{"paper_id":"24898aee-bf2f-4439-9ec5-080f8b25b653","body_text":"Association between Endothelial Activation and Stress Index and mortality in coronary heart disease ICU 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 Association between Endothelial Activation and Stress Index and mortality in coronary heart disease ICU patients: A retrospective cohort study Shuyang Dai, Bingjie Li, Zongshan Zhang, Tingting Wang, Si Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8191194/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Coronary heart disease (CHD) is a major cause of mortality in critically ill patients, with endothelial dysfunction playing a pivotal role in disease progression. The Endothelial Activation and Stress Index (EASIX), a composite biomarker reflecting endothelial injury and systemic stress, has demonstrated prognostic value across various cardiovascular conditions. Nevertheless, the association of this phenomenon with mortality in CHD patients requiring intensive care remains to be elucidated. Methods A retrospective cohort study was conducted using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The study encompassed a total of 2,469 critically ill CHD patients, who were stratified into tertiles based on admission EASIX scores. The application of Cox proportional hazard models and restricted cubic spline regression was utilised for the purpose of evaluating the association between EASIX and all-cause mortality within a 30-day, 90-day and 365-day timeframe. Subgroup analyses were performed in order to assess potential effect modifications. Results The median age of the cohort was found to be 73 years (interquartile range: 64–81 years), with 66.13% of subjects being male (1632/2468). Patients in the highest EASIX tertile exhibited significantly elevated 30-day mortality (34.87% vs. 10.45%, p<0.001) and 365-day mortality (38.88% vs. 12.15%, p<0.001) compared to the lowest tertile. Cox regression analysis revealed EASIX to be an independent predictor of 30-day mortality (adjusted hazard ratio [HR]: 2.237; 95% confidence interval [CI]: 1.534-3.262; p < 0.001) and 365-day mortality (adjusted HR: 2.204; 95% CI: 1.553-3.129; p < 0.001) following comprehensive adjustment for confounders. The Kaplan-Meier analysis demonstrated a significantly inferior survival probability in the highest EASIX stratum (log-rank p<0.0001). The restricted cubic spline analysis indicated a near-linear dose-response relationship between EASIX and mortality risk (p for non-linearity < 0.05). Conclusion Elevated EASIX scores have been shown to independently correlate with increased short and long-term mortality in critically ill CHD patients, suggesting its utility as a novel prognostic biomarker for risk stratification in this high-risk population. Further prospective validation and investigation of therapeutic implications are warranted. Clinical trial number Not applicable. Coronary heart disease Intensive care unit Endothelial Activation and Stress Index Mortality Predictor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Coronary heart disease (CHD) constitutes the predominant cause of cardiovascular morbidity and mortality globally, accounting for approximately 17.8 million deaths annually[ 1 ]. The pathophysiology of CHD involves complex interactions among atherosclerotic plaque formation, myocardial ischemia, and thrombotic complications, with endothelial dysfunction serving as a critical initiating and perpetuating factor[ 2 ]. Critically ill CHD patients, particularly those presenting with acute coronary syndromes (ACS) or post-cardiac arrest states, demonstrate exacerbated endothelial injury and systemic inflammatory responses, which significantly influence clinical outcomes[ 3 ]. The Endothelial Activation and Stress Index (EASIX) is a clinically accessible composite score calculated from lactate dehydrogenase (LDH), creatinine, and platelet counts, originally developed to prognosticate outcomes in hematopoietic stem cell transplantation[ 4 ]. Recent evidence has expanded its application to diverse conditions characterized by endothelial perturbation, including COVID-19, sepsis, and hypertensive emergencies[ 5 – 7 ]. The index encapsulates key pathophysiological processes: LDH reflects cellular death and tissue hypoperfusion, creatinine mirrors renal microcirculatory integrity, and platelet count indicates consumptive coagulopathy and endothelial interaction[ 8 ]. Endothelial dysfunction in CHD manifests as impaired nitric oxide bioavailability, increased oxidative stress, and upregulated expression of adhesion molecules, collectively promoting plaque vulnerability and adverse cardiac events[ 9 ]. Inflammatory cascades and oxidative damage create a vicious cycle that accelerates coronary artery disease progression[ 10 ]. Despite accumulating data supporting EASIX as a mortality predictor in heterogeneous ICU populations, no prior investigations have specifically examined its prognostic significance in CHD patients with critical illness. We hypothesized that higher EASIX scores at ICU admission would independently predict increased short-term mortality in this cohort. Materials and Methods Study Population This retrospective cohort study utilized the MIMIC-IV database (version 3.1), a publicly accessible critical care repository from Beth Israel Deaconess Medical Center[ 11 ]. The Institutional Review Boards of MIT and BIDMC approved the database, with waiver of informed consent. One author (SD) completed the required human subject research training (record ID: 14012091). We identified adult patients (≥ 18 years) admitted to the ICU between 2008–2019 with a primary diagnosis of CHD according to ICD-9/10 codes. Exclusion criteria comprised: (1) patients under 18 years old at the time of first admission; (2)multiple ICU admissions (only first admission retained); (3) Missing data on LDH, creatinine or Plantet within 24 hours of admission; (4)Outlier data on LDH, creatinine or Plantet. Outlier data were defined based on data quantiles and the interquartile range (IQR), data points beyond Q1 − 1.5IQR or Q3 + 1.5IQR were considered outliers. The final cohort included 2,469 patients stratified into EASIX tertiles (Fig. 1 ). Data Collection Data extraction employed PostgreSQL (version 13.7.2) and Navicat Premium (version 16). Variables encompassed: (1) demographics: age, sex, race, body mass index(BMI); (2) comorbidities: respiratory failure, arterial fibrillation, hypertension, acute kidney injury(AKI), stroke, hypertension, type 2 diabetes mellitus(T2DM), heart failure, myocardial infarction; (3) laboratory parameters: hemoglobin, red blood cells(RBC), white blood cells(WBC), international normalized ratio(INR), prothrombin time(PT), activated partial thromboplastin time(APTT), fasting blood glucose(FBG), potassium, sodium, serum creatinine, lactate dehydrogenase(LDH), and platelet; (4) severity scores: SOFA, SAPS II, APS III, OASIS; and (5)therapeutic༚ invasive ventilation, antihypertensive drug, glucocorticoid. EASIX was calculated as: [LDH (U/L) × creatinine (mg/dL)] / platelet (10⁹/L) using admission values within 24 hours[ 12 ]. The primary outcomes were 30-day and 365-day all-cause mortality, and the secondary outcome was 90-day all-cause mortality. Statistical Analysis Continuous variables were expressed as mean ± SD or median (IQR) based on normality (Kolmogorov-Smirnov test). Categorical variables were presented as frequencies (percentages). Given EASIX's skewed distribution, log₂ transformation was applied before analysis[ 12 , 13 ]. Inter-group comparisons utilized ANOVA, Kruskal-Wallis, or χ² tests as appropriate. Survival analysis employed Kaplan-Meier curves with log-rank tests. Cox proportional hazards models calculated hazard ratios (HR) and 95% confidence intervals (CI) across three hierarchical models: Model 1 (unadjusted); Model 2 (demographics); Model 3 (adjust all parameters). The proportional hazards assumption was verified using Schoenfeld residuals. Restricted cubic spline regression with four knots assessed non-linear relationships. Subgroup analyses examined effect modification by age (<65 vs. ≥65 years), sex, BMI(<25 vs. ≥25), arterial fibrillation, stroke, T2DM and heart failure status. The Boruta algorithm identified variable importance for mortality prediction. All statistical analyses were performed using Stata 17.0, R version 4.3.2 and DecisionLinnc 1.1 software, with a p-value of less than 0.05 being considered statistically significant. DecisionLinnc 1.1 is a data analysis platform integrating multiple programming languages and providing a visual interface for processing data and performing analyses[ 14 ]. Results Baseline Characteristics The median age was 73 (IQR: 64–81) years, with 1632 (66.13%) male patients. The overall 30-day, 90-day, and 365-day mortality rates were 20.99%, 22.65% and 23.99%, respectively. Patients in the highest EASIX tertile (T3) were older, predominantly male, and exhibited higher prevalence of respiratory failure, arterial fibrillation, AKI and T2DM(Table 1 ). Laboratory indices revealed significant trends across tertiles: platelet counts decreased (T1: 224.5×10⁹/L vs. T3: 133×10⁹/L, p < 0.001), while LDH (T1: 215 U/L vs. T3: 435 U/L, p < 0.001) and creatinine (T1: 0.85 mg/dL vs. T3:1.9 mg/dL, p < 0.001) progressively increased. Severity scores demonstrated analogous deterioration (SOFA: T1 = 4 vs. T3 = 8, p < 0.001). Table 1 Baseline characteristics stratified by EASIX tertiles Characteristics Overall(n = 2468) T1(n = 823) T2(n = 822) T3(n = 823) p .value Age(years) 73 (64–81) 72 (62–81) 74 (64–82) 74 (66–82) 0.001 Sex (%) Female 836 (33.87) 344 (41.80) 249 (30.29) 243 (29.53) < 0.001 Male 1632 (66.13) 479 (58.20) 573 (69.71) 580 (70.47) Race (%) Black 154 (6.24) 42 (5.10) 56 (6.81) 56 (6.80) 0.578 White 2039 (82.62) 691 (83.96) 674 (82.00) 674 (81.90) Others 275 (11.14) 90 (10.94) 92 (11.19) 93 (11.30) BMI(kg/m 2 ) 27.682 (24.251–31.831) 26.953 (23.959–31.211) 27.948 (24.333–32.277) 28.086 (24.566–32.19) 0.017 Comorbidities Respiratory failure (%) 1137 (46.07) 264 (32.08) 388 (47.20) 485 (58.93) < 0.001 Arterial fibrillation (%) 1090 (44.17) 311 (37.79) 372 (45.26) 407 (49.45) < 0.001 Hypertension (%) 900 (36.47) 414 (50.30) 276 (33.58) 210 (25.52) < 0.001 AKI (%) 1244 (50.41) 182 (22.11) 436 (53.04) 626 (76.06) < 0.001 Stroke (%) 249 (10.09) 75 (9.11) 91 (11.07) 83 (10.09) 0.420 T2DM (%) 963 (39.02) 251 (30.50) 335 (40.75) 377 (45.81) < 0.001 Heart failure (%) 1310 (53.08) 323 (39.25) 473 (57.54) 514 (62.45) < 0.001 Myocardial infarction (%) 856 (34.68) 211 (25.64) 300 (36.50) 345 (41.92) < 0.001 illness severity scores SOFA 6 (3–9) 4 (2–6) 5 (3–8) 8 (6–11) < 0.001 APSIII 47 (35–62) 38 (29–49) 46 (36–58) 58 (47–74) < 0.001 SAPSII 40 (32–50) 35 (28–43) 39 (32–48) 46 (38–57) < 0.001 OASIS 34 (28–40) 32 (26–38) 33 (28–39) 36 (30–43) < 0.001 Laboratory tests Hemoglobin(g/dL) 10.4 (8.97-11.972) 10.67 (9.19–12.19) 10.5 (9-12.022) 10.1 (8.7–11.7) < 0.001 RBC (×10 6 /µL) 3.47 (3.03–4.02) 3.57 (3.16–4.105) 3.5 (3.04–4.04) 3.37 (2.89–3.9) < 0.001 WBC (×10 3 /µL) 11.4 (8.5–15.6) 11.05 (8.365–14.45) 11.4 (8.57–15.6) 11.8 (8.415–16.76) < 0.001 INR 1.3 (1.17–1.57) 1.2 (1.1–1.4) 1.3 (1.18–1.5) 1.41 (1.2–1.85) < 0.001 PT(sec) 14.24 (12.8-17.135) 13.5 (12.408–15.3) 14.2 (12.8-16.45) 15.625 (13.45-20.148) < 0.001 APTT(sec) 36 (29.26–54.83) 33.5 (28.315–49.125) 36 (29.162–53.63) 39.225 (30.855–62.455) < 0.001 FBG (mg/dL) 135.33 (112–176) 125 (107–151) 138 (113.5-177.917) 147.415 (118.082-196.375) < 0.001 Potassium (mEq/L) 4.17 (3.87–4.53) 4.07 (3.83–4.378) 4.18 (3.9-4.545) 4.3 (3.95–4.73) < 0.001 Sodium (mEq/L) 138.5 (135.67–141) 138.5 (136-140.5) 138.5 (136–141) 138.25 (135-141.225) 0.976 Serum creatinine(mg/dL) 1.2 (0.87–1.77) 0.85 (0.7–1.03) 1.2 (0.952-1.6) 1.9 (1.33–2.7) < 0.001 EASIX 1.129 (0.206–2.089) -0.115 (-0.57-0.205) 1.129 (0.843–1.393) 2.486 (2.089–3.142) < 0.001 LDH(U/L) 296 (219–435) 215 (177–272) 300.75 (239.125–407.75) 435 (313.5-618.335) < 0.001 Platelet(×10⁹/L) 180 (133–237) 224.5 (174.75-287.835) 180.67 (147-229.5) 133 (93.855-182.285) < 0.001 Therapeutic Invasive ventilation (%) 2212 (89.63) 722 (87.73) 739 (89.90) 751 (91.25) 0.061 Antihypertensive drug(%) 2107 (85.37) 692 (84.08) 712 (86.62) 703 (85.42) 0.347 Glucocorticoid(%) 647 (26.22) 161 (19.56) 217 (26.40) 269 (32.69) < 0.001 Events 30-day mortality (%) 518 (20.99) 86 (10.45) 145 (17.64) 287 (34.87) < 0.001 90-day mortality (%) 559 (22.65) 96 (11.66) 161 (19.59) 302 (36.70) < 0.001 365-day mortality (%) 592 (23.99) 100 (12.15) 172 (20.92) 320 (38.88) < 0.001 Data are presented as median (IQR) or frequencies (percentages). Abbreviation: EASIX, Endothelial Activation and Stress Index; BMI, body mass index; AKI, acute kidney injury; T2DM, type 2 diabetes mellitus; RBC, red blood cell; WBC, white blood cells; INR, international normalized ratio; PT, prothrombin time; APTT, activated partial thromboplastin time; FBG, fasting blood glucose; LDH, lactate dehydrogenase; SOFA, sequential organ failure assessment; SAPS II, simplified acute physiological score II; APS III, acute physiology score III; OASIS, oxford acute severity of illness score. Table 2 Comparisons between survivors and non-survivors based on 30-day outcomes Characteristics Overall (n = 2468) Survivors (n = 1950) Non-survivors (n = 518) p .value Age 73 (64–81) 72 (63–81) 76 (68–82) < 0.001 Sex (%) Female 836 (33.87) 646 (33.13) 190 (36.68) 0.143 Male 1632 (66.13) 1304 (66.87) 328 (63.32) Race (%) Black 154 (6.24) 118 (6.05) 36 (6.95) 0.258 White 2039 (82.62) 1605 (82.31) 434 (83.78) Others 275 (11.14) 227 (11.64) 48 (9.27) BMI 27.68(24.25–31.83) 27.8 (24.36–31.80) 27.191 (23.88–31.98) 0.056 Comorbidities Respiratory failure (%) 1137 (46.07) 773 (39.64) 364 (70.27) < 0.001 Arterial fibrillation (%) 1090 (44.17) 826 (42.36) 264 (50.97) 0.001 Hypertension (%) 900 (36.47) 741 (38.00) 159 (30.69) 0.003 AKI (%) 1244 (50.41) 868 (44.51) 376 (72.59) < 0.001 Stroke (%) 249 (10.09) 202 (10.36) 47 (9.07) 0.435 T2DM (%) 963 (39.02) 746 (38.26) 217 (41.89) 0.145 Heart failure (%) 1310 (53.08) 1004 (51.49) 306 (59.07) 0.002 Myocardial infarction (%) 856 (34.68) 678 (34.77) 178 (34.36) 0.904 illness severity scores SOFA 6 (3–9) 5 (3–8) 8 (5–11) < 0.001 APSIII 47 (35–62) 43 (33–56) 64 (50–79) < 0.001 SAPSII 40 (32–50) 37 (31–47) 48 (40–60) < 0.001 OASIS 34 (28–40) 32 (27-38.75) 39 (32–46) < 0.001 Laboratory tests Hemoglobin(g/dL) 10.4 (8.97–11.97) 10.53 (9.1–12.1) 9.91 (8.535–11.45) < 0.001 RBC (×10 6 /µL) 3.47 (3.03–4.02) 3.51 (3.08–4.07) 3.34 (2.87–3.88) < 0.001 WBC (×10 3 /µL) 11.4 (8.5–15.6) 11.15 (8.4–15.1) 12.435 (8.90-17.57) 0.001 INR 1.3 (1.17–1.57) 1.275 (1.15–1.5) 1.45 (1.2–1.9) < 0.001 PT(sec) 14.24 (12.8-17.135) 14.03 (12.7–16.3) 15.95 (13.5–20.9) < 0.001 APTT(sec) 36 (29.26–54.83) 35.15 (28.96–53.04) 38.7 (30.39–62.56) < 0.001 FBG (mg/dL) 135.33 (112–176) 132 (111–170) 148.415 (119.62-193.63) < 0.001 Potassium (mEq/L) 4.17 (3.87–4.53) 4.15 (3.87–4.5) 4.2 (3.87–4.65) 0.004 Sodium (mEq/L) 138.5 (135.67–141) 138.5 (136–141) 138.33 (134.8-141.4) 0.311 Serum creatinine(mg/dL) 1.2 (0.87–1.77) 1.1 (0.85–1.6) 1.57 (1.08–2.37) < 0.001 EASIX 1.129 (0.21–2.09) 0.927 (0.09–1.83) 1.962 (0.96–2.84) < 0.001 LDH(U/L) 296 (219–435) 280.5 (207-396.75) 391 (274.25-566.75) < 0.001 Platelet(×10 3 /µL) 180 (133–237) 181 (136.25-236.38) 173.34 (122-238.94) 0.123 Therapeutic Invasive ventilation (%) 2212 (89.63) 1744 (89.44) 468 (90.35) 0.600 Antihypertensive drugs(%) 2107 (85.37) 1695 (86.92) 412 (79.54) < 0.001 Glucocorticoid(%) 647 (26.22) 448 (22.97) 199 (38.42) < 0.001 Data are presented as median (IQR) or frequencies (percentages). Abbreviation: EASIX, Endothelial Activation and Stress Index; BMI, body mass index; AKI, acute kidney injury; T2DM, type 2 diabetes mellitus; RBC, red blood cell; WBC, white blood cells; INR, international normalized ratio; PT, prothrombin time; APTT, activated partial thromboplastin time; FBG, fasting blood glucose; LDH, lactate dehydrogenase; SOFA, sequential organ failure assessment; SAPS II, simplified acute physiological score II; APS III, acute physiology score III; OASIS, oxford acute severity of illness score. EASIX and Mortality Associations Non-survivors exhibited significantly higher EASIX scores compared to survivors [1.96 (0.96–2.84) vs. 0.93 (0.09–1.83), p < 0.001] based on 30-day outcomes (Table 2 ). Mortality analyses (Tables S1-S2) revealed that non-survivors had consistently higher EASIX values than survivors at 90-day and 365-day outcomes. The Boruta algorithm identified EASIX as a \"Confirmed\" predictor of 30-day mortality, with importance Z-score ranking third after APSIII and SAPS II (Fig. 2 ). Kaplan-Meier analysis revealed significant mortality gradients across EASIX tertiles for both 30-day and 365-day endpoints (log-rank p < 0.001) (Fig. 3 ). Patients in the highest EASIX tertile exhibited significantly elevated 30-day mortality (34.87% vs. 10.45%, p < 0.001) and 365-day mortality (38.88% vs. 12.15%, p < 0.001) compared to the lowest tertile. Similar findings were observed for 90-day mortality(fig. S1 ). Cox regression demonstrated consistent associations across all models (Table 3 ). In the fully-adjusted Model 3, each log₂-unit increase in EASIX conferred an 52.2% higher mortality risk at 30days (HR: 1.522; 95% CI: 1.346–1.72; p < 0.001) and 47.7% higher mortality risk at 365 days (HR: 1.477; 95% CI: 1.315–1.653; p < 0.001). Compared to T1, T3 patients exhibited a 2.237-fold elevated mortality risk at 30 days (HR: 2.237; 95% CI: 1.534–3.262; p < 0.001) and 2.204-fold elevated mortality risk at 365 days (HR: 2.204; 95% CI: 1.553–3.129; p < 0.001) after multivariable adjustment. These patterns remained consistent at the intermediate 90-day time point, confirming the robust prognostic value of EASIX. Table 3 Cox proportional hazards analysis for in-hospital mortality Events (%) Model 1 Model 2 Model 3 HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value 30-day mortality Continuous variable per unit 518(20.99) 1.523(1.436–1.614) < 0.001 1.54(1.451–1.635) < 0.001 1.522(1.346–1.72) < 0.001 T1 (n = 823) 86 (3.48) 1.00 1.00 1.00 T2 (n = 822) 145 (5.88) 1.778 (1.362–2.322) < 0.001 1.786(1.365–2.337) < 0.001 1.426(1.043–1.952) 0.026 T3(n = 823) 287 (11 .63) 3.911 (3.073–4.977) < 0.001 3.95(3.095–5.04) < 0.001 2.237(1.534–3.262) < 0.001 P for trend < 0.001 < 0.001 < 0.001 90-day mortality Continuous variable per unit 559(22.65) 1.503(1.420–1.591) < 0.001 1.519(1.434–1.61) < 0.001 1.499(1.333–1.686) < 0.001 T1 (n = 823) 96 (3.89) 1.00 1.00 1.00 T2 (n = 822) 161 (6.52) 1.774 (1.378–2.284) < 0.001 1.79 (1.387–2.308) < 0.001 1.426 (1.059–1.919) 0.019 T3(n = 823) 302 (12.24) 3.732 (2.966–4.696) < 0.001 3.782 (2.998–4.772) < 0.001 2.162 (1.509–3.098) < 0.001 P for trend < 0.001 < 0.001 < 0.001 365-day mortality Continuous variable per unit 592(23.99) 1.512(1.431–1.597) < 0.001 1.526(1.443–1.614) < 0.001 1.474(1.315–1.653) < 0.001 T1 (n = 823) 96 (3.89) 1.00 1.00 1.00 T2 (n = 822) 161 (6.52) 1.824(1.426–2.334) < 0.001 1.844(1.439–2.364) < 0.001 1.465(1.097–1.958) 0.01 T3(n = 823) 302 (12.24) 3.836(3.064–4.802) < 0.001 3.891(3.101–4.883) < 0.001 2.204(1.553–3.129) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Model 1: unadjusted crude model. Model 2: adjusted for sex, age, race, BMI. Model 3: adjusted for factors in Model 2 and respiratory failure, arterial fibrillation, hypertension, AKI, stroke, hypertension, T2DM, heart failure, myocardial infarction, hemoglobin, RBC, WBC, PT, APTT, FBG, potassium, sodium, serum creatinine, platelet, SOFA, SAPS II, APS III, OASIS, invasive ventilation, antihypertensive drug, glucocorticoid. Dose-Response Relationship Using Cox model 3(adjusted for sex, age, race,BMI, respiratory failure, arterial fibrillation, hypertension, AKI, stroke, hypertension, T2DM, heart failure, myocardial infarction, hemoglobin, RBC, WBC, PT, APTT, FBG, potassium, sodium, serum creatinine, platelet, SOFA, SAPS II, APS III, OASIS, invasive ventilation, antihypertensive drug, glucocorticoid), RCS regression confirmed a linear dose–response relationship between EASIX and 30-day as well as 365-day all-cause mortality (p for non-linear > 0.05)(Fig. 4 ). A consistent pattern was observed at the intermediate 90-day time point (Fig S2). Figure 4 . Restricted cubic spline analysis of EASIX and 30-day and 365-day all-cause mortality in critically ill CHD based on Cox proportional hazards model 3 . (A)30-day all-cause mortality. (B)365-day all-cause mortality. Cox proportional hazards model 3:adjusted for sex, age, race,BMI, respiratory failure, arterial fibrillation, hypertension, AKI, stroke, hypertension, T2DM, heart failure, myocardial infarction, hemoglobin, RBC, WBC, PT, APTT, FBG, potassium, sodium, serum creatinine, platelet, SOFA, SAPS II, APS III, OASIS, invasive ventilation, antihypertensive drug, glucocorticoid. Subgroup Analyses We performed stratified analyses to examine the consistency between EASIX and 30-day and 365-day mortality association across key clinical subgroups defined by age (< 65 vs. ≥65 years), sex (female vs. male), BMI (< 25 vs. ≥25 kg/m²), atrial fibrillation, stroke, T2DM and heart failure. Using the fully adjusted model 3, we assessed these relationships at 30 and 365 days post-CHD, with T1 serving as the reference category. P for interaction values were computed to assess effect modification across strata. Overall, a consistent dose-dependent relationship was observed, with progressively elevated mortality risk from T2 to T3 across most subgroups. Notably, BMI demonstrated a statistically significant interaction with EASIX for both 30-day (p = 0.028) and 365-day mortality (p = 0.042), whereas no significant interactions were detected for other variables (all p > 0.05), suggesting the prognostic utility of EASIX is largely independent of these clinical characteristics, except for potential effect modification by BMI status. Discussion This study systematically evaluated the predictive value of the EASIX for short-term and long-term outcomes in critically ill patients with CHD admitted to ICU for the first time, based on an analysis of 2,469 patients from the MIMIC-IV database. The main findings showed that an EASIX calculated within 24 hours of admission was an independent predictor of all-cause mortality at 30, 90, and 365 days. Patients in the highest tertile had an approximately 2.2-fold higher mortality risk than those in the lowest tertile, and this association remained robust after full adjustment for multidimensional confounding factors. Further analysis using restricted cubic splines revealed an approximately linear dose-response relationship between EASIX and mortality. The Boruta algorithm identified EASIX as the third most important variable for predicting 30-day mortality, ranking behind only the APS III and the SAPS II. These results support the use of EASIX as a composite biomarker reflecting endothelial injury and systemic stress status, with potential clinical utility in the risk stratification of critically ill CHD patients. EASIX was initially developed by Imahorn et al[ 15 ]for predicting transplant-associated thrombotic microangiopathy and mortality in patients undergoing hematopoietic stem cell transplantation (HSCT). Recent studies have extended its application to disease states centered on endothelial dysfunction, such as COVID-19[ 16 ], sepsis[ 17 ], and hypertensive emergency[ 18 ]. EASIX assesses the severity of endothelial dysfunction by integrating indicators reflecting renal function (creatinine), cellular damage (LDH), and coagulation/inflammatory status (platelets). Endothelial activation is a core mechanism in many pathological processes, such as transplant complications, infection, and immunotherapy toxicity. Elevated EASIX levels indicate endothelial barrier disruption and an increased risk of microthrombus formation[ 19 – 21 ]. The present study is the first to apply EASIX to the population of CHD patients in ICUs, and the results are consistent with those of the aforementioned studies[ 22 , 23 ]. Notably, although the pathophysiological mechanisms of CHD involve multiple processes including atherosclerotic plaque formation, myocardial ischemia, and thrombotic complications, endothelial dysfunction remains a key link in initiating and sustaining disease progression[ 24 ]. In CHD, progressive endothelial cell stress may promote plaque instability, microvascular obstruction, and amplification of systemic inflammatory responses, ultimately leading to multiple organ failure [ 25 ]. The vicious cycle formed by the inflammatory cascade and oxidative damage can accelerate the progression of coronary artery lesions[ 26 ]. By integrating three indicators—LDH, creatinine, and platelet count—EASIX is precisely able to capture the core elements of this pathological process: elevated LDH indicates cell death and tissue hypoperfusion, serum creatinine levels reflect the integrity of renal microcirculation, and decreased platelet count suggests consumptive coagulopathy and activation of endothelial-platelet interactions[ 27 – 30 ]. In the present study, patients in the highest EASIX tertile exhibited a typical \"endothelial injury phenotype\": platelet count was 40.7% lower than that in the lowest tertile (133 vs. 224.5 × 10⁹/L), LDH level was 102.3% higher (435 vs. 215 U/L), creatinine level doubled (1.9 vs. 0.85 mg/dL), accompanied by significant deterioration in SOFA score, and the prevalence of AKI and respiratory failure. This pattern of synchronous multisystem dysregulation is highly consistent with the pathophysiological characteristics of systemic endothelial dysfunction in critically ill CHD patients[ 31 ]. The Boruta algorithm ranked EASIX as more important than traditional laboratory indicators (e.g., creatinine, WBC) and age, suggesting that as a composite index, it may capture systemic pathological information that cannot be reflected by individual variables. Compared with traditional scoring systems, EASIX only requires three routine laboratory tests for calculation, offering convenient clinical access and significant cost-effectiveness, which is particularly important in resource-limited medical settings. The findings of this study offer multiple implications for the clinical management of critically ill CHD patients. Firstly, EASIX can serve as an early risk identification tool. The 30-day mortality rate of patients in the highest tertile (EASIX > 2.089) reached 34.87%, compared with only 10.45% in the lowest tertile. This significant difference suggests that clinicians can identify ultra-high-risk subgroups within 24 hours of ICU admission, thereby triggering more aggressive monitoring and intervention strategies, such as enhanced hemodynamic support, optimization of oxygen supply-demand balance, and early initiation of renal replacement therapy. Secondly, the existence of a linear dose-response relationship indicates that EASIX can not only be used for binary risk stratification but also as a continuous variable for dynamic risk assessment, providing a quantitative basis for adjusting the intensity of individualized treatment. Thirdly, subgroup analysis revealed that the prognostic value of EASIX was more pronounced in patients with BMI < 25 kg/m². This finding may be related to insufficient nutritional reserves and more severe inflammatory responses in underweight patients, suggesting that EASIX has stronger discriminative power in vulnerable populations and facilitates the realization of precision medicine. From the perspective of research tools, EASIX can serve as a valid covariate for patient stratification in clinical trials. In randomized controlled trials (RCTs) evaluating novel anti-inflammatory, antithrombotic, or endothelial protective therapies, incorporating EASIX as a stratification factor can balance baseline risks between groups and enhance statistical power. Furthermore, given its core attribute of reflecting endothelial function, EASIX may potentially be used to monitor the efficacy of targeted therapies for endothelial injury in the future, although this application requires validation by prospective studies. This study has several methodological strengths. Firstly, it features a large and well-defined sample size. As a high-quality ICU cohort, the MIMIC-IV database provides abundant clinical variables and long-term follow-up data, enhancing statistical power. Secondly, the study covers comprehensive time points by evaluating outcomes at 30 days, 90 days, and 365 days, confirming that the predictive value of EASIX is temporally stable—suitable for both short-term prognosis assessment and long-term risk prediction. Thirdly, the statistical analysis methods are rigorous: a multilevel Cox proportional hazards model was used to sequentially adjust for confounding factors, ranging from demographic characteristics to comprehensive laboratory indicators and disease severity scores, ensuring the robustness of the results; restricted cubic spline analysis objectively verified the linear relationship, avoiding selection bias from artificial cutoffs; the Boruta algorithm, based on the principle of random forests, ranked variable importance, reducing the false-positive risk associated with traditional univariate analysis. Fourthly, subgroup analyses covered key clinical characteristics, and interaction tests revealed no significant heterogeneity (except for BMI), indicating that EASIX has broad applicability and is not significantly influenced by factors such as age, gender, or comorbidities. The limitations of this study should be fully considered when interpreting the results. Firstly, the retrospective design inherently limits causal inference. Despite comprehensive adjustments, residual confounding from unmeasured variables (e.g., severity of coronary artery lesions, specific treatment strategies, timing of infection source control) cannot be completely ruled out. Prospective cohort studies are necessary to validate causal relationships. Secondly, although the MIMIC-IV database is large-scale, the 2,469 patients ultimately included in this analysis accounted for only 10.7% of the initially screened population, primarily due to missing key variables in 16,206 patients. This selective inclusion may introduce survival bias and overestimate the predictive performance of EASIX. Since LDH is not a routine test in clinical practice, future studies need to evaluate its generalizability in cohorts with complete data. Thirdly, the single-center data source (Beth Israel Deaconess Medical Center, Boston, USA) restricts the external validity of the results. Differences in clinical practices across regions, ethnicities, and healthcare systems may affect the optimal cutoff value and predictive efficacy of EASIX. Fourthly, this study only used baseline EASIX values calculated within 24 hours of ICU admission and did not assess its dynamic changes. Endothelial injury is a dynamic process, and serial monitoring of EASIX may provide more abundant time-dependent prognostic information. Fifthly, although outliers were excluded, EASIX is calculated as the product of three indicators, meaning measurement errors in any single parameter may be amplified. The potential impact of extreme values on the results requires attention. Future research should focus on the following directions: (1) Conduct multicenter, prospective cohort studies to validate the predictive performance of EASIX across diverse healthcare settings and ethnic populations, and explore ethnicity-specific cutoff values; (2) Design nested studies to assess the incremental predictive value of dynamic changes in EASIX (e.g., daily monitoring) for prognosis, as well as whether a decrease in EASIX following therapeutic interventions is associated with improved survival; (3) Further investigate the biological mechanisms underlying the interaction between BMI and EASIX, particularly the regulatory roles of muscle mass, adipokines, and inflammatory status; (4) Evaluate in randomized controlled trials whether EASIX-guided treatment strategies (e.g., intensified antithrombotic, anti-inflammatory, or endothelial protective therapy) can improve clinical outcomes, and validate its potential as a therapeutic target; (5) Explore the combined application of EASIX with other emerging biomarkers (e.g., soluble thrombomodulin, von Willebrand factor), to construct more accurate prognostic models related to endothelial injury. Conclusion This study confirms that EASIX is an independent predictor of short-term and long-term mortality in CHD patients admitted to ICUs. Its predictive performance is robust, with an approximately linear dose-response relationship with mortality. As a simple and cost-effective composite biomarker, EASIX can effectively capture the systemic pathological status characterized by endothelial injury, microcirculatory disorders, and coagulation dysfunction, providing a novel tool for early clinical risk stratification. Despite limitations such as the retrospective design and selection bias, the current evidence sufficiently supports the routine monitoring of EASIX in critically ill CHD patients. Future prospective studies are needed to further validate its clinical utility and explore its potential value as an indicator for monitoring treatment responses and a therapeutic target. Abbreviations CHD Coronary heart disease ICU intensive care unit EASIX Endothelial Activation and Stress Index MIMIC-IV Medical Information Mart for Intensive Care IV HR hazard ratio CI confidence interval LDH lactate dehydrogenase IQR interquartile range RCS restricted cubic spline BMI body mass index AKI acute kidney injury T2DM type 2 diabetes mellitus WBC white blood cells RBC red blood cells INR international normalized ratio PT prothrombin time APTT activated partial thromboplastin time FBG fasting blood glucose SOFA Sequential Organ Failure Assessment APSIII Acute Physiology ScoreIII (APSIII) SAPS II simplified acute physiological score II OASIS oxford acute severity of illness score Declarations Acknowledgements Not applicable. Author contributions Si Guo designed and conceptualized this study, Shuyang Dai analyzed the data and wrote the manuscript, Bingjie Li drew the images, and Zongshan Zhang and Tingting Wang checked the manuscript. Data availability The datasets generated during and/or analyzed during the current study are available in the MIMIC-IV database, https://physionet.org/content/mimiciv/3.0. Ethics approval and consent to participate The requirement of ethical approval for this was waived by the Institutional Review Board of Fuwai Central China Cardiovascular Hospital, because the data was accessed from MIMIC-IV (a publicly available database). The need for written informed consent was waived by the Institutional Review Board of Fuwai Central China Cardiovascular Hospital due to retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations. Consent for publication Not applicable. Competing interests The authors declare no competing interests.Publisher’s note. References Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. 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Modified EASIX predicts severe cytokine release syndrome and neurotoxicity after chimeric antigen receptor T cells. Blood Adv. 2021;5:3397–406. https://doi.org/10.1182/bloodadvances.2020003885 . Reventun P, Sánchez-Esteban S, Cook-Calvete A, Delgado-Marín M, Roza C, Jorquera-Ortega S, et al. Endothelial ILK induces cardioprotection by preventing coronary microvascular dysfunction and endothelial-to-mesenchymal transition. Basic Res Cardiol. 2023;118:28. https://doi.org/10.1007/s00395-023-00997-0 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial11.24.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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08:33:03\",\"extension\":\"xml\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":136465,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"b2638a26f0614d0a8fad3d21c0c3e8cf1structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8191194/v1/0ce1989abb1dbebb72a7287a.xml\"},{\"id\":98427570,\"identity\":\"673c0157-fb53-4d42-a706-7a53cf2e4b80\",\"added_by\":\"auto\",\"created_at\":\"2025-12-17 16:40:45\",\"extension\":\"html\",\"order_by\":15,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":144862,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8191194/v1/ebff3f6de5d598cc4e2e6fac.html\"},{\"id\":98427293,\"identity\":\"ee5d6f0a-c25f-4b93-af67-8f6d40d7686c\",\"added_by\":\"auto\",\"created_at\":\"2025-12-17 16:40:04\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":22187,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFlowchart of the selection of patients. MIMIC-IV: Medical Information Mart for Intensive Care IV; ICU: intensive care unit; LDH: Lactate dehydrogenase.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8191194/v1/f2ccf2ee9a0784afb9146da7.png\"},{\"id\":98425966,\"identity\":\"2800ae21-9d09-4c5f-8938-c34630975cba\",\"added_by\":\"auto\",\"created_at\":\"2025-12-17 16:35:25\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":89834,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVariable importance ranking for 30-daymortality by Boruta algorithm\\u003c/strong\\u003e. Abbreviation: EASIX, Endothelial Activation and Stress Index; BMI, body mass index; AKI, acute kidney injury; RBC, red blood cell; WBC, white blood cells; INR, international normalized ratio; PT, prothrombin time; FBG, fasting blood glucose; LDH, lactate dehydrogenase; SOFA, sequential organ failure assessment; SAPSII, simplified acute physiological scoreII; APSIII, acute physiology score III; OASIS, oxford acute severity of illness score.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8191194/v1/f204bf71b15e671e9b021659.png\"},{\"id\":98427638,\"identity\":\"d24442f6-f7fe-47d9-842f-beb403e51edb\",\"added_by\":\"auto\",\"created_at\":\"2025-12-17 16:40:54\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":80459,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eKaplan-Meier survival curves comparing EASIX tertiles\\u003c/strong\\u003e. (A) Death within 30 days after admission. (B) Death within 365 days after admission.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8191194/v1/e695f19de17c95bea5326f9d.png\"},{\"id\":98427662,\"identity\":\"984c82cc-1583-4385-9e91-a8e4860eb56d\",\"added_by\":\"auto\",\"created_at\":\"2025-12-17 16:40:57\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":30999,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRestricted cubic spline analysis of EASIX and 30-day and 365-day all-cause mortality in critically ill CHD based on Cox proportional hazards model 3\\u003c/strong\\u003e. (A)30-day all-cause mortality. (B)365-day all-cause mortality. Cox proportional hazards model 3:adjusted for sex, age, race,BMI, respiratory failure, arterial fibrillation, hypertension, AKI, stroke, hypertension, T2DM, heart failure, myocardial infarction, hemoglobin, RBC, WBC, PT, APTT, FBG, potassium, sodium, serum creatinine, platelet, SOFA, SAPS II, APS III, OASIS, invasive ventilation, antihypertensive drug, glucocorticoid.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8191194/v1/ac4ac0b2051d169d35a5aace.png\"},{\"id\":98425852,\"identity\":\"69e96f7b-5e06-4e3f-8705-eaa3f93f6e41\",\"added_by\":\"auto\",\"created_at\":\"2025-12-17 16:35:18\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":135243,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFig 4. Subgroup analysis forest plots for 30-day and 365-day mortality\\u003c/strong\\u003e. (A)30-day all-cause mortality; (B)365-day all-cause mortality.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8191194/v1/e2239fb179eea4c1fb9eda2c.png\"},{\"id\":106972774,\"identity\":\"1a2adf32-f00c-44f3-9b87-e828da9025a3\",\"added_by\":\"auto\",\"created_at\":\"2026-04-15 10:24:12\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1606283,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8191194/v1/6776e7cf-1590-4364-9970-53f30b291a88.pdf\"},{\"id\":98048257,\"identity\":\"b9a3903c-6b0f-4370-bc52-460d1f333c98\",\"added_by\":\"auto\",\"created_at\":\"2025-12-12 08:33:03\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":157712,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryMaterial11.24.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8191194/v1/771f3f133803bed7069f97a8.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Association between Endothelial Activation and Stress Index and mortality in coronary heart disease ICU patients: A retrospective cohort study\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eCoronary heart disease (CHD) constitutes the predominant cause of cardiovascular morbidity and mortality globally, accounting for approximately 17.8\\u0026nbsp;million deaths annually[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. The pathophysiology of CHD involves complex interactions among atherosclerotic plaque formation, myocardial ischemia, and thrombotic complications, with endothelial dysfunction serving as a critical initiating and perpetuating factor[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Critically ill CHD patients, particularly those presenting with acute coronary syndromes (ACS) or post-cardiac arrest states, demonstrate exacerbated endothelial injury and systemic inflammatory responses, which significantly influence clinical outcomes[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eThe Endothelial Activation and Stress Index (EASIX) is a clinically accessible composite score calculated from lactate dehydrogenase (LDH), creatinine, and platelet counts, originally developed to prognosticate outcomes in hematopoietic stem cell transplantation[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Recent evidence has expanded its application to diverse conditions characterized by endothelial perturbation, including COVID-19, sepsis, and hypertensive emergencies[\\u003cspan additionalcitationids=\\\"CR6\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. The index encapsulates key pathophysiological processes: LDH reflects cellular death and tissue hypoperfusion, creatinine mirrors renal microcirculatory integrity, and platelet count indicates consumptive coagulopathy and endothelial interaction[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eEndothelial dysfunction in CHD manifests as impaired nitric oxide bioavailability, increased oxidative stress, and upregulated expression of adhesion molecules, collectively promoting plaque vulnerability and adverse cardiac events[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Inflammatory cascades and oxidative damage create a vicious cycle that accelerates coronary artery disease progression[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Despite accumulating data supporting EASIX as a mortality predictor in heterogeneous ICU populations, no prior investigations have specifically examined its prognostic significance in CHD patients with critical illness. We hypothesized that higher EASIX scores at ICU admission would independently predict increased short-term mortality in this cohort.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStudy Population\\u003c/h2\\u003e\\u003cp\\u003eThis retrospective cohort study utilized the MIMIC-IV database (version 3.1), a publicly accessible critical care repository from Beth Israel Deaconess Medical Center[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. The Institutional Review Boards of MIT and BIDMC approved the database, with waiver of informed consent. One author (SD) completed the required human subject research training (record ID: 14012091).\\u003c/p\\u003e\\u003cp\\u003eWe identified adult patients (\\u0026ge;\\u0026thinsp;18 years) admitted to the ICU between 2008\\u0026ndash;2019 with a primary diagnosis of CHD according to ICD-9/10 codes. Exclusion criteria comprised: (1) patients under 18 years old at the time of first admission; (2)multiple ICU admissions (only first admission retained); (3) Missing data on LDH, creatinine or Plantet within 24 hours of admission; (4)Outlier data on LDH, creatinine or Plantet. Outlier data were defined based on data quantiles and the interquartile range (IQR), data points beyond Q1\\u0026thinsp;\\u0026minus;\\u0026thinsp;1.5IQR or Q3\\u0026thinsp;+\\u0026thinsp;1.5IQR were considered outliers. The final cohort included 2,469 patients stratified into EASIX tertiles (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eData Collection\\u003c/h3\\u003e\\n\\u003cp\\u003eData extraction employed PostgreSQL (version 13.7.2) and Navicat Premium (version 16). Variables encompassed: (1) demographics: age, sex, race, body mass index(BMI); (2) comorbidities: respiratory failure, arterial fibrillation, hypertension, acute kidney injury(AKI), stroke, hypertension, type 2 diabetes mellitus(T2DM), heart failure, myocardial infarction; (3) laboratory parameters: hemoglobin, red blood cells(RBC), white blood cells(WBC), international normalized ratio(INR), prothrombin time(PT), activated partial thromboplastin time(APTT), fasting blood glucose(FBG), potassium, sodium, serum creatinine, lactate dehydrogenase(LDH), and platelet; (4) severity scores: SOFA, SAPS II, APS III, OASIS; and (5)therapeutic༚ invasive ventilation, antihypertensive drug, glucocorticoid. EASIX was calculated as: [LDH (U/L) \\u0026times; creatinine (mg/dL)] / platelet (10⁹/L) using admission values within 24 hours[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. The primary outcomes were 30-day and 365-day all-cause mortality, and the secondary outcome was 90-day all-cause mortality.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e\\u003cp\\u003eContinuous variables were expressed as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD or median (IQR) based on normality (Kolmogorov-Smirnov test). Categorical variables were presented as frequencies (percentages). Given EASIX's skewed distribution, log₂ transformation was applied before analysis[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Inter-group comparisons utilized ANOVA, Kruskal-Wallis, or χ\\u0026sup2; tests as appropriate. Survival analysis employed Kaplan-Meier curves with log-rank tests. Cox proportional hazards models calculated hazard ratios (HR) and 95% confidence intervals (CI) across three hierarchical models: Model 1 (unadjusted); Model 2 (demographics); Model 3 (adjust all parameters). The proportional hazards assumption was verified using Schoenfeld residuals. Restricted cubic spline regression with four knots assessed non-linear relationships. Subgroup analyses examined effect modification by age (\\u0026lt;65 vs. \\u0026ge;65 years), sex, BMI(\\u0026lt;25 vs. \\u0026ge;25), arterial fibrillation, stroke, T2DM and heart failure status. The Boruta algorithm identified variable importance for mortality prediction. All statistical analyses were performed using Stata 17.0, R version 4.3.2 and DecisionLinnc 1.1 software, with a p-value of less than 0.05 being considered statistically significant. DecisionLinnc 1.1 is a data analysis platform integrating multiple programming languages and providing a visual interface for processing data and performing analyses[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e].\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eBaseline Characteristics\\u003c/h2\\u003e\\u003cp\\u003eThe median age was 73 (IQR: 64\\u0026ndash;81) years, with 1632 (66.13%) male patients. The overall 30-day, 90-day, and 365-day mortality rates were 20.99%, 22.65% and 23.99%, respectively. Patients in the highest EASIX tertile (T3) were older, predominantly male, and exhibited higher prevalence of respiratory failure, arterial fibrillation, AKI and T2DM(Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Laboratory indices revealed significant trends across tertiles: platelet counts decreased (T1: 224.5\\u0026times;10⁹/L vs. T3: 133\\u0026times;10⁹/L, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), while LDH (T1: 215 U/L vs. T3: 435 U/L, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and creatinine (T1: 0.85 mg/dL vs. T3:1.9 mg/dL, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) progressively increased. Severity scores demonstrated analogous deterioration (SOFA: T1\\u0026thinsp;=\\u0026thinsp;4 vs. T3\\u0026thinsp;=\\u0026thinsp;8, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eBaseline characteristics stratified by EASIX tertiles\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eOverall(n\\u0026thinsp;=\\u0026thinsp;2468)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eT1(n\\u0026thinsp;=\\u0026thinsp;823)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eT2(n\\u0026thinsp;=\\u0026thinsp;822)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eT3(n\\u0026thinsp;=\\u0026thinsp;823)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e.value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge(years)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e73 (64\\u0026ndash;81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e72 (62\\u0026ndash;81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e74 (64\\u0026ndash;82)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e74 (66\\u0026ndash;82)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSex (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e836 (33.87)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e344 (41.80)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e249 (30.29)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e243 (29.53)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1632 (66.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e479 (58.20)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e573 (69.71)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e580 (70.47)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRace (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBlack\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e154 (6.24)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e42 (5.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e56 (6.81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e56 (6.80)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.578\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWhite\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2039 (82.62)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e691 (83.96)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e674 (82.00)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e674 (81.90)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOthers\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e275 (11.14)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e90 (10.94)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e92 (11.19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e93 (11.30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBMI(kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e27.682 (24.251\\u0026ndash;31.831)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e26.953 (23.959\\u0026ndash;31.211)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e27.948 (24.333\\u0026ndash;32.277)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e28.086 (24.566\\u0026ndash;32.19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.017\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eComorbidities\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRespiratory failure (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1137 (46.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e264 (32.08)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e388 (47.20)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e485 (58.93)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eArterial fibrillation (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1090 (44.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e311 (37.79)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e372 (45.26)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e407 (49.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHypertension (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e900 (36.47)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e414 (50.30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e276 (33.58)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e210 (25.52)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAKI (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1244 (50.41)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e182 (22.11)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e436 (53.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e626 (76.06)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStroke (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e249 (10.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e75 (9.11)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e91 (11.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e83 (10.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.420\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT2DM (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e963 (39.02)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e251 (30.50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e335 (40.75)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e377 (45.81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHeart failure (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1310 (53.08)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e323 (39.25)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e473 (57.54)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e514 (62.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMyocardial infarction (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e856 (34.68)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e211 (25.64)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e300 (36.50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e345 (41.92)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eillness severity scores\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSOFA\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6 (3\\u0026ndash;9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4 (2\\u0026ndash;6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5 (3\\u0026ndash;8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e8 (6\\u0026ndash;11)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAPSIII\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e47 (35\\u0026ndash;62)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e38 (29\\u0026ndash;49)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e46 (36\\u0026ndash;58)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e58 (47\\u0026ndash;74)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSAPSII\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e40 (32\\u0026ndash;50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e35 (28\\u0026ndash;43)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e39 (32\\u0026ndash;48)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e46 (38\\u0026ndash;57)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOASIS\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e34 (28\\u0026ndash;40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e32 (26\\u0026ndash;38)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e33 (28\\u0026ndash;39)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e36 (30\\u0026ndash;43)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLaboratory tests\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHemoglobin(g/dL)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10.4 (8.97-11.972)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e10.67 (9.19\\u0026ndash;12.19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e10.5 (9-12.022)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e10.1 (8.7\\u0026ndash;11.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRBC (\\u0026times;10\\u003csup\\u003e6\\u003c/sup\\u003e/\\u0026micro;L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.47 (3.03\\u0026ndash;4.02)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.57 (3.16\\u0026ndash;4.105)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.5 (3.04\\u0026ndash;4.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3.37 (2.89\\u0026ndash;3.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWBC (\\u0026times;10\\u003csup\\u003e3\\u003c/sup\\u003e/\\u0026micro;L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e11.4 (8.5\\u0026ndash;15.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11.05 (8.365\\u0026ndash;14.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e11.4 (8.57\\u0026ndash;15.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e11.8 (8.415\\u0026ndash;16.76)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eINR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.3 (1.17\\u0026ndash;1.57)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.2 (1.1\\u0026ndash;1.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.3 (1.18\\u0026ndash;1.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.41 (1.2\\u0026ndash;1.85)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePT(sec)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14.24 (12.8-17.135)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e13.5 (12.408\\u0026ndash;15.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14.2 (12.8-16.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e15.625 (13.45-20.148)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAPTT(sec)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e36 (29.26\\u0026ndash;54.83)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e33.5 (28.315\\u0026ndash;49.125)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e36 (29.162\\u0026ndash;53.63)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e39.225 (30.855\\u0026ndash;62.455)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFBG (mg/dL)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e135.33 (112\\u0026ndash;176)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e125 (107\\u0026ndash;151)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e138 (113.5-177.917)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e147.415 (118.082-196.375)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePotassium (mEq/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.17 (3.87\\u0026ndash;4.53)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.07 (3.83\\u0026ndash;4.378)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.18 (3.9-4.545)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.3 (3.95\\u0026ndash;4.73)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSodium (mEq/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e138.5 (135.67\\u0026ndash;141)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e138.5 (136-140.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e138.5 (136\\u0026ndash;141)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e138.25 (135-141.225)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.976\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSerum creatinine(mg/dL)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.2 (0.87\\u0026ndash;1.77)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.85 (0.7\\u0026ndash;1.03)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.2 (0.952-1.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.9 (1.33\\u0026ndash;2.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEASIX\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.129 (0.206\\u0026ndash;2.089)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e-0.115 (-0.57-0.205)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.129 (0.843\\u0026ndash;1.393)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2.486 (2.089\\u0026ndash;3.142)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLDH(U/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e296 (219\\u0026ndash;435)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e215 (177\\u0026ndash;272)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e300.75 (239.125\\u0026ndash;407.75)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e435 (313.5-618.335)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePlatelet(\\u0026times;10⁹/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e180 (133\\u0026ndash;237)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e224.5 (174.75-287.835)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e180.67 (147-229.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e133 (93.855-182.285)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTherapeutic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eInvasive ventilation (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2212 (89.63)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e722 (87.73)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e739 (89.90)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e751 (91.25)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.061\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAntihypertensive drug(%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2107 (85.37)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e692 (84.08)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e712 (86.62)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e703 (85.42)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.347\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlucocorticoid(%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e647 (26.22)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e161 (19.56)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e217 (26.40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e269 (32.69)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEvents\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e30-day mortality (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e518 (20.99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e86 (10.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e145 (17.64)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e287 (34.87)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e90-day mortality (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e559 (22.65)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e96 (11.66)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e161 (19.59)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e302 (36.70)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e365-day mortality (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e592 (23.99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e100 (12.15)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e172 (20.92)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e320 (38.88)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eData are presented as median (IQR) or frequencies (percentages). Abbreviation: EASIX, Endothelial Activation and Stress Index; BMI, body mass index; AKI, acute kidney injury; T2DM, type 2 diabetes mellitus; RBC, red blood cell; WBC, white blood cells; INR, international normalized ratio; PT, prothrombin time; APTT, activated partial thromboplastin time; FBG, fasting blood glucose; LDH, lactate dehydrogenase; SOFA, sequential organ failure assessment; SAPS II, simplified acute physiological score II; APS III, acute physiology score III; OASIS, oxford acute severity of illness score.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eComparisons between survivors and non-survivors based on 30-day outcomes\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCharacteristics\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eOverall (n\\u0026thinsp;=\\u0026thinsp;2468)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eSurvivors\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;1950)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eNon-survivors\\u003c/p\\u003e\\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;518)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e.value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e73 (64\\u0026ndash;81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e72 (63\\u0026ndash;81)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e76 (68\\u0026ndash;82)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSex (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFemale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e836 (33.87)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e646 (33.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e190 (36.68)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.143\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMale\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1632 (66.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1304 (66.87)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e328 (63.32)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRace (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBlack\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e154 (6.24)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e118 (6.05)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e36 (6.95)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.258\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWhite\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2039 (82.62)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1605 (82.31)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e434 (83.78)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOthers\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e275 (11.14)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e227 (11.64)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e48 (9.27)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBMI\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e27.68(24.25\\u0026ndash;31.83)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e27.8 (24.36\\u0026ndash;31.80)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e27.191 (23.88\\u0026ndash;31.98)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.056\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eComorbidities\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRespiratory failure (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1137 (46.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e773 (39.64)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e364 (70.27)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eArterial fibrillation (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1090 (44.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e826 (42.36)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e264 (50.97)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHypertension (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e900 (36.47)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e741 (38.00)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e159 (30.69)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.003\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAKI (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1244 (50.41)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e868 (44.51)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e376 (72.59)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStroke (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e249 (10.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e202 (10.36)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e47 (9.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.435\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT2DM (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e963 (39.02)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e746 (38.26)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e217 (41.89)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.145\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHeart failure (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1310 (53.08)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1004 (51.49)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e306 (59.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.002\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMyocardial infarction (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e856 (34.68)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e678 (34.77)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e178 (34.36)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.904\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eillness severity scores\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSOFA\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6 (3\\u0026ndash;9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5 (3\\u0026ndash;8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e8 (5\\u0026ndash;11)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAPSIII\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e47 (35\\u0026ndash;62)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e43 (33\\u0026ndash;56)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e64 (50\\u0026ndash;79)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSAPSII\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e40 (32\\u0026ndash;50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e37 (31\\u0026ndash;47)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e48 (40\\u0026ndash;60)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOASIS\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e34 (28\\u0026ndash;40)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e32 (27-38.75)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e39 (32\\u0026ndash;46)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLaboratory tests\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHemoglobin(g/dL)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e10.4 (8.97\\u0026ndash;11.97)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e10.53 (9.1\\u0026ndash;12.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e9.91 (8.535\\u0026ndash;11.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRBC (\\u0026times;10\\u003csup\\u003e6\\u003c/sup\\u003e/\\u0026micro;L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.47 (3.03\\u0026ndash;4.02)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.51 (3.08\\u0026ndash;4.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.34 (2.87\\u0026ndash;3.88)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWBC (\\u0026times;10\\u003csup\\u003e3\\u003c/sup\\u003e/\\u0026micro;L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e11.4 (8.5\\u0026ndash;15.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e11.15 (8.4\\u0026ndash;15.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e12.435 (8.90-17.57)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eINR\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.3 (1.17\\u0026ndash;1.57)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.275 (1.15\\u0026ndash;1.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.45 (1.2\\u0026ndash;1.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePT(sec)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14.24 (12.8-17.135)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e14.03 (12.7\\u0026ndash;16.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e15.95 (13.5\\u0026ndash;20.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAPTT(sec)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e36 (29.26\\u0026ndash;54.83)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e35.15 (28.96\\u0026ndash;53.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e38.7 (30.39\\u0026ndash;62.56)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFBG (mg/dL)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e135.33 (112\\u0026ndash;176)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e132 (111\\u0026ndash;170)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e148.415 (119.62-193.63)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePotassium (mEq/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.17 (3.87\\u0026ndash;4.53)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.15 (3.87\\u0026ndash;4.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.2 (3.87\\u0026ndash;4.65)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.004\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSodium (mEq/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e138.5 (135.67\\u0026ndash;141)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e138.5 (136\\u0026ndash;141)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e138.33 (134.8-141.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.311\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSerum creatinine(mg/dL)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.2 (0.87\\u0026ndash;1.77)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.1 (0.85\\u0026ndash;1.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.57 (1.08\\u0026ndash;2.37)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEASIX\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.129 (0.21\\u0026ndash;2.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.927 (0.09\\u0026ndash;1.83)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.962 (0.96\\u0026ndash;2.84)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLDH(U/L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e296 (219\\u0026ndash;435)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e280.5 (207-396.75)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e391 (274.25-566.75)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003ePlatelet(\\u0026times;10\\u003csup\\u003e3\\u003c/sup\\u003e/\\u0026micro;L)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e180 (133\\u0026ndash;237)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e181 (136.25-236.38)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e173.34 (122-238.94)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.123\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTherapeutic\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eInvasive ventilation (%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2212 (89.63)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1744 (89.44)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e468 (90.35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.600\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAntihypertensive drugs(%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2107 (85.37)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1695 (86.92)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e412 (79.54)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eGlucocorticoid(%)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e647 (26.22)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e448 (22.97)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e199 (38.42)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eData are presented as median (IQR) or frequencies (percentages). Abbreviation: EASIX, Endothelial Activation and Stress Index; BMI, body mass index; AKI, acute kidney injury; T2DM, type 2 diabetes mellitus; RBC, red blood cell; WBC, white blood cells; INR, international normalized ratio; PT, prothrombin time; APTT, activated partial thromboplastin time; FBG, fasting blood glucose; LDH, lactate dehydrogenase; SOFA, sequential organ failure assessment; SAPS II, simplified acute physiological score II; APS III, acute physiology score III; OASIS, oxford acute severity of illness score.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eEASIX and Mortality Associations\\u003c/h2\\u003e\\u003cp\\u003eNon-survivors exhibited significantly higher EASIX scores compared to survivors [1.96 (0.96\\u0026ndash;2.84) vs. 0.93 (0.09\\u0026ndash;1.83), p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001] based on 30-day outcomes (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Mortality analyses (Tables S1-S2) revealed that non-survivors had consistently higher EASIX values than survivors at 90-day and 365-day outcomes. The Boruta algorithm identified EASIX as a \\\"Confirmed\\\" predictor of 30-day mortality, with importance Z-score ranking third after APSIII and SAPS II (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eKaplan-Meier analysis revealed significant mortality gradients across EASIX tertiles for both 30-day and 365-day endpoints (log-rank p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Patients in the highest EASIX tertile exhibited significantly elevated 30-day mortality (34.87% vs. 10.45%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and 365-day mortality (38.88% vs. 12.15%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) compared to the lowest tertile. Similar findings were observed for 90-day mortality(fig.\\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eCox regression demonstrated consistent associations across all models (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). In the fully-adjusted Model 3, each log₂-unit increase in EASIX conferred an 52.2% higher mortality risk at 30days (HR: 1.522; 95% CI: 1.346\\u0026ndash;1.72; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and 47.7% higher mortality risk at 365 days (HR: 1.477; 95% CI: 1.315\\u0026ndash;1.653; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Compared to T1, T3 patients exhibited a 2.237-fold elevated mortality risk at 30 days (HR: 2.237; 95% CI: 1.534\\u0026ndash;3.262; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and 2.204-fold elevated mortality risk at 365 days (HR: 2.204; 95% CI: 1.553\\u0026ndash;3.129; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) after multivariable adjustment. These patterns remained consistent at the intermediate 90-day time point, confirming the robust prognostic value of EASIX.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eCox proportional hazards analysis for in-hospital mortality\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eEvents (%)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eModel 1\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e\\u003cp\\u003eModel 2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u003cp\\u003eModel 3\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eHR (95% CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eP value\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eHR (95% CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eP value\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003eHR (95% CI)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003eP value\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"8\\\" nameend=\\\"c8\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e30-day mortality\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eContinuous variable per unit\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e518(20.99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.523(1.436\\u0026ndash;1.614)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.54(1.451\\u0026ndash;1.635)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.522(1.346\\u0026ndash;1.72)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT1 (n\\u0026thinsp;=\\u0026thinsp;823)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e86 (3.48)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT2 (n\\u0026thinsp;=\\u0026thinsp;822)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e145 (5.88)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.778 (1.362\\u0026ndash;2.322)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.786(1.365\\u0026ndash;2.337)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.426(1.043\\u0026ndash;1.952)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.026\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT3(n\\u0026thinsp;=\\u0026thinsp;823)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e287 (11\\u003c/p\\u003e\\u003cp\\u003e.63)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.911 (3.073\\u0026ndash;4.977)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3.95(3.095\\u0026ndash;5.04)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2.237(1.534\\u0026ndash;3.262)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eP for trend\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"8\\\" nameend=\\\"c8\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e90-day mortality\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eContinuous variable per unit\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e559(22.65)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.503(1.420\\u0026ndash;1.591)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.519(1.434\\u0026ndash;1.61)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.499(1.333\\u0026ndash;1.686)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT1 (n\\u0026thinsp;=\\u0026thinsp;823)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e96 (3.89)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT2 (n\\u0026thinsp;=\\u0026thinsp;822)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e161 (6.52)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.774 (1.378\\u0026ndash;2.284)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.79 (1.387\\u0026ndash;2.308)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.426 (1.059\\u0026ndash;1.919)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.019\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT3(n\\u0026thinsp;=\\u0026thinsp;823)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e302 (12.24)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.732 (2.966\\u0026ndash;4.696)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3.782 (2.998\\u0026ndash;4.772)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2.162 (1.509\\u0026ndash;3.098)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eP for trend\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"8\\\" nameend=\\\"c8\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e365-day mortality\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eContinuous variable per unit\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e592(23.99)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.512(1.431\\u0026ndash;1.597)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.526(1.443\\u0026ndash;1.614)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.474(1.315\\u0026ndash;1.653)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT1 (n\\u0026thinsp;=\\u0026thinsp;823)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e96 (3.89)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.00\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT2 (n\\u0026thinsp;=\\u0026thinsp;822)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e161 (6.52)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.824(1.426\\u0026ndash;2.334)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.844(1.439\\u0026ndash;2.364)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e1.465(1.097\\u0026ndash;1.958)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eT3(n\\u0026thinsp;=\\u0026thinsp;823)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e302 (12.24)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.836(3.064\\u0026ndash;4.802)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3.891(3.101\\u0026ndash;4.883)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u003cp\\u003e2.204(1.553\\u0026ndash;3.129)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eP for trend\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eModel 1: unadjusted crude model. Model 2: adjusted for sex, age, race, BMI. Model 3: adjusted for factors in Model 2 and respiratory failure, arterial fibrillation, hypertension, AKI, stroke, hypertension, T2DM, heart failure, myocardial infarction, hemoglobin, RBC, WBC, PT, APTT, FBG, potassium, sodium, serum creatinine, platelet, SOFA, SAPS II, APS III, OASIS, invasive ventilation, antihypertensive drug, glucocorticoid.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eDose-Response Relationship\\u003c/h3\\u003e\\n\\u003cp\\u003eUsing Cox model 3(adjusted for sex, age, race,BMI, respiratory failure, arterial fibrillation, hypertension, AKI, stroke, hypertension, T2DM, heart failure, myocardial infarction, hemoglobin, RBC, WBC, PT, APTT, FBG, potassium, sodium, serum creatinine, platelet, SOFA, SAPS II, APS III, OASIS, invasive ventilation, antihypertensive drug, glucocorticoid), RCS regression confirmed a linear dose\\u0026ndash;response relationship between EASIX and 30-day as well as 365-day all-cause mortality (p for non-linear\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05)(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). A consistent pattern was observed at the intermediate 90-day time point (Fig S2).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eFigure\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. \\u003cb\\u003eRestricted cubic spline analysis of EASIX and 30-day and 365-day all-cause mortality in critically ill CHD based on Cox proportional hazards model 3\\u003c/b\\u003e. (A)30-day all-cause mortality. (B)365-day all-cause mortality. Cox proportional hazards model 3:adjusted for sex, age, race,BMI, respiratory failure, arterial fibrillation, hypertension, AKI, stroke, hypertension, T2DM, heart failure, myocardial infarction, hemoglobin, RBC, WBC, PT, APTT, FBG, potassium, sodium, serum creatinine, platelet, SOFA, SAPS II, APS III, OASIS, invasive ventilation, antihypertensive drug, glucocorticoid.\\u003c/p\\u003e\\n\\u003ch3\\u003eSubgroup Analyses\\u003c/h3\\u003e\\n\\u003cp\\u003eWe performed stratified analyses to examine the consistency between EASIX and 30-day and 365-day mortality association across key clinical subgroups defined by age (\\u0026lt;\\u0026thinsp;65 vs. \\u0026ge;65 years), sex (female vs. male), BMI (\\u0026lt;\\u0026thinsp;25 vs. \\u0026ge;25 kg/m\\u0026sup2;), atrial fibrillation, stroke, T2DM and heart failure. Using the fully adjusted model 3, we assessed these relationships at 30 and 365 days post-CHD, with T1 serving as the reference category. P for interaction values were computed to assess effect modification across strata. Overall, a consistent dose-dependent relationship was observed, with progressively elevated mortality risk from T2 to T3 across most subgroups. Notably, BMI demonstrated a statistically significant interaction with EASIX for both 30-day (p\\u0026thinsp;=\\u0026thinsp;0.028) and 365-day mortality (p\\u0026thinsp;=\\u0026thinsp;0.042), whereas no significant interactions were detected for other variables (all p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05), suggesting the prognostic utility of EASIX is largely independent of these clinical characteristics, except for potential effect modification by BMI status.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study systematically evaluated the predictive value of the EASIX for short-term and long-term outcomes in critically ill patients with CHD admitted to ICU for the first time, based on an analysis of 2,469 patients from the MIMIC-IV database. The main findings showed that an EASIX calculated within 24 hours of admission was an independent predictor of all-cause mortality at 30, 90, and 365 days. Patients in the highest tertile had an approximately 2.2-fold higher mortality risk than those in the lowest tertile, and this association remained robust after full adjustment for multidimensional confounding factors. Further analysis using restricted cubic splines revealed an approximately linear dose-response relationship between EASIX and mortality. The Boruta algorithm identified EASIX as the third most important variable for predicting 30-day mortality, ranking behind only the APS III and the SAPS II. These results support the use of EASIX as a composite biomarker reflecting endothelial injury and systemic stress status, with potential clinical utility in the risk stratification of critically ill CHD patients.\\u003c/p\\u003e\\u003cp\\u003eEASIX was initially developed by Imahorn et al[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]for predicting transplant-associated thrombotic microangiopathy and mortality in patients undergoing hematopoietic stem cell transplantation (HSCT). Recent studies have extended its application to disease states centered on endothelial dysfunction, such as COVID-19[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e], sepsis[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e], and hypertensive emergency[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. EASIX assesses the severity of endothelial dysfunction by integrating indicators reflecting renal function (creatinine), cellular damage (LDH), and coagulation/inflammatory status (platelets). Endothelial activation is a core mechanism in many pathological processes, such as transplant complications, infection, and immunotherapy toxicity. Elevated EASIX levels indicate endothelial barrier disruption and an increased risk of microthrombus formation[\\u003cspan additionalcitationids=\\\"CR20\\\" citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. The present study is the first to apply EASIX to the population of CHD patients in ICUs, and the results are consistent with those of the aforementioned studies[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Notably, although the pathophysiological mechanisms of CHD involve multiple processes including atherosclerotic plaque formation, myocardial ischemia, and thrombotic complications, endothelial dysfunction remains a key link in initiating and sustaining disease progression[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. In CHD, progressive endothelial cell stress may promote plaque instability, microvascular obstruction, and amplification of systemic inflammatory responses, ultimately leading to multiple organ failure [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. The vicious cycle formed by the inflammatory cascade and oxidative damage can accelerate the progression of coronary artery lesions[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. By integrating three indicators\\u0026mdash;LDH, creatinine, and platelet count\\u0026mdash;EASIX is precisely able to capture the core elements of this pathological process: elevated LDH indicates cell death and tissue hypoperfusion, serum creatinine levels reflect the integrity of renal microcirculation, and decreased platelet count suggests consumptive coagulopathy and activation of endothelial-platelet interactions[\\u003cspan additionalcitationids=\\\"CR28 CR29\\\" citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eIn the present study, patients in the highest EASIX tertile exhibited a typical \\\"endothelial injury phenotype\\\": platelet count was 40.7% lower than that in the lowest tertile (133 vs. 224.5 \\u0026times; 10⁹/L), LDH level was 102.3% higher (435 vs. 215 U/L), creatinine level doubled (1.9 vs. 0.85 mg/dL), accompanied by significant deterioration in SOFA score, and the prevalence of AKI and respiratory failure. This pattern of synchronous multisystem dysregulation is highly consistent with the pathophysiological characteristics of systemic endothelial dysfunction in critically ill CHD patients[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. The Boruta algorithm ranked EASIX as more important than traditional laboratory indicators (e.g., creatinine, WBC) and age, suggesting that as a composite index, it may capture systemic pathological information that cannot be reflected by individual variables. Compared with traditional scoring systems, EASIX only requires three routine laboratory tests for calculation, offering convenient clinical access and significant cost-effectiveness, which is particularly important in resource-limited medical settings.\\u003c/p\\u003e\\u003cp\\u003eThe findings of this study offer multiple implications for the clinical management of critically ill CHD patients. Firstly, EASIX can serve as an early risk identification tool. The 30-day mortality rate of patients in the highest tertile (EASIX\\u0026thinsp;\\u0026gt;\\u0026thinsp;2.089) reached 34.87%, compared with only 10.45% in the lowest tertile. This significant difference suggests that clinicians can identify ultra-high-risk subgroups within 24 hours of ICU admission, thereby triggering more aggressive monitoring and intervention strategies, such as enhanced hemodynamic support, optimization of oxygen supply-demand balance, and early initiation of renal replacement therapy. Secondly, the existence of a linear dose-response relationship indicates that EASIX can not only be used for binary risk stratification but also as a continuous variable for dynamic risk assessment, providing a quantitative basis for adjusting the intensity of individualized treatment. Thirdly, subgroup analysis revealed that the prognostic value of EASIX was more pronounced in patients with BMI\\u0026thinsp;\\u0026lt;\\u0026thinsp;25 kg/m\\u0026sup2;. This finding may be related to insufficient nutritional reserves and more severe inflammatory responses in underweight patients, suggesting that EASIX has stronger discriminative power in vulnerable populations and facilitates the realization of precision medicine.\\u003c/p\\u003e\\u003cp\\u003eFrom the perspective of research tools, EASIX can serve as a valid covariate for patient stratification in clinical trials. In randomized controlled trials (RCTs) evaluating novel anti-inflammatory, antithrombotic, or endothelial protective therapies, incorporating EASIX as a stratification factor can balance baseline risks between groups and enhance statistical power. Furthermore, given its core attribute of reflecting endothelial function, EASIX may potentially be used to monitor the efficacy of targeted therapies for endothelial injury in the future, although this application requires validation by prospective studies.\\u003c/p\\u003e\\u003cp\\u003eThis study has several methodological strengths. Firstly, it features a large and well-defined sample size. As a high-quality ICU cohort, the MIMIC-IV database provides abundant clinical variables and long-term follow-up data, enhancing statistical power. Secondly, the study covers comprehensive time points by evaluating outcomes at 30 days, 90 days, and 365 days, confirming that the predictive value of EASIX is temporally stable\\u0026mdash;suitable for both short-term prognosis assessment and long-term risk prediction. Thirdly, the statistical analysis methods are rigorous: a multilevel Cox proportional hazards model was used to sequentially adjust for confounding factors, ranging from demographic characteristics to comprehensive laboratory indicators and disease severity scores, ensuring the robustness of the results; restricted cubic spline analysis objectively verified the linear relationship, avoiding selection bias from artificial cutoffs; the Boruta algorithm, based on the principle of random forests, ranked variable importance, reducing the false-positive risk associated with traditional univariate analysis. Fourthly, subgroup analyses covered key clinical characteristics, and interaction tests revealed no significant heterogeneity (except for BMI), indicating that EASIX has broad applicability and is not significantly influenced by factors such as age, gender, or comorbidities.\\u003c/p\\u003e\\u003cp\\u003eThe limitations of this study should be fully considered when interpreting the results. Firstly, the retrospective design inherently limits causal inference. Despite comprehensive adjustments, residual confounding from unmeasured variables (e.g., severity of coronary artery lesions, specific treatment strategies, timing of infection source control) cannot be completely ruled out. Prospective cohort studies are necessary to validate causal relationships. Secondly, although the MIMIC-IV database is large-scale, the 2,469 patients ultimately included in this analysis accounted for only 10.7% of the initially screened population, primarily due to missing key variables in 16,206 patients. This selective inclusion may introduce survival bias and overestimate the predictive performance of EASIX. Since LDH is not a routine test in clinical practice, future studies need to evaluate its generalizability in cohorts with complete data. Thirdly, the single-center data source (Beth Israel Deaconess Medical Center, Boston, USA) restricts the external validity of the results. Differences in clinical practices across regions, ethnicities, and healthcare systems may affect the optimal cutoff value and predictive efficacy of EASIX. Fourthly, this study only used baseline EASIX values calculated within 24 hours of ICU admission and did not assess its dynamic changes. Endothelial injury is a dynamic process, and serial monitoring of EASIX may provide more abundant time-dependent prognostic information. Fifthly, although outliers were excluded, EASIX is calculated as the product of three indicators, meaning measurement errors in any single parameter may be amplified. The potential impact of extreme values on the results requires attention.\\u003c/p\\u003e\\u003cp\\u003eFuture research should focus on the following directions: (1) Conduct multicenter, prospective cohort studies to validate the predictive performance of EASIX across diverse healthcare settings and ethnic populations, and explore ethnicity-specific cutoff values; (2) Design nested studies to assess the incremental predictive value of dynamic changes in EASIX (e.g., daily monitoring) for prognosis, as well as whether a decrease in EASIX following therapeutic interventions is associated with improved survival; (3) Further investigate the biological mechanisms underlying the interaction between BMI and EASIX, particularly the regulatory roles of muscle mass, adipokines, and inflammatory status; (4) Evaluate in randomized controlled trials whether EASIX-guided treatment strategies (e.g., intensified antithrombotic, anti-inflammatory, or endothelial protective therapy) can improve clinical outcomes, and validate its potential as a therapeutic target; (5) Explore the combined application of EASIX with other emerging biomarkers (e.g., soluble thrombomodulin, von Willebrand factor), to construct more accurate prognostic models related to endothelial injury.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study confirms that EASIX is an independent predictor of short-term and long-term mortality in CHD patients admitted to ICUs. Its predictive performance is robust, with an approximately linear dose-response relationship with mortality. As a simple and cost-effective composite biomarker, EASIX can effectively capture the systemic pathological status characterized by endothelial injury, microcirculatory disorders, and coagulation dysfunction, providing a novel tool for early clinical risk stratification. Despite limitations such as the retrospective design and selection bias, the current evidence sufficiently supports the routine monitoring of EASIX in critically ill CHD patients. Future prospective studies are needed to further validate its clinical utility and explore its potential value as an indicator for monitoring treatment responses and a therapeutic target.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eCHD \\u0026nbsp;Coronary heart disease\\u003c/p\\u003e\\n\\u003cp\\u003eICU \\u0026nbsp;intensive care unit\\u003c/p\\u003e\\n\\u003cp\\u003eEASIX \\u0026nbsp;Endothelial Activation and Stress Index\\u003c/p\\u003e\\n\\u003cp\\u003eMIMIC-IV \\u0026nbsp; Medical Information Mart for Intensive Care IV\\u003c/p\\u003e\\n\\u003cp\\u003eHR \\u0026nbsp; hazard ratio\\u003c/p\\u003e\\n\\u003cp\\u003eCI \\u0026nbsp;confidence interval\\u003c/p\\u003e\\n\\u003cp\\u003eLDH \\u0026nbsp;lactate dehydrogenase\\u003c/p\\u003e\\n\\u003cp\\u003eIQR \\u0026nbsp;interquartile range\\u003c/p\\u003e\\n\\u003cp\\u003eRCS \\u0026nbsp;restricted cubic spline\\u003c/p\\u003e\\n\\u003cp\\u003eBMI \\u0026nbsp;body mass index\\u003c/p\\u003e\\n\\u003cp\\u003eAKI \\u0026nbsp;acute kidney injury\\u003c/p\\u003e\\n\\u003cp\\u003eT2DM \\u0026nbsp;type 2 diabetes mellitus\\u003c/p\\u003e\\n\\u003cp\\u003eWBC \\u0026nbsp;white blood cells\\u003c/p\\u003e\\n\\u003cp\\u003eRBC \\u0026nbsp;red blood cells\\u003c/p\\u003e\\n\\u003cp\\u003eINR \\u0026nbsp;international normalized ratio\\u003c/p\\u003e\\n\\u003cp\\u003ePT \\u0026nbsp;prothrombin time\\u003c/p\\u003e\\n\\u003cp\\u003eAPTT \\u0026nbsp;activated partial thromboplastin time\\u003c/p\\u003e\\n\\u003cp\\u003eFBG \\u0026nbsp; fasting blood glucose\\u003c/p\\u003e\\n\\u003cp\\u003eSOFA \\u0026nbsp;Sequential Organ Failure Assessment\\u003c/p\\u003e\\n\\u003cp\\u003eAPSIII \\u0026nbsp;Acute Physiology ScoreIII (APSIII)\\u003c/p\\u003e\\n\\u003cp\\u003eSAPS II \\u0026nbsp; simplified acute physiological score II\\u003c/p\\u003e\\n\\u003cp\\u003eOASIS \\u0026nbsp;oxford acute severity of illness score\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSi Guo designed and conceptualized this study, Shuyang Dai analyzed the data and wrote the manuscript, Bingjie Li drew the images, and Zongshan Zhang and Tingting Wang checked the manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated during and/or analyzed during the current study are available in the MIMIC-IV database, https://physionet.org/content/mimiciv/3.0.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe requirement of ethical approval for this was waived by the Institutional Review Board of Fuwai Central China Cardiovascular Hospital, because the data was accessed from MIMIC-IV (a publicly available database). The need for written informed consent was waived by the Institutional Review Board of Fuwai Central China Cardiovascular Hospital due to retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.Publisher\\u0026rsquo;s note.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990\\u0026ndash;2019: Update From the GBD 2019 Study. 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J Clin Med. 2021;10:4373. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/jcm10194373\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/jcm10194373\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKalicińska E, Biernat M, Rybka J, Zińczuk A, Janocha-Litwin J, Rosiek-Biegus M, et al. Endothelial Activation and Stress Index (EASIX) as an Early Predictor for Mortality and Overall Survival in Hematological and Non-Hematological Patients with COVID-19: Multicenter Cohort Study. 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Association Between Endothelial Activation and Stress Index and 28-Day Mortality in Septic ICU patients: a Retrospective Cohort Study. Int J Med Sci. 2023;20:1165\\u0026ndash;73. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.7150/ijms.85870\\u003c/span\\u003e\\u003cspan address=\\\"10.7150/ijms.85870\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eTeam DC. DecisionLinnc is a platform that integrates multiple programming language environments and enables data processing, data analysis, and machine learning through a visual interface. 2023.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLuft T, Benner A, Jodele S, Dandoy CE, Storb R, Gooley T, et al. EASIX in patients with acute graft-versus-host disease: a retrospective cohort analysis. 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CRP and ferritin in addition to the EASIX score predict CAR-T-related toxicity. Blood Adv. 2021;5:2799\\u0026ndash;806. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1182/bloodadvances.2021004575\\u003c/span\\u003e\\u003cspan address=\\\"10.1182/bloodadvances.2021004575\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePe\\u0026ntilde;a M, Salas MQ, Mussetti A, Moreno-Gonzalez G, Bosch A, Pati\\u0026ntilde;o B, et al. Pretransplantation EASIX predicts intensive care unit admission in allogeneic hematopoietic cell transplantation. Blood Adv. 2021;5:3418\\u0026ndash;26. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1182/bloodadvances.2021004812\\u003c/span\\u003e\\u003cspan address=\\\"10.1182/bloodadvances.2021004812\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePennisi M, Sanchez-Escamilla M, Flynn JR, Shouval R, Alarcon Tomas A, Silverberg ML, et al. Modified EASIX predicts severe cytokine release syndrome and neurotoxicity after chimeric antigen receptor T cells. Blood Adv. 2021;5:3397\\u0026ndash;406. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1182/bloodadvances.2020003885\\u003c/span\\u003e\\u003cspan address=\\\"10.1182/bloodadvances.2020003885\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eReventun P, S\\u0026aacute;nchez-Esteban S, Cook-Calvete A, Delgado-Mar\\u0026iacute;n M, Roza C, Jorquera-Ortega S, et al. Endothelial ILK induces cardioprotection by preventing coronary microvascular dysfunction and endothelial-to-mesenchymal transition. Basic Res Cardiol. 2023;118:28. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s00395-023-00997-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s00395-023-00997-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Coronary heart disease, Intensive care unit, Endothelial Activation and Stress Index, Mortality, Predictor\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8191194/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8191194/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground: \\u003c/strong\\u003eCoronary heart disease (CHD) is a major cause of mortality in critically ill patients, with endothelial dysfunction playing a pivotal role in disease progression. The Endothelial Activation and Stress Index (EASIX), a composite biomarker reflecting endothelial injury and systemic stress, has demonstrated prognostic value across various cardiovascular conditions. Nevertheless, the association of this phenomenon with mortality in CHD patients requiring intensive care remains to be elucidated.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods \\u003c/strong\\u003eA retrospective cohort study was conducted using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The study encompassed a total of 2,469 critically ill CHD patients, who were stratified into tertiles based on admission EASIX scores. The application of Cox proportional hazard models and restricted cubic spline regression was utilised for the purpose of evaluating the association between EASIX and all-cause mortality within a 30-day, 90-day and 365-day timeframe. Subgroup analyses were performed in order to assess potential effect modifications.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e The median age of the cohort was found to be 73 years (interquartile range: 64–81 years), with 66.13% of subjects being male (1632/2468). Patients in the highest EASIX tertile exhibited significantly elevated 30-day mortality (34.87% vs. 10.45%, p\\u0026lt;0.001) and 365-day mortality (38.88% vs. 12.15%, p\\u0026lt;0.001) compared to the lowest tertile. Cox regression analysis revealed EASIX to be an independent predictor of 30-day mortality (adjusted hazard ratio [HR]: 2.237; 95% confidence interval [CI]: 1.534-3.262; p \\u0026lt; 0.001) and 365-day mortality (adjusted HR: 2.204; 95% CI: 1.553-3.129; p \\u0026lt; 0.001) following comprehensive adjustment for confounders. The Kaplan-Meier analysis demonstrated a significantly inferior survival probability in the highest EASIX stratum (log-rank p\\u0026lt;0.0001). The restricted cubic spline analysis indicated a near-linear dose-response relationship between EASIX and mortality risk (p for non-linearity \\u0026lt; 0.05).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion \\u003c/strong\\u003eElevated EASIX scores have been shown to independently correlate with increased short and long-term mortality in critically ill CHD patients, suggesting its utility as a novel prognostic biomarker for risk stratification in this high-risk population. Further prospective validation and investigation of therapeutic implications are warranted.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical trial number\\u003c/strong\\u003e \\u0026nbsp;Not applicable.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Association between Endothelial Activation and Stress Index and mortality in coronary heart disease ICU patients: A retrospective cohort study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-12 08:32:58\",\"doi\":\"10.21203/rs.3.rs-8191194/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"782d5509-3ee8-4774-a204-01aece382480\",\"owner\":[],\"postedDate\":\"December 12th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-04-15T10:04:12+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-12 08:32:58\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8191194\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8191194\",\"identity\":\"rs-8191194\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}