4) <0.001 BUN (mg/dL) 18.0(12.0,29.0) 12.0(9.0,16.0) 16.0(12.0,21.0) 21.0(15.0,31.0) 32.0(20.0,51.0) <0.001 Serum calcium (mg/dL) 8.3(7.7,8.8) 8.4(7.8,8.9) 8.3(7.8,8.8) 8.3(7.7,8.8) 8.3(7.6,8.9) 0.026 Serum chloride (mg/dL) 104.0(101.0,108.0) 104.0(101.0,107.0) 104.0(101.0,108.0) 105.0(101.0,108.0) 104.0(99.0,108.0) <0.001 Serum sodium (mg/dL) 139.0(136.0,141.0) 138.0(135.0,141.0) 139.0(136.0,141.0) 139.0(136.0,142.0) 138.0(135.0,141.0) 0.006 Serum potassium (mg/dL) 4.1(3.7,4.6) 3.9(3.6,4.3) 4.0(3.6,4.4) 4.2(3.7,4.6) 4.4(3.9,5.0) <0.001 Serum glucose (mg/dL) 133.0(107.0,172.0) 123.0(102.0,152.0) 132.0(108.0,164.0) 140.0(114.0,188.0) 138.0(108.0,195.0) <0.001 PT (sec) 14.2(12.5,16.8) 13.4(12.1,15.5) 13.6(12.3,15.9) 14.4(12.6,16.7) 16.5(13.5,20.9) <0.001 PTT (sec) 30.5(26.8,36.0) 29.3(26.4,34.1) 29.4(26.1,34.8) 30.6(26.7,36.0) 34.0(29.1,41.9) <0.001 INR 1.3(1.1,1.5) 1.2(1.1,1.4) 1.2(1.1,1.4) 1.3(1.1,1.5) 1.5(1.2,1.9) <0.001 Total bilirubin (mg/dL) 0.7(0.4,1.6) 0.5(0.3,0.9) 0.6(0.4,1.1) 0.8(0.5,1.7) 1.2(0.6,3.2) <0.001 ALT (IU/L) 30.0(17.0,78.0) 23.0(15.0,46.0) 27.0(16.0,59.0) 31.0(17.0,72.0) 54.0(22.0,311.0) <0.001 AST (IU/L) 41.0(24.0,104.0) 27.0(19.0,49.0) 35.0(22.0,68.0) 45.0(27.0,107.0) 89.0(38.0,497.5) <0.001 Albumin (g/dL) 3.0(2.6-3.5) 3.1(2.6-3.7) 3.1(2.7-3.6) 3.0(2.6-3.5) 2.9(2.5-3.4) <0.001 SOFA score 4.0(2.0,7.0) 2.0(1.0,4.0) 3.0(2.0,5.0) 5.0(3.0,7.0) 8.0(5.0,11.0) <0.001 length of ICU 3.7(2.0-8.1) 3.1(1.9-6.9) 3.7(1.9-8.0) 3.8(2.1-8.2) 4.5(2.2-10.0) <0.001 length of hospitalization 13.7(7.5-24.3) 12.6(6.9-23.4) 13.1(7.4-22.7) 13.5(7.2-23.9) 15.9(8.3-26.9) <0.001 28-day ICU mortality No 3603 (82.00) 1002 (91.17) 946 (86.16) 876 (79.78) 779 (70.88) <0.001 Yes 791 (18.00) 97 (8.83) 152 (13.84) 222 (20.22) 320 (29.12) 28-day in-hospital mortality No 3643 (82.91) 1008 (91.72) 950 (86.52) 883 (80.42) 802 (72.98) <0.001 Yes 751 (17.09) 91 (8.28) 148 (13.48) 215 (19.58) 297 (27.02) Notes: Data were presented as median (IQR), or n (%). Abbreviations: IQR, interquartile range; Scr, serum creatinine; LDH, lactate dehydrogenase; PLT, platelet count; WBC, white blood cell; RDW, red cell distribution width; BUN, blood urea nitrogen; PT, prothrombin time; PTT, partial thromboplastin time; INR, international normalized ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; AKI, acute kidney injury; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO₂, pulse oxygen saturation; SOFA, Sequential Organ Failure Assessment; ICU, intensive care unit; aEASIX, Age-Adjusted Endothelial Activation and Stress Index. Table 2 Multivariable logistic regression analysis of the association between Ln aEASIX and mortality. Exposure Non-adjusted Model Ⅰ Model Ⅱ OR (95% CI) P OR (95% CI) P OR (95% CI) P 28-day ICU mortality Ln aEASIX 1.481(1.397,1.571) ＜ 0.001 1.511(1.424,1.603) ＜ 0.001 1.348(1.259,1.445) ＜ 0.001 Q1 Ref Ref Ref Q2 1.66(1.27,2.179) ＜ 0.001 1.682(1.286,2.21) ＜ 0.001 1.573(1.197,2.076) 0.001 Q3 2.618(2.034,3.391) ＜ 0.001 2.709(2.103,3.512) ＜ 0.001 2.222(1.709,2.906) ＜ 0.001 Q4 4.243(3.331,5.448) 4.506(3.53,5.797) 2.927(2.222,3.877) P for trend ＜ 0.001 ＜ 0.001 ＜ 0.001 28-day in-hospital mortality Ln aEASIX 1.462(1.378,1.551) ＜ 0.001 1.49(1.404,1.583) ＜ 0.001 1.367(1.275,1.465) ＜ 0.001 Q1 Q2 1.726(1.313,2.28) ＜ 0.001 1.749(1.33,2.312) ＜ 0.001 1.652(1.251,2.192) ＜ 0.001 Q3 2.697(2.084,3.516) ＜ 0.001 2.79(2.154,3.64) ＜ 0.001 2.368(1.812,3.115) ＜ 0.001 Q4 4.102(3.2,5.304) ＜ 0.001 4.35(3.386,5.637) ＜ 0.001 3.083(2.327,4.111) ＜ 0.001 P for trend ＜ 0.001 ＜ 0.001 ＜ 0.001 Notes: Non-adjusted models: None; Model Ⅰ adjusted for: weight; Model Ⅱ adjusted for: confounders in the minimally adjusted (Model Ⅰ) + WBC, RDW, PTT, BUN, Respiratory rate, and AKI (yes/no). Abbreviations: Q, Quartile; OR, odds ratios; CI, confidence intervals; WBC, white blood cell; RDW, red cell distribution width; BUN, blood urea nitrogen; PTT, partial thromboplastin time; AKI, acute kidney injury; aEASIX, Age-Adjusted Endothelial Activation and Stress Index. Table 3 Threshold effect analysis of the impact of Ln aEASIX on mortality using a two-piecewise linear regression model. 28-day ICU mortality OR (95% CI) P 28-day in-hospital mortality OR (95% CI) P Inflection point 5.19 Inflection point 5.14 <5.19 1.63 (1.37,1.93) < 0 .001 <5.14 1.68 (1.40,2.01) <0.001 ≥5.19 1.19 (1.04,1.35) 0.010 ≥5.14 1.16 (1.03,1.32) 0.018 P for likelihood test 0.031 P for likelihood test 0.007 Abbreviations: OR, odds ratios; CI, confidence intervals; ICU, intensive care unit; aEASIX, Age-Adjusted Endothelial Activation and Stress Index. Additional Declarations No competing interests reported. Supplementary Files Additionalfile.docx Table S1 Missing Number (%) for Included Variables in Dataset Table S2 Univariate logistic regression analysis Table S3 The variance inflation factor test Cite Share Download PDF Status: Published Journal Publication published 28 Mar, 2026 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 02 Jan, 2026 Reviews received at journal 29 Dec, 2025 Reviewers agreed at journal 27 Dec, 2025 Reviewers agreed at journal 24 Dec, 2025 Reviewers agreed at journal 23 Dec, 2025 Reviewers agreed at journal 22 Dec, 2025 Reviews received at journal 06 Nov, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviews received at journal 16 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers invited by journal 15 Sep, 2025 Editor assigned by journal 22 Aug, 2025 Submission checks completed at journal 21 Aug, 2025 First submitted to journal 13 Aug, 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. 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13:07:32","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144044,"visible":true,"origin":"","legend":"","description":"","filename":"092cbc689ba743a1b387fe2bcdeabb201structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7364770/v1/8fc58db727177d803099dd9c.xml"},{"id":92085957,"identity":"cf9507a9-48b4-4a58-9463-38db2c6aab37","added_by":"auto","created_at":"2025-09-24 12:59:32","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154808,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7364770/v1/d845a53d01c9a17512f9c5d6.html"},{"id":92085937,"identity":"a1e62d02-825b-4587-812f-4591292998b1","added_by":"auto","created_at":"2025-09-24 12:59:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":20560,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the research patient selection process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: ICU, intensive care unit; MIMIC, medical information mart for intensive care; Q, quartile.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7364770/v1/e48d33fc59980eb6807117b4.png"},{"id":92085936,"identity":"393bde57-7730-407b-8b39-4baaad77415e","added_by":"auto","created_at":"2025-09-24 12:59:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16069,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationship between LnaEASIX and mortality. (A) Restricted cubic spline for 28-day ICU mortality. (B) Restricted cubic spline for 28-day in-hospital mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: ICU, intensive care unit; LnaEASIX: natural log-transformed Age-Adjusted Endothelial Activation and Stress Index.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7364770/v1/fc13c91b61d87e034d0fdd68.png"},{"id":92085938,"identity":"5f4015b3-716d-4607-87a1-67a9b75100e1","added_by":"auto","created_at":"2025-09-24 12:59:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22576,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between LnaEASIX and mortality. (A) Forest plot for 28-day ICU mortality. (B) Forest plot for 28-day in-hospital mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eOR: odds ratio; CI: confidence interval; ICU: intensive care unit; AKI, acute kidney injury; LnaEASIX: natural log-transformed Age-Adjusted Endothelial Activation and Stress Index.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7364770/v1/b985d4a26d53055fd8316353.png"},{"id":92087203,"identity":"39c917ab-cf3f-4a9d-b09a-c4535957ae38","added_by":"auto","created_at":"2025-09-24 13:07:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24649,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for patients stratified by LnaEASIX quartiles. (A) 28-day ICU survival probability. (B) 28-day in-hospital survival probability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations: \u003c/strong\u003eQ, Quartile; LnaEASIX: natural log-transformed Age-Adjusted Endothelial Activation and Stress Index.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7364770/v1/e1d6e42de98d337e6358f86e.png"},{"id":92085943,"identity":"e1c0a0cf-5338-4c8f-81cf-0a99a7519aa8","added_by":"auto","created_at":"2025-09-24 12:59:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":49630,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for LnaEASIX and SOFA score in predicting mortality. (A) ROC curves for 28-day ICU mortality. (B) ROC curves for 28-day in-hospital mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e ICU: intensive care unit; Scr, serum creatinine; LDH, lactate dehydrogenase; PLT, platelet count; SOFA, Sequential Organ Failure Assessment; LnaEASIX: natural log-transformed Age-Adjusted Endothelial Activation and Stress Index.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7364770/v1/4873f080fb4620187ca86a4c.png"},{"id":92087204,"identity":"78565853-87fa-46a5-9560-eb8ca3557350","added_by":"auto","created_at":"2025-09-24 13:07:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":17077,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis diagram illustrating the proportion of the effect of LnaEASIX on mortality mediated by the SOFA score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e * indicates \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e ICU: intensive care unit; SOFA, Sequential Organ Failure Assessment; LnaEASIX: natural log-transformed Age-Adjusted Endothelial Activation and Stress Index.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7364770/v1/f3aac186be43d898d9093ba4.png"},{"id":105755891,"identity":"c8262a45-dc42-4bb7-ae9b-137099f2d9f0","added_by":"auto","created_at":"2026-03-30 16:32:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1438195,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7364770/v1/bc56d993-c045-4e36-b46b-bcb83e1a5800.pdf"},{"id":92088682,"identity":"0e95fe28-e020-4b23-aebc-d116159c4dd9","added_by":"auto","created_at":"2025-09-24 13:15:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e Missing Number (%) for Included Variables in Dataset\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2 \u003c/strong\u003eUnivariate logistic regression analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3\u003c/strong\u003e The variance inflation factor test\u003c/p\u003e","description":"","filename":"Additionalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7364770/v1/4c0a288ede1224015843983d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the prognostic value of Age-adjusted EASIX for predicting mortality in critically ill surgical patients: a retrospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith over 310\u0026nbsp;million major surgical procedures performed annually worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, a growing subset of these patients requires intensive care, imposing a substantial burden on healthcare systems. The number of critically ill surgical patients is steadily increasing, now accounting for up to one-third of all intensive care unit (ICU) admissions in some reports\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Despite advances in perioperative medicine, this population continues to experience high mortality rates, which vary starkly from under 25% in high-income nations to as high as 50% in resource-limited settings\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This clinical challenge is compounded by a significant economic impact, with critical care consuming up to 1% of the gross domestic product in some developed countries\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Given the scale of this population, its high mortality, and immense cost, the need for early and accurate risk stratification is paramount to improving outcomes and optimizing resource allocation.\u003c/p\u003e\u003cp\u003eIn this high-stakes context, early and accurate risk stratification upon admission to the surgical intensive care unit (SICU) is paramount for optimizing treatment and allocating resources. While established scoring system like the Sequential Organ Failure Assessment (SOFA) is widely used,\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e they can be complex to calculate and may not fully capture the specific pathophysiological mechanisms driving adverse outcomes.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe Endothelial Activation and Stress Index (EASIX), a simple score derived from lactate dehydrogenase (LDH), platelet (PLT) count, and serum creatinine (Scr), was specifically developed to quantify endothelial damage. \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003eHowever, the original EASIX score omits age, a powerful and independent predictor of mortality in nearly all critically ill populations.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Consequently, the Age-Adjusted EASIX (aEASIX) was developed to incorporate this crucial variable. The prognostic utility of aEASIX has been validated in other critically ill cohorts, such as patients with severe COVID-19, where it demonstrated superior predictive performance for ICU mortality compared to the SOFA score.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Despite this promising evidence, the prognostic value of aEASIX in a broad, unselected population of critically ill surgical patients remains largely uninvestigated. Therefore, this study aimed to investigate the association between the aEASIX and short-term mortality in a large cohort of critically ill surgical patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eData source\u0026nbsp;\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study is a retrospective cohort study based on data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v 3.1) database. MIMIC-IV is a large, publicly available database containing de-identified electronic health records for patients admitted to ICU at the Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, between 2008 and 2022. The establishment of the database was approved by the institutional review boards of both the Massachusetts Institute of Technology (MIT) and BIDMC. One of the authors (Z.Z., to be filled in) was granted access to the database after completing the required training (Certificate number: 47608458). As the database is de-identified, the requirement for individual patient consent was waived.\u003c/p\u003e\n\u003ch2\u003eParticipant selection\u0026nbsp;\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe initially identified 21,390 patients admitted to the Surgical ICU (SICU), Trauma Surgical ICU (TSICU), or Neurosurgical ICU (Neuro SICU) within the MIMIC-IV database. The inclusion criterion was adult patients (aged \u0026ge; 18 years) with a first-time admission to one of the specified surgical ICUs. The exclusion criteria were as follows: (1) patients who were not on their first ICU admission during their first hospitalization; (2) patients with missing key data on the first day of ICU admission required for the calculation of the aEASIX (age, Scr, LDH, or PLT).\u003c/p\u003e\n\u003ch2\u003eClinical outcomes \u0026nbsp;\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe primary outcome of this study was 28-day ICU mortality. The secondary outcome was 28-day in-hospital mortality.\u003c/p\u003e\n\u003ch2\u003eData extraction\u003c/h2\u003e\n\u003cp\u003eData extractions were performed using the PostgreSQL software. We collected the following data from the first 24 hours of ICU admission: \u0026nbsp;Baseline characteristics: Age, gender, race, and weight. Comorbidities and Complications: History of hypertension, diabetes, and the presence of acute kidney injury (AKI). Vital Signs: Heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP), respiratory rate, temperature, and pulse oxygen saturation (SpO₂). Laboratory Tests: laboratory parameters including hematological tests [white blood cell (WBC) count, platelet (PLT) count, hemoglobin, hematocrit, red blood cell count, and red cell distribution width (RDW)], renal and metabolic panels [blood urea nitrogen (BUN), serum creatinine (Scr), serum glucose, sodium, potassium, chloride, and calcium], coagulation profiles [prothrombin time (PT), partial thromboplastin time (PTT), and international normalized ratio (INR)], and liver function markers [lactate dehydrogenase (LDH), total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and albumin], and Sequential Organ Failure Assessment (SOFA) score. The primary exposure variable, the Age-Adjusted Endothelial Activation and Stress Index (aEASIX), was calculated using the formula: aEASIX = age (years) *[Scr (mg/dL) * LDH (IU/L) / PLT (10⁹/L)].\u003csup\u003e19\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eContinuous variables were presented as median (interquartile range, IQR) due to their skewed distribution, while categorical variables were expressed as numbers (percentages). \u0026nbsp;Because the aEASIX distribution was markedly skewed, we applied a natural-log transformation (LnaEASIX) for all regression analyses. Patients were categorized into four groups based on the quartiles of their LnaEASIX. Differences in baseline characteristics across these quartiles were compared using the Kruskal-Wallis test for continuous variables and the chi-square test for categorical variables. To build the multivariable models, we first used univariate binary logistic regression to screen for potential confounders associated with mortality. Variables with a P-value \u0026lt; 0.1 were considered for inclusion.\u003csup\u003e20\u003c/sup\u003e To avoid multicollinearity, we calculated the variance inflation factor (VIF) for all selected variables and excluded those with a VIF \u0026gt; 5.\u003csup\u003e21\u003c/sup\u003e Multivariable logistic regression analysis was performed to assess the association between LnaEASIX (as both a continuous variable and a categorical variable by quartiles) and the mortality outcomes. We constructed three models: Non-adjusted model: No covariates were adjusted. Model\u0026nbsp;Ⅰ\u0026nbsp;(Minimally-adjusted): Adjusted for weight.\u0026nbsp;Model\u0026nbsp;Ⅱ\u0026nbsp;(Fully-adjusted): Adjusted for covariates included in Model I plus WBC, RDW, PTT, BUN, respiratory rate, and AKI.\u0026nbsp;Results were presented as odds ratios (OR) with 95% confidence intervals (CI).\u0026nbsp;To explore the potential non-linear relationship between LnaEASIX and mortality, we employed restricted cubic splines (RCS) with three knots. If a non-linear association was detected, a two-piecewise linear regression model was used to calculate the threshold effect and identify the inflection point.\u0026nbsp;The predictive performance of\u0026nbsp;aEASIX for mortality was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with the SOFA score. Kaplan-Meier survival curves were plotted to visualize survival differences among the LnaEASIX quartiles, and the log-rank test was used for comparison.\u0026nbsp;Subgroup analyses were conducted across various strata, including age, gender, hypertension, diabetes, and AKI, to assess the robustness of our findings. Interaction tests were performed to examine whether the effect of LnaEASIX on mortality differed across these subgroups. Finally, a mediation analysis was conducted to explore whether the SOFA score mediated the relationship between LnaEASIX and mortality.\u0026nbsp;Variables with missing data were assessed, and as the proportion of missingness was low for all included variables (all \u0026lt; 5%), multiple imputation was performed. All statistical analyses were conducted using R software (version 4.4.1). P-value \u0026lt; 0.05 was considered statistically significant.\u003csup\u003e22\u003c/sup\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eBaseline characteristics\u003c/h2\u003e\n\u003cp\u003eFollowing the screening process detailed in the patient selection flowchart (\u003cstrong\u003eFigure 1\u003c/strong\u003e), a total of 4,394 patients were included in the final analysis.\u0026nbsp;The median age of the cohort was 64.0 years (IQR 52.0-76.0), with 57.5% being male. The overall 28-day ICU mortality and 28-day in-hospital mortality rates were 18.0% and 17.09%, respectively. Patients were stratified into four groups based on quartiles of their LnaEASIX. As detailed in Table 1, patients in the higher LnaEASIX quartiles exhibited a greater burden of critical illness. Specifically, with increasing LnaEASIX quartiles, there was a significant trend towards higher SOFA scores (median: 2.0 in Q1 vs. 8.0 in Q4), elevated levels of Scr, LDH, BUN, and PTT. Conversely, patients in the highest quartile (Q4) had significantly lower PLT (median: 272 K/\u0026micro;L in Q1 vs. 122 K/\u0026micro;L in Q4) and hemoglobin levels. Crucially, both 28-day ICU mortality (8.8% in Q1 to 29.1% in Q4) and 28-day in-hospital mortality (8.3% in Q1 to 27.0% in Q4) increased progressively and significantly across the quartiles (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;Association between LnaEASIX and mortality\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAfter multivariable adjustment for weight, WBC, RDW, PTT, BUN, respiratory rate, and AKI (Model II, Table 2), LnaEASIX remained an independent predictor of both 28-day ICU mortality (OR 1.348, 95% CI 1.259-1.445,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.001) and 28-day in-hospital mortality (OR 1.367, 95% CI 1.275-1.465, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eWhen analyzed by quartiles, a clear graded relationship was observed. Compared to the lowest quartile (Q1), patients in the highest quartile (Q4) had a significantly increased risk of 28-day ICU mortality (Model II: OR 2.927, 95% CI 2.222-3.877,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.001) and 28-day in-hospital mortality (Model II: OR 3.083, 95% CI 2.327-4.111,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.001) (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.001).\u003c/p\u003e\n\u003ch2\u003eDose-Response Relationship and Threshold Effect\u003c/h2\u003e\n\u003cp\u003eThe RCS plots revealed a non-linear, monotonically increasing association for both 28-day ICU mortality (\u003cem\u003eP\u003c/em\u003e for non-linearity = 0.014) and 28-day in-hospital mortality (\u003cem\u003eP\u003c/em\u003e for non-linearity = 0.002) (Figure 2A and 2B). The risk of mortality increased steeply at lower levels of LnaEASIX and then began to plateau at higher levels.\u003c/p\u003e\n\u003cp\u003eA two-piecewise linear regression model was fitted to identify the inflection points (Table 3). For 28-day ICU mortality, an inflection point was identified at an LnaEASIX value of 5.19. Below this threshold, each unit increase in LnaEASIX was associated with a sharp 63% rise in mortality risk (OR 1.63, 95% CI 1.37-1.93,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.001). Above this point, the association remained significant but was attenuated (OR 1.19, 95% CI 1.04-1.35,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e = 0.010). A similar pattern was observed for 28-day in-hospital mortality, with an inflection point at 5.14 (\u003cem\u003eP\u003c/em\u003e for likelihood test \u0026lt; 0.05).\u003c/p\u003e\n\u003ch2\u003eSubgroup and Survival Analysis\u003c/h2\u003e\n\u003cp\u003eTo confirm the robustness of our findings, we performed subgroup analyses stratified by age, gender, hypertension, diabetes, and AKI. As shown in the forest plots (Figure 3A and 3B), the positive association between higher LnaEASIX and increased mortality was consistent across all examined subgroups for both 28-day ICU and in-hospital mortality. No significant interactions were detected (all \u003cem\u003eP\u003c/em\u003e for interaction \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eKaplan-Meier survival analysis demonstrated a clear separation in survival probabilities among the \u0026nbsp;LnaEASIX quartiles (Figure 4A and 4B). Patients in the highest quartile (Q4) had a significantly lower 28-day survival probability compared to those in the lower quartiles for both ICU and in-hospital survival, with the lowest quartile (Q1) exhibiting the best survival outcome (Log-rank test, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003ch2\u003ePredictive Performance\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe predictive utility of the composite LnaEASIX surpassed that of its individual components (age, LDH, Scr, and PLT). Furthermore, when benchmarked against the established SOFA score, LnaEASIX demonstrated significantly better discriminative ability for predicting both 28-day ICU mortality (AUC: 0.653 vs. 0.625) and 28-day in-hospital mortality (AUC: 0.649 vs. 0.614) (Figure 5A and 5B).\u003c/p\u003e\n\u003ch2\u003eMediation Analysis\u003c/h2\u003e\n\u003cp\u003eFor 28-day ICU mortality, the SOFA score was found to mediate\u0026nbsp;17.581%\u0026nbsp;of the total effect of LnaEASIX on mortality. Similarly, it mediated\u0026nbsp;14.731%\u0026nbsp;of the total effect for 28-day in-hospital mortality (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), suggesting that a notable portion of the prognostic impact of LnaEASIX is channeled through the development or severity of organ dysfunction as measured by SOFA (Figure 6).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this study is the first to demonstrate that the Age-Adjusted Endothelial Activation and Stress Index (aEASIX) is an independent predictor of short-term mortality in a broad population of critically ill surgical patients. After multivariable adjustment, we established that a higher aEASIX upon SICU admission is significantly linked to an increased risk of both 28-day ICU and in-hospital mortality. This association was underscored by a clear dose-response relationship and was robust across various subgroups. As a corollary, a higher aEASIX corresponded to a significantly lower survival probability. Of note, the predictive performance of aEASIX for mortality appeared to exceed that of the conventional SOFA score.\u003c/p\u003e\u003cp\u003eOur findings are consistent with and significantly extend the results of previous research that validated aEASIX in more specific patient populations. Perez-Garcia et al. reported that after multivariable adjustment, a higher log2-aEASIX was associated with a significantly increased risk of 28-day mortality (Hazard Ratio [HR]\u0026thinsp;=\u0026thinsp;1.61, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and demonstrated excellent discrimination with an AUC of 0.827. \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Similarly, Jeong et al. found that log2-aEASIX was significantly associated with ICU mortality in a multivariable model (OR\u0026thinsp;=\u0026thinsp;1.56, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and showed superior predictive performance compared to the SOFA score (AUC 0.730 vs. 0.660, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0016).\u003csup\u003e18\u003c/sup\u003e While these studies established the principle that a marker of endothelial injury is prognostically important in the context of a specific viral-induced endotheliopathy,\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e the unique contribution of our study lies in its application to a large, heterogeneous SICU cohort. This broadens the generalizability of aEASIX, suggesting its utility is not confined to specific viral or immunological conditions but reflects a fundamental pathophysiological process common to many forms of surgical critical illness.\u003c/p\u003e\u003cp\u003eThe prognostic power of aEASIX lies in its dual function: acting as a surrogate marker for systemic endothelial injury and dysfunction,\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e while simultaneously accounting for age-related vulnerability. This endothelial insult is a central pathological process in critically ill surgical patients, often triggered by insults like extensive tissue damage, ischemia-reperfusion, and systemic inflammation. \u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Physiological reserves differ across age groups, making age a critical independent determinant of outcomes in trauma;\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e therefore, by integrating age, aEASIX provides a more accurate, individualized risk assessment. Our mediation analysis, which found that the SOFA score partially mediates the effect of aEASIX on mortality, supports this biological rationale. This leads to our proposed causal pathway: aEASIX captures the initial, age-modulated endothelial damage, which precedes and directly contributes to the multi-organ failure syndrome, and these factors jointly promote the death of critically ill surgical patients.\u003c/p\u003e\u003cp\u003eThe primary advantage of aEASIX is its simplicity and immediate availability.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e It is calculated from four parameters (age, LDH, Scr, PLT) that are routinely measured upon ICU admission, making it an easy-to-implement, cost-effective tool for rapid, bedside risk assessment. The subgroup analyses revealed a consistent association between aEASIX and mortality across different strata of age, gender, and comorbidities, underscoring its robustness. The identification of a clear dose-response relationship and a specific inflection point provides clinicians with a tangible threshold for identifying patients who may warrant more aggressive monitoring, earlier intervention, or consideration for advanced therapies.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e This could be particularly valuable for optimizing resource allocation and facilitating more informed and timely prognostic discussions with patients and their families.\u003c/p\u003e\u003cp\u003eDespite its strengths, our study has several limitations that must be acknowledged. First, its retrospective design, based on data from a single, albeit large, medical center in the United States (MIMIC-IV), inherently carries risks of selection bias and may limit the external generalizability of our findings to other healthcare systems or patient populations with different demographic or clinical profiles. Second, our cohort was heterogeneous, including patients from general, trauma, and neurosurgical ICUs. We did not stratify our analysis by the specific type or urgency (e.g., elective vs. emergency) of the surgical procedure, which could represent a source of unmeasured confounding, as the underlying pathophysiology and risk profiles can vary substantially. Third, while we adjusted for numerous potential confounders using multivariable regression, the risk of residual confounding from unmeasured variables, such as specific perioperative fluid management strategies or the use of certain medications, cannot be completely eliminated.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this large retrospective cohort study, we have demonstrated that the aEASIX is a robust and independent predictor of 28-day ICU and 28-day in-hospital mortality among a diverse population of critically ill surgical patients. The strength of this association is underscored by a clear dose-response relationship and its consistency across various patient subgroups. Notably, aEASIX exhibited superior predictive discrimination compared to the widely used SOFA score. While these findings are promising, future prospective, multi-center studies are warranted to validate its utility across different healthcare settings and surgical populations, and to explore its potential role in guiding therapeutic strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eaEASIX: Age-Adjusted Endothelial Activation and Stress Index\u003c/p\u003e\n\u003cp\u003eLnaEASIX: natural log-transformed Age-Adjusted Endothelial Activation and Stress Index\u003c/p\u003e\n\u003cp\u003eAKI: acute kidney injury\u003c/p\u003e\n\u003cp\u003eALT: alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eAST: aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eBUN: blood urea nitrogen\u003c/p\u003e\n\u003cp\u003eDBP: diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eEASIX: Endothelial Activation and Stress Index\u003c/p\u003e\n\u003cp\u003eICU: intensive care unit\u003c/p\u003e\n\u003cp\u003eINR: international normalized ratio\u003c/p\u003e\n\u003cp\u003eLDH: lactate dehydrogenase\u003c/p\u003e\n\u003cp\u003eMIMIC-IV: Medical Information Mart for Intensive Care IV\u003c/p\u003e\n\u003cp\u003eMIT: Massachusetts Institute of Technology\u003c/p\u003e\n\u003cp\u003eNeuro SICU: Neurosurgical ICU\u003c/p\u003e\n\u003cp\u003ePLT: platelet\u003c/p\u003e\n\u003cp\u003ePT: prothrombin time\u003c/p\u003e\n\u003cp\u003ePTT: partial thromboplastin time\u003c/p\u003e\n\u003cp\u003eRDW: red cell distribution width\u003c/p\u003e\n\u003cp\u003eSBP: systolic blood pressure\u003c/p\u003e\n\u003cp\u003eScr: serum creatinine\u003c/p\u003e\n\u003cp\u003eSICU: surgical intensive care unit\u003c/p\u003e\n\u003cp\u003eSOFA: Sequential Organ Failure Assessment\u003c/p\u003e\n\u003cp\u003eSpO₂: pulse oxygen saturation\u003c/p\u003e\n\u003cp\u003eTSICU: Trauma Surgical ICU\u003c/p\u003e\n\u003cp\u003eWBC: white blood cell\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe data were available on the MIMIC-IV website at https://mimic.physionet.org/.\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eMIMIC-IV is an anonymized public database.\u0026nbsp;The Institutional Review Board at BIDMC approved the use of data for research purposes without informed consent and authorized principal investigator (Z.Z.) \u0026nbsp;to access the database (certificate number: 47608458).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests in this section.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eConceptualization, Z.Z. and Q.L.; Data analysis, Z.Z.; Writing \u0026ndash; Original Draft, Z.Z.; Writing \u0026ndash; Review \u0026amp; Editing, Z.Z. and Q.L. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgment\u003c/h2\u003e\n\u003cp\u003eWe acknowledge the contributions of all staff who participated in the construction and maintenance of the MIMIC-IV database.\u003c/p\u003e\n\u003ch2\u003eClinical trial\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent to Publish declaration\u003c/h2\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was funded by Research project of Zigong City Science \u0026amp; Technology and Intellectual Property Right Bureau (2023-YGY-3-04).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWeiser TG, Haynes AB, Molina G, et al. 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\u003cp\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eN=4394\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=1099)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=1098)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=1098)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=1099)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"21\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"21\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 259px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2528(57.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e543(49.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e613(55.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e672(61.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e700(63.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1866(42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e556(50.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e485(44.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e426(38.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e399(36.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eAge at admission (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e64.0(52.0,76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e54.0(40.0,66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e66.0(54.0,77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e69.0(57.0,81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e66.0(55.0,77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eWhite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2654(60.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e678(61.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e663(60.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e676(61.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e637(58.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eOther\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1740(39.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e421(38.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e435(39.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e422(38.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e462(42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e78.6(66.0,93.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e75.5(63.1,90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e76.9(65.9,91.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e79.2(67.2,93.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e82.2(69.5,97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2558(58.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e672(61.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e547(49.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e601(54.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e738(67.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1836(41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e427(38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e551(50.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e497(45.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e361(32.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eDiabetes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3304(75.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e927(84.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e860(78.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e768(69.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e749(68.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1090(24.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e172(15.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e238(21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e330(30.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e350(31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eAKI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e888(20.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e355(32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e258(23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e170(15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e105(9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3506(79.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e744(67.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e840(76.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e928(84.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e994(90.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVital signs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHeart rate (bpm)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e90.0(77.0,105.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e90.0(76.0,107.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e89.0(75.0,103.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e90.0(78.0,105.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e92.0(78.0,106.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eSBP (mmHg)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e124.0(106.0,142.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e123.0(108.0,139.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e125.0(108.0,142.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e125.0(106.0,146.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e120.0(104.0,139.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eDBP (mmHg)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e69.0(58.0,83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e71.0(60.5,84.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e70.0(60.0,83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e68.0(58.0,83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e68.0(56.0,81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eRespiratory rate (bpm)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e19.0(15.0,22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e18.0(15.0,22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e18.0(15.0,22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e19.0(15.0,23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e20.0(16.0,23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eTemperature (℃)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e36.8(36.5,37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e36.9(36.6,37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e36.9(36.5,37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e36.8(36.5,37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e36.8(36.5,37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eSpO₂\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e98.0(96.0,100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e98.0(96.0,100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e98.0(96.0,100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e98.0(96.0,100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e98.0(95.0,100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"34\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory tests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eScr(mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.9(0.7,1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.7(0.6,0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.8(0.7,1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1.1(0.8,1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1.7(1.2,3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eLDH (IU/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e266.0(200.0,382.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e199.0(163.0,252.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e248.0(195.0,311.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e283.0(223.0,383.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e422.0(283.0,800.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003ePLT (K/\u0026micro;L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e189.0(132.0,263.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e272.0(212.5,343.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e206.0(162.0,264.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e164.0(124.0,216.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e122.0(77.0,176.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eLn aEASIX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4.5 (3.8,5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e3.4 (2.9,3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4.2 (4.0,4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e4.9 (4.7,5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e6.1 (5.7,6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eWBC (K/\u0026micro;L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e11.2(7.7,15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e11.8(8.4,16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e11.1(8.0,14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e11.1(7.4,15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e10.9(7.0,15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHematocrit (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e33.0(28.2,37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e33.7(29.3,38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e34.3(29.8,38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e32.8(28.2,37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e30.6(26.2,35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eHemoglobin (g/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e10.9(9.3,12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e11.2(9.6,12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e11.4(9.8,12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e10.8(9.4,12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e10.1(8.6,11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eRed blood count (m/uL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.6(3.1,4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.8(3.3,4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.8(3.3,4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3.6(3.0,4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e3.3(2.8,3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eRDW (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e14.5(13.4,16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e13.9(13.1,15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e14.2(13.3,15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e14.6(13.6,16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e15.3(14.1,17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eBUN (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e18.0(12.0,29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e12.0(9.0,16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e16.0(12.0,21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e21.0(15.0,31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e32.0(20.0,51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eSerum calcium (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e8.3(7.7,8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e8.4(7.8,8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e8.3(7.8,8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e8.3(7.7,8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e8.3(7.6,8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eSerum chloride (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e104.0(101.0,108.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e104.0(101.0,107.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e104.0(101.0,108.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e105.0(101.0,108.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e104.0(99.0,108.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eSerum sodium (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e139.0(136.0,141.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e138.0(135.0,141.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e139.0(136.0,141.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e139.0(136.0,142.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e138.0(135.0,141.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eSerum potassium (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4.1(3.7,4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.9(3.6,4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4.0(3.6,4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e4.2(3.7,4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4.4(3.9,5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eSerum glucose (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e133.0(107.0,172.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e123.0(102.0,152.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e132.0(108.0,164.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e140.0(114.0,188.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e138.0(108.0,195.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003ePT (sec)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e14.2(12.5,16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e13.4(12.1,15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e13.6(12.3,15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e14.4(12.6,16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e16.5(13.5,20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003ePTT (sec)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e30.5(26.8,36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e29.3(26.4,34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e29.4(26.1,34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e30.6(26.7,36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e34.0(29.1,41.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1.3(1.1,1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1.2(1.1,1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1.2(1.1,1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e1.3(1.1,1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1.5(1.2,1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eTotal bilirubin (mg/dL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.7(0.4,1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.5(0.3,0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e0.6(0.4,1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.8(0.5,1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e1.2(0.6,3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eALT (IU/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e30.0(17.0,78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e23.0(15.0,46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e27.0(16.0,59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e31.0(17.0,72.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e54.0(22.0,311.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eAST (IU/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e41.0(24.0,104.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e27.0(19.0,49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e35.0(22.0,68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e45.0(27.0,107.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e89.0(38.0,497.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.0(2.6-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.1(2.6-3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.1(2.7-3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3.0(2.6-3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e2.9(2.5-3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOFA\u003c/strong\u003e \u003cstrong\u003escore\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4.0(2.0,7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e2.0(1.0,4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.0(2.0,5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e5.0(3.0,7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e8.0(5.0,11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elength of ICU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.7(2.0-8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.1(1.9-6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3.7(1.9-8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3.8(2.1-8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e4.5(2.2-10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elength of hospitalization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e13.7(7.5-24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e12.6(6.9-23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e13.1(7.4-22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e13.5(7.2-23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e15.9(8.3-26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28-day ICU mortality\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"5\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3603 (82.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1002 (91.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e946 (86.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e876 (79.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e779 (70.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e791 (18.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e97 (8.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e152 (13.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e222 (20.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e320 (29.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 259px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28-day in-hospital mortality\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"24\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e3643 (82.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1008 (91.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e950 (86.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e883 (80.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e802 (72.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e751 (17.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e91 (8.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e148 (13.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e215 (19.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e297 (27.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd height=\"16\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Data were presented as median (IQR), or n (%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e IQR, interquartile range; Scr, serum creatinine; LDH, lactate dehydrogenase; PLT, platelet count; WBC, white blood cell; RDW, red cell distribution width; BUN, blood urea nitrogen; PT, prothrombin time; PTT, partial thromboplastin time; INR, international normalized ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; AKI, acute kidney injury; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO₂, pulse oxygen saturation; SOFA, Sequential Organ Failure Assessment; ICU, intensive care unit; aEASIX, Age-Adjusted Endothelial Activation and Stress Index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Multivariable logistic regression analysis of the association between Ln aEASIX and mortality.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"703\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003eNon-adjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 186px;\"\u003e\n \u003cp\u003eModel\u0026nbsp;Ⅰ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003eModel\u0026nbsp;Ⅱ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 270px;\"\u003e\n \u003cp\u003e28-day ICU mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp; Ln\u0026nbsp;aEASIX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.481(1.397,1.571)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.511(1.424,1.603)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.348(1.259,1.445)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.66(1.27,2.179)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.682(1.286,2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.573(1.197,2.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.618(2.034,3.391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.709(2.103,3.512)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.222(1.709,2.906)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e4.243(3.331,5.448)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e4.506(3.53,5.797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.927(2.222,3.877)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 215px;\"\u003e\n \u003cp\u003e28-day in-hospital mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp; Ln\u0026nbsp;aEASIX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.462(1.378,1.551)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.49(1.404,1.583)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.367(1.275,1.465)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.726(1.313,2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.749(1.33,2.312)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.652(1.251,2.192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.697(2.084,3.516)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.79(2.154,3.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.368(1.812,3.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp; Q4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e4.102(3.2,5.304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e4.35(3.386,5.637)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e3.083(2.327,4.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\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\u003cstrong\u003eNotes:\u003c/strong\u003e Non-adjusted models: None; Model\u0026nbsp;Ⅰ\u0026nbsp;adjusted for: weight; Model\u0026nbsp;Ⅱ\u0026nbsp;adjusted for: confounders in the minimally adjusted (Model\u0026nbsp;Ⅰ) +\u0026nbsp;WBC, RDW, PTT, BUN, Respiratory rate, and AKI (yes/no).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eQ, Quartile; OR, odds ratios; CI, confidence intervals; WBC, white blood cell; RDW, red cell distribution width; BUN, blood urea nitrogen; PTT, partial thromboplastin time; AKI, acute kidney injury; aEASIX, Age-Adjusted Endothelial Activation and Stress Index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Threshold effect analysis of the impact of Ln aEASIX on mortality using a two-piecewise linear regression model.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"675\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e28-day ICU mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e28-day in-hospital mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eInflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e5.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eInflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;5.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.63 (1.37,1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003cstrong\u003e.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026lt;5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.68 (1.40,2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;5.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.19 (1.04,1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026ge;5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.16 (1.03,1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eP for likelihood test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eP for likelihood test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\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\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eOR, odds ratios; CI, confidence intervals; ICU, intensive care unit; aEASIX, Age-Adjusted Endothelial Activation and Stress Index.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Age-Adjusted Endothelial Activation and Stress Index, surgical intensive care unit, mortality risk, MIMIC-IV database","lastPublishedDoi":"10.21203/rs.3.rs-7364770/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7364770/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e Critically ill surgical patients face high mortality rates, necessitating early and accurate risk stratification. The Age-Adjusted Endothelial Activation and Stress Index (aEASIX), a simple biomarker for endothelial dysfunction, has shown promise in other cohorts but remains uninvestigated in a broad surgical intensive care unit (SICU) population. This study aimed to evaluate the association between the aEASIX upon SICU admission and short-term mortality in critically ill surgical patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients and methods:\u003c/strong\u003e This retrospective cohort study utilized data from the MIMIC-IV database. We included 4,394 adult patients with a first-time admission to a SICU. Because the aEASIX distribution was markedly skewed, we applied a natural-log transformation (LnaEASIX). The primary outcome was 28-day ICU mortality, and the secondary outcome was 28-day in-hospital mortality. Multivariable logistic regression, restricted cubic splines, and receiver operating characteristic (ROC) curve analyses were performed. A mediation analysis explored the role of the SOFA score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The overall 28-day ICU and 28-day in-hospital mortality rates were 18.0% and 17.09%, respectively. After adjusting for confounders, a higher LnaEASIX was independently associated with an increased risk of both 28-day ICU mortality (OR 1.348, 95% CI 1.259-1.445, P \u0026lt; 0.001) and 28-day in-hospital mortality (OR 1.367, 95% CI 1.275-1.465, P \u0026lt; 0.001). A non-linear, dose-response relationship was observed for both outcomes. In predictive performance analysis, aEASIX demonstrated significantly better discrimination than the SOFA score for both 28-day ICU mortality (AUC: 0.653 vs. 0.625) and 28-day in-hospital mortality (AUC: 0.649 vs. 0.614). Mediation analysis revealed that the SOFA score mediated 17.6% and 14.7% of the total effect of LnaEASIX on 28-day ICU and 28-day in-hospital mortality, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe aEASIX is a significant and independent predictor of short-term mortality in a large, heterogeneous cohort of critically ill surgical patients. As a simple, readily available tool, aEASIX outperforms the SOFA score in predicting both ICU and in-hospital mortality and may serve as a valuable instrument for early bedside risk stratification in the SICU.\u003c/p\u003e","manuscriptTitle":"Assessing the prognostic value of Age-adjusted EASIX for predicting mortality in critically ill surgical patients: a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 12:59:26","doi":"10.21203/rs.3.rs-7364770/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-02T06:28:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T14:13:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247042954811257264367280675679751733902","date":"2025-12-27T10:09:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242740864408248438069146660896685217310","date":"2025-12-24T15:17:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245467535102047332736605647713270266936","date":"2025-12-23T08:42:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154206800108022338642980491977339360403","date":"2025-12-22T11:16:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T05:16:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-10-29T08:24:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-16T17:49:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138459846219448361398214429436222069589","date":"2025-09-15T19:24:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-15T19:07:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-22T09:01:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-21T06:17:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-08-13T11:54:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f3fa4b9-1003-4c62-97f8-33232a0c8741","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:28:50+00:00","versionOfRecord":{"articleIdentity":"rs-7364770","link":"https://doi.org/10.1186/s40001-026-04297-w","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2026-03-28 16:11:54","publishedOnDateReadable":"March 28th, 2026"},"versionCreatedAt":"2025-09-24 12:59:26","video":"","vorDoi":"10.1186/s40001-026-04297-w","vorDoiUrl":"https://doi.org/10.1186/s40001-026-04297-w","workflowStages":[]},"version":"v1","identity":"rs-7364770","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7364770","identity":"rs-7364770","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}