Association between C-reactive protein-triglyceride glucose index (CTI) and short-term mortality in critically ill patients with sepsis: a prospective cohort study

Association between C-reactive protein-triglyceride glucose index (CTI) and short-term mortality in critically ill patients with sepsis: a prospective 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 Article Association between C-reactive protein-triglyceride glucose index (CTI) and short-term mortality in critically ill patients with sepsis: a prospective cohort study Qingjiang Cai, Yuanyuan Qin, Biheng Feng, Mingjie Xie, Liuyun Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7299325/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 This study aimed to investigate the association between the C-reactive protein-triglyceride-glucose index (CTI) and the risk of in-hospital mortality, 60-day mortality, and 90-day mortality in critically ill patients with sepsis. Methods This was a retrospective cohort study using data from the Medical Intensive Care Unit Marketplace IV (MIMIC IV 3.1) database of patients with sepsis. Participants were divided into four groups based on the quartiles of the CTI index. Multivariate Cox regression was used to assess the association between CTI and mortality, and Restricted Cubic Spline (RCS) analysis was employed to evaluate the dose-response relationship between the CTI index and short-term mortality risk in sepsis patients; Subgroup analysis was conducted using stratified comparisons and interaction tests to assess the consistency of the association between CTI and mortality across different subgroups; the Boruta algorithm was applied to assess the importance of CTI. Kaplan-Meier (KM) curves were used to assess the cumulative survival probability of patients in different CTI groups. In the KM curves, the Log-rank test was used to compare differences between groups (mortality vs. survival). Results A total of 3,693 patients were included. The in-hospital mortality rate, 60-day mortality rate, and 90-day mortality rate were 17.5%, 21.6%, and 23.8%, respectively. In the multivariate Cox regression analysis, when CTI was treated as a continuous variable, each unit increase in CTI was associated with a 23% increase in mortality risk in a model fully adjusted for confounding factors. Additionally, trend tests indicated that the risk of in-hospital mortality, 60-day mortality, and 90-day mortality increased with higher quartiles of the CTI index. RCS analysis confirmed a linear relationship between CTI and the risk of in-hospital, 60-day, and 90-day mortality. Based on subgroup analysis results, in the fully adjusted model, in the majority of the included subgroups, an increase in CTI index was positively associated with an increased risk of in-hospital, 60-day, and 90-day mortality (HR > 1), and this association remained consistent in direction after multivariable adjustment. Notably, no significant interactions were observed (all interaction P values > 0.05). Survival curves also confirmed that patients in the low CTI level group had significantly higher cumulative survival probabilities at 60 days and 90 days compared to those in the high CTI level group. Additionally, the survival probability of critically ill sepsis patients gradually deteriorated from low to high CTI levels. Furthermore, the Boruta algorithm validated that CTI is a key indicator of outcomes in sepsis patients. Conclusion This study confirmed that CTI is linearly associated with in-hospital mortality, 60-day mortality, and 90-day mortality in sepsis patients. Therefore, dynamic monitoring of CTI levels and timely intervention in sepsis patients may be an effective clinical strategy to reduce short-term mortality in sepsis patients. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Sepsis C-reactive protein-triglyceride-glucose index Inflammation Insulin resistance MIMIC-IV Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection[ 1 ]. Although the mortality rate of sepsis has been declining with advancements in medical technology, sepsis remains one of the leading causes of global health issues [ 2 ]. Therefore, early identification of sepsis-related outcome indicators is essential. The C-reactive protein-triglyceride-glucose index (CTI), developed by Ruan et al. [ 3 ], combines inflammation and insulin resistance (IR) to predict survival rates in cancer patients, confirming that CTI has good predictive ability for survival in cancer patients. In recent years, the triglyceride-glucose (TyG) index[ 4 ] has been proposed, and related studies [ 5 , 6 ] have found that TyG is closely associated with adverse outcomes in critically ill patients with ischemic stroke, cardiac arrest, and other conditions. Current research has also confirmed that TyG is associated with adverse outcomes in sepsis patients [ 7 ]. Currently, increasing evidence suggests that the C-reactive protein-triglyceride-glucose index (CTI) can serve as an indicator for comprehensively assessing the severity of IR and inflammation. CTI has demonstrated good predictive value in forecasting stroke incidence in hypertensive populations[ 8 ] and cancer mortality in the general population[ 9 ]. To date, there have been no specific studies evaluating the association between CTI and mortality in sepsis patients. Given that IR and inflammation are both closely associated with the development of sepsis, it is essential to further explore the relationship between this composite index and adverse outcomes in sepsis patients. This study aims to investigate the association between the CTI index and clinical outcomes in sepsis patients, providing evidence-based support for future interventions targeting CTI to improve short-term adverse outcomes in sepsis patients. 2 Methods 2.1 Study Design and Patients A retrospective analysis was conducted using the Medical Informatics Market for Critical Care Medicine-IV (MIMIC-IV 3.1) database to investigate the association between CTI and short-term mortality in sepsis patients. The database contains information on clearly defined and characterized patients admitted to the intensive care unit (ICU) at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2022. One author obtained access to the database and was responsible for data extraction (certification number 14326907). Since only third-party anonymous publicly available data were used, the Institutional Review Board at BIDMC approved the waiver of informed consent and the sharing of research resources. This study is a re-report of research conducted in accordance with the Declaration on the Use of Routinely Collected Health Data (RECORD). Screening of sepsis patients admitted to the ICU for the first time. Inclusion criteria were as follows: The study included sepsis patients meeting the definition of sepsis according to the Third International Consensus on Sepsis and Sepsis Shock (Sepsis-3), defined as a Sequential Organ Failure Assessment (SOFA) score ≥ 2, and suspected infection during ICU hospitalization[ 1 ]. Exclusion criteria were as follows: (1) patients not admitted to the ICU for the first time; (2) patients with a hospital stay of less than 1 day, or an ICU stay of less than 1 day or more than 100 days; (3) insufficient data (e.g., triglycerides, fasting blood glucose, CRP, etc.). Finally, the cohort consisted of 3,693 sepsis patients aged 18 years or older at baseline (Fig. 1 ). 2.2 Variable Extraction In this study, information was extracted using PostgresSQL software (version 15) and Navicat Premium software (version 16) by executing Structured Query Language (SQL). The baseline information extracted from the MIMIC-IV database for this study included demographic characteristics, comorbidities, laboratory parameters, clinical outcomes, and disease severity scores. The variables involved include: ① Demographic characteristics: age at admission, gender, marital status, insurance type, and body mass index (BMI); ② Comorbidities: coronary heart disease, heart failure, hypertension, diabetes, and obesity; ③ Scores: SOFA score; ④ Laboratory indicators: red blood cells, platelets, creatinine, and lactate, etc. The CTI index was calculated using the formula: CTI = 0.412 * Ln(CRP) + TyG; TyG = Ln[fasting triglycerides (mg/dL) * fasting blood glucose (mg/dL)/2] Calculation [ 3 ]. Since the proportion of missing values for continuous variables in all baseline data was less than 20%, we imputed missing data using the mean or median based on the distribution characteristics of the missing data. 2.2 Primary and Secondary Outcomes The primary outcome of this study was in-hospital all-cause mortality, with secondary outcomes being mortality within 60 days and 90 days of admission to the ICU. Mortality data for discharged patients were obtained from the US Social Security Death Index. 2.3 Statistical Analysis All statistical analyses were performed using R version 4.5.0 (R Foundation) and Free Statistics software version 2.0. Normally distributed continuous variables are expressed as mean ± standard deviation (SD) and compared using t-tests. Continuous variables with non-normal distributions were expressed as median and interquartile range (IQR) and compared using the Wilcoxon rank-sum test. Categorical variables were expressed as numbers and percentages and compared using the Pearson chi-square test. CTI was also assessed as a continuous variable to enhance the robustness of the results. Additionally, patients were divided into two groups based on whether they died during their ICU stay for analysis. To explore the nonlinear association between CTI levels and in-hospital, 60-day, and 90-day mortality rates, restricted cubic spline analysis was performed. Subsequently, following Harrell's recommendation, four knots were set at the 5th, 35th, 65th, and 95th percentiles[ 10 ]. Kaplan-Meier curves were used to assess the cumulative survival probability of patients in different CTI groups. In the KM curves, the Log-rank test was used to compare differences between groups (death vs. survival). To assess the association between the CTI index and the risk of in-hospital mortality, 60-day mortality, and 90-day mortality, a multivariable Cox regression analysis was conducted, calculating the hazard ratio (HR) and its corresponding 95% confidence intervals (CIs) to quantify the impact of the CTI index on these outcomes. Additionally, subgroup analyses were conducted based on gender, age (< 60 years, ≥ 60 years), SOFA score (< 4, 5–8, ≥ 9), marital status (married, single, other), insurance type (private insurance, Medicare insurance, Medicaid insurance, other), BMI (< 18.5, 18.5–24.9, 25–29.9, ≥ 29.9), and presence of comorbidities such as hypertension, diabetes, and heart disease. Through stratified comparisons and interaction tests, the consistency of the association between CTI and mortality across different subgroups was assessed. Statistical significance was defined as a two-sided P < 0.05. 2.4 Boruta Algorithm Boruta is a feature selection algorithm based on a random forest classifier. The goal of the Boruta feature selection algorithm is to select the feature set most relevant to the dependent variable (rather than a specific model), rather than selecting the smallest compact feature set that best fits a specific model. Selecting important features for model refinement is believed to improve model accuracy and stability. If the Z-value of an original feature is significantly higher than the maximum Z-value of the shadow feature at each step of the iteration, the feature is considered important[ 11 ]; otherwise, it is considered unimportant or provisional. The Boruta algorithm is used to assess the importance of CTI. 3 Results 3.1 Baseline Characteristic Analysis A total of 3,693 patients were included from the MIMIC IV database. The in-hospital, 60-day, and 90-day mortality rates were 17.5%, 21.6%, and 23.8%, respectively. The baseline characteristics of this study are shown in Table 1 . Patients were grouped based on their CTI levels using quartiles: Q1 (CTI ≤ 9.61); Q2 (9.61 < CTI ≤ 10.42); Q3 (10.42 11.19). The average age of participants was 60.7 ± 14.7 years, with males accounting for 57.5% of the total. Compared with patients in the lowest CTI group (Q1), patients in the highest CTI group (Q4) had higher rates of obesity, diabetes, and SOFA scores, indicating more severe conditions. Additionally, patients with higher CTI indices exhibited elevated platelet counts, blood urea nitrogen levels, creatinine levels, and reduced HDL levels. Furthermore, longer hospital stays and ICU durations were observed in the higher CTI index groups. As shown in Table 2 , comparisons between non-survivors and survivors revealed that patient characteristics such as gender, marital status, hypertension, SOFA score, platelet count, red blood cell count, blood urea nitrogen (BUN), blood glucose, lactate, and C-reactive protein (CRP) were also associated with in-hospital mortality. Table 1 Participant Characteristics and Outcomes by CTI Index CTI Index Quartile Total (n = 3693) T1 (n = 923) T2 (n = 923) T3 (n = 923) T4 (n = 924) p χ 2 /t gender (%) < 0.001 16.95 Male 2124 (57.5) 491 (53.2) 515 (55.8) 544 (58.9) 574 (62.1) Female 1569 (42.5) 432 (46.8) 408 (44.2) 379 (41.1) 350 (37.9) Age, ( \(\:\stackrel{-}{X}\) ± S) 60.7 ± 14.7 61.5 ± 14.5 62.0 ± 14.6 60.7 ± 14.8 58.6 ± 14.6 < 0.001 9.904 Insurance (%) 0.054 16.661 Medicaid 619 (16.8) 149 (16.1) 146 (15.8) 154 (16.7) 170 (18.4) Medicare 1666 (45.1) 441 (47.8) 436 (47.2) 419 (45.4) 370 (40) OTHER 156 ( 4.2) 37 (4) 42 (4.6) 31 (3.4) 46 (5) Private 1252 (33.9) 296 (32.1) 299 (32.4) 319 (34.6) 338 (36.6) Marital Status (%) 0.549 4.961 MARRIED 1671 (45.2) 434 (47) 411 (44.5) 425 (46) 401 (43.4) OTHER 841 (22.8) 200 (21.7) 202 (21.9) 211 (22.9) 228 (24.7) SINGLE 1181 (32.0) 289 (31.3) 310 (33.6) 287 (31.1) 295 (31.9) BMI, ( \(\:\stackrel{-}{X}\) ± S) 41.9 ± 45.0 41.4 ± 27.0 42.6 ± 31.9 44.9 ± 74.3 38.7 ± 28.7 0.029 3.006 Hypertension(%) 0.438 2.715 NO 2289 (62.0) 563 (61) 586 (63.5) 582 (63.1) 558 (60.4) YES 1404 (38.0) 360 (39) 337 (36.5) 341 (36.9) 366 (39.6) Obesity (%) < 0.001 59.011 NO 2866 (77.6) 767 (83.1) 747 (80.9) 712 (77.1) 640 (69.3) YES 827 (22.4) 156 (16.9) 176 (19.1) 211 (22.9) 284 (30.7) CHF(%) 0.013 10.821 NO 2323 (62.9) 597 (64.7) 556 (60.2) 558 (60.5) 612 (66.2) YES 1370 (37.1) 326 (35.3) 367 (39.8) 365 (39.5) 312 (33.8) CAD(%) 0.519 2.268 NO 2536 (68.7) 637 (69) 617 (66.8) 635 (68.8) 647 (70) YES 1157 (31.3) 286 (31) 306 (33.2) 288 (31.2) 277 (30) Diabetes (%) < 0.001 86.523 NO 2372 (64.2) 673 (72.9) 624 (67.6) 586 (63.5) 489 (52.9) YES 1321 (35.8) 250 (27.1) 299 (32.4) 337 (36.5) 435 (47.1) SOFA, ( \(\:\stackrel{-}{X}\) ± S) 3.9 ± 2.2 3.8 ± 2.1 3.9 ± 2.2 3.9 ± 2.2 4.1 ± 2.3 0.009 3.897 Lactate, (mmol/L,IQR) 1.8 (1.4, 2.4) 1.8 (1.5, 2.3) 1.8 (1.5, 2.4) 1.8 (1.5, 2.4) 1.8 (1.4, 2.3) 0.172 4.993 RBC, (10 9 /L,IQR) 3.5 (3.1, 4.0) 3.5 (3.1, 3.9) 3.5 (3.1, 4.0) 3.5 (3.1, 3.9) 3.6 (3.2, 4.0) 0.048 7.902 PLT, (10 9 /L,IQR) 206.0 (151.8, 268.0) 194.1 (141.7, 244.0) 204.9 (148.0, 267.4) 214.0 (157.2, 278.6) 218.6 (159.4, 285.0) < 0.001 51.562 BUN, (mg/dL,IQR) 1.2 (0.8, 1.9) 1.1 (0.8, 1.5) 1.2 (0.8, 1.8) 1.2 (0.9, 1.9) 1.3 (0.9, 2.3) < 0.001 48.348 Creatinine, (µmol/L,IQR) 119.3 (98.8, 161.2) 119.3 (86.8, 161.8) 119.3 (87.0, 143.4) 119.3 (94.9, 147.5) 119.3 (119.3, 189.2) < 0.001 24.68 TC, (mg/dL,IQR) 153.0 (139.0, 162.0) 153.0 (123.0, 173.0) 153.0 (130.0, 169.0) 153.0 (141.5, 159.0) 153.0 (153.0, 153.0) 0.013 10.779 TC/HDL, (IQR) 3.4 (3.2, 3.7) 3.4 (2.6, 3.4) 3.4 (3.0, 3.8) 3.4 (3.4, 3.8) 3.4 (3.4, 3.8) < 0.001 231.658 HDL, (mg/dL,IQR) 44.0 (40.0, 47.0) 44.0 (41.0, 58.0) 44.0 (38.0, 50.0) 44.0 (38.0, 44.0) 44.0 (42.0, 44.0) < 0.001 112.596 LDL (mg/dL,IQR) 79.3 (71.0, 84.0) 79.3 (60.0, 92.0) 79.3 (67.0, 91.0) 79.3 (73.0, 84.0) 79.3 (79.3, 79.3) 0.304 3.631 Icu_Los_Day ( \(\:\stackrel{-}{X}\) ± S) 7.6 ± 9.8 5.1 ± 6.6 6.6 ± 8.5 7.6 ± 10.3 11.0 ± 12.1 < 0.001 63.436 Hospital_Los_Day ( \(\:\stackrel{-}{X}\) ± S) 13.8 ± 18.6 9.4 ± 11.9 12.3 ± 19.7 14.0 ± 19.1 19.4 ± 20.7 < 0.001 48.836 In hospital mortality(%) < 0.001 23.458 Survivors 3045 (82.5) 795 (86.1) 768 (83.2) 764 (82.8) 718 (77.7) Non-survivors 648 (17.5) 128 (13.9) 155 (16.8) 159 (17.2) 206 (22.3) 60 day mortality (%) 0.003 14.195 Survivors 2895 (78.4) 751 (81.4) 725 (78.5) 732 (79.3) 687 (74.4) Non-survivors 798 (21.6) 172 (18.6) 198 (21.5) 191 (20.7) 237 (25.6) 90 day mortality(%) < 0.001 17.917 Survivors 2815 (76.2) 737 (79.8) 703 (76.2) 713 (77.2) 662 (71.6) Non-survivors 878 (23.8) 186 (20.2) 220 (23.8) 210 (22.8) 262 (28.4) Data: N (%) or Mean (IQR) or mean ± standard deviation BMI: Body Mass Index; RBC: Red Cell count; PLT: Platelet Coun; TC: Total Cholesterol; HDL: High-Density Lipoprotein; LDL:Low-Density Lipoprotein; BUN: Blood Urea Nitrogen; CHF: Congestive Heart Failure;CAD:Coronary Artery Disease;CRP: C-Reactive Protein; SOFA: Sequential Organ Failure Assessment; ICU_Los_Day: Intensive Care Unit Length of Stay;Hospital_Los_Day: Hospital Length of Stay Table 2 Baseline information on survivors and non-survivors Variables Total (n = 3693) Survive (n = 3045) Non-survive (n = 648) p statistic Gender(%) 0.522 0.409 Male 2124 (57.5) 1744 (57.3) 380 (58.6) Female 1569 (42.5) 1301 (42.7) 268 (41.4) Age, ( \(\:\stackrel{-}{X}\) ± S) 60.7 ± 14.7 60.2 ± 14.7 63.0 ± 14.2 < 0.001 19.415 Insurance(%) 0.008 11.959 Medicaid 619 (16.8) 522 (17.1) 97 (15) Medicare 1666 (45.1) 1335 (43.8) 331 (51.1) OTHER 156 ( 4.2) 128 (4.2) 28 (4.3) Private 1252 (33.9) 1060 (34.8) 192 (29.6) Marital Status, n (%) 0.002 12.04 MARRIED 1671 (45.2) 1406 (46.2) 265 (40.9) OTHER 841 (22.8) 661 (21.7) 180 (27.8) SINGLE 1181 (32.0) 978 (32.1) 203 (31.3) BMI, ( \(\:\stackrel{-}{X}\) ± S) 41.9 ± 45.0 41.3 ± 47.0 44.6 ± 34.2 0.093 2.83 hypertension.(%) < 0.001 13.584 NO 2289 (62.0) 1846 (60.6) 443 (68.4) YES 1404 (38.0) 1199 (39.4) 205 (31.6) CTI(%) < 0.001 23.458 1 923 (25.0) 795 (26.1) 128 (19.8) 2 923 (25.0) 768 (25.2) 155 (23.9) 3 923 (25.0) 764 (25.1) 159 (24.5) 4 924 (25.0) 718 (23.6) 206 (31.8) Obesity(%) 0.67 0.182 NO 2866 (77.6) 2359 (77.5) 507 (78.2) YES 827 (22.4) 686 (22.5) 141 (21.8) CHF(%) 0.567 0.327 NO 2323 (62.9) 1909 (62.7) 414 (63.9) YES 1370 (37.1) 1136 (37.3) 234 (36.1) CAD (%) 0.093 2.823 NO 2536 (68.7) 2073 (68.1) 463 (71.5) YES 1157 (31.3) 972 (31.9) 185 (28.5) Diabetes(%) 0.248 1.333 NO 2372 (64.2) 1943 (63.8) 429 (66.2) YES 1321 (35.8) 1102 (36.2) 219 (33.8) SOFA, ( \(\:\stackrel{-}{X}\) ± S) 3.9 ± 2.2 3.8 ± 2.1 4.4 ± 2.5 < 0.001 40.171 CTI, ( \(\:\stackrel{-}{X}\) ± S) 10.4 ± 1.2 10.4 ± 1.2 10.7 ± 1.2 < 0.001 30.168 CRP, (mg/L,IQR)) 30.9 (5.3, 102.5) 28.7 (5.0, 96.6) 45.0 (6.9, 128.8) < 0.001 18.364 First glu value, (mg/dL,IQR) 132.0 (105.0, 173.0) 130.0 (104.0, 169.0) 140.5 (109.8, 193.0) < 0.001 18.37 lactate, (mmol/L,IQR) 1.8 (1.4, 2.4) 1.8 (1.4, 2.3) 1.8 (1.6, 2.6) < 0.001 16.404 RBC, (10 9 /L,IQR) 3.5 (3.1, 4.0) 3.6 (3.2, 4.0) 3.4 (3.0, 3.9) < 0.001 24.658 PLT, (10 9 /L,IQR) 206.0 (151.8, 268.0) 210.2 (155.2, 271.5) 193.0 (130.2, 250.6) < 0.001 28.249 BUN, (mg/dL,IQR)) 1.2 (0.8, 1.9) 1.1 (0.8, 1.8) 1.4 (0.9, 2.4) < 0.001 33.717 Creatinine (µmol/L,IQR) 119.3 (98.8, 161.2) 119.3 (95.3, 161.0) 119.3 (109.0, 165.1) 0.298 1.081 TG, (mg/dL,IQR) 127.0 (88.0, 193.0) 126.0 (88.0, 190.0) 132.5 (89.0, 217.2) 0.028 4.832 TC/HDL (mg/dL,IQR) 3.4 (3.2, 3.7) 3.4 (3.1, 3.7) 3.4 (3.4, 3.4) 0.51 0.435 HDL (mg/dL,IQR) 44.0 (40.0, 47.0) 44.0 (39.0, 48.0) 44.0 (44.0, 44.0) 0.704 0.144 LDL (mg/dL,IQR) 79.3 (71.0, 84.0) 79.3 (70.0, 87.0) 79.3 (79.3, 79.3) 0.862 0.03 TC (mg/dL,IQR)) 153.0 (139.0, 162.0) 153.0 (136.0, 166.0) 153.0 (153.0, 153.0) 0.89 0.019 Data: N (%) or Mean (Q1–Q3) or mean ± standard deviation BMI: Body Mass Index; RBC: Red Cell count; PLT: Platelet Coun; TC: Total Cholesterol;TG:Triglyceride;HDL:High-Density Lipoprotein; LDL:Low-Density Lipoprotein; BUN: Blood Urea Nitrogen; CHF: Congestive Heart Failure;CAD:Coronary Artery Disease;CRP: C-Reactive Protein; SOFA: Sequential Organ Failure Assessment; 3.2 Association between CTI Index and In-Hospital, 60-Day, and 90-Day Mortality Rates This study constructed a multivariable Cox regression model with CTI as the independent variable and patient short-term mortality as the dependent variable to investigate the relationship between CTI and in-hospital, 60-day, and 90-day mortality(Table 3 ). Model 1 was unadjusted for confounding factors, Model 2 adjusted for demographic factors including gender, age, marital status, and insurance type, and Model 3 further adjusted for patient comorbidities and disease severity scores based on Model 2. Model 4 adjusted for patient laboratory indicators based on Model 3, with the following variables included: age; gender; marital status; insurance type; body mass index (BMI); SOFA score; hypertension; coronary artery disease (CAD); red blood cells (RBC); lactate; blood urea nitrogen (BUN); and platelets (PLT). When CTI is treated as a continuous variable, Model 1, which does not adjust for confounding factors, shows that CTI is a risk factor for in-hospital, 60-day, and 90-day mortality in sepsis patients. This association persists in Model 2, which progressively adjusts for covariates (HR: 1.21, 95% CI: 1.14–1.29, P < 0.001), Model 3 (HR: 1.2, 95% CI: 1.13–1.28, P < 0.001), and Model 4. In Model 4, which fully adjusted for confounding factors, each 1-unit increase in CTI was associated with a 23% increase in the risk of death. Patients were grouped based on CTI quartiles for further analysis. In fully adjusted Model 4, the risk of death increased with rising CTI levels. This upward trend in risk remained statistically significant across Models 1 to 4 (trend tests all P < 0.05). Table 3 Multivariate Cox regression analysis of CTI and mortality Variable Model 1 Model 2 Model 3 Model 4 OR(95%CI) Ρ OR(95%CI) Ρ OR(95%CI) Ρ OR(95%CI) Ρ In hospital mortality CTI as continuous 1.19 (1.12 ~ 1.27) < 0.001 1.21 (1.14 ~ 1.29) < 0.001 1.2 (1.13 ~ 1.28) < 0.001 1.23 (1.15 ~ 1.32) < 0.001 Q1 Ref Ref Ref Ref Q2 1.24 (0.98 ~ 1.57) 0.068 1.23 (0.97 ~ 1.56) 0.082 1.21 (0.96 ~ 1.53) 0.106 1.26 (1 ~ 1.59) 0.053 Q3 1.26 (1 ~ 1.59) 0.053 1.27 (1.01 ~ 1.6) 0.044 1.24 (0.98 ~ 1.56) 0.075 1.29 (1.02 ~ 1.63) 0.033 Q4 1.67 (1.34 ~ 2.09) < 0.001 1.73 (1.38 ~ 2.16) < 0.001 1.68 (1.35 ~ 2.1) < 0.001 1.82 (1.45 ~ 2.28) < 0.001 Trend test < 0.001 < 0.001 < 0.001 < 0.001 60 day mortality CTI as continuous 1.12 (1.06 ~ 1.19) < 0.001 1.15 (1.09 ~ 1.22) < 0.001 1.14 (1.08 ~ 1.21) < 0.001 1.17 (1.1 ~ 1.24) < 0.001 Q1 Ref Ref Ref Ref Q2 1.17 (0.95 ~ 1.43) 0.134 1.16 (0.95 ~ 1.42) 0.154 1.14 (0.93 ~ 1.4) 0.203 1.19 (0.97 ~ 1.46) 0.104 Q3 1.11 (0.91 ~ 1.37) 0.306 1.14 (0.92 ~ 1.4) 0.223 1.1 (0.9 ~ 1.36) 0.344 1.15 (0.93 ~ 1.42) 0.186 Q4 1.42 (1.16 ~ 1.72) 0.001 1.51 (1.24 ~ 1.84) < 0.001 1.47 (1.2 ~ 1.79) < 0.001 1.58 (1.29 ~ 1.93) < 0.001 Trend test 0.001 < 0.001 < 0.001 < 0.001 90 day mortality CTI as continuous 1.13 (1.07 ~ 1.19) < 0.001 1.16 (1.1 ~ 1.23) < 0.001 1.15 (1.09 ~ 1.22) < 0.001 1.18 (1.11 ~ 1.25) < 0.001 Q1 Ref Ref Ref Ref Q2 1.2 (0.99 ~ 1.46) 0.062 1.2 (0.98 ~ 1.45) 0.073 1.18 (0.97 ~ 1.43) 0.1 1.22 (1 ~ 1.48) 0.048 Q3 1.14 (0.93 ~ 1.38) 0.208 1.16 (0.95 ~ 1.41) 0.149 1.13 (0.92 ~ 1.37) 0.241 1.17 (0.96 ~ 1.42) 0.129 Q4 1. 4 6 (1.21 ~ 1.76) < 0.001 1.55 (1.28 ~ 1.87) < 0.001 1.51 (1.25 ~ 1.83) < 0.001 1.62 (1.34 ~ 1.97) < 0.001 Trend test < 0.001 < 0.001 < 0.001 < 0.001 Model 1: Unadjusted; Model 2: Adjusted For CTI + Age + Marital status + Insurance + gender; Model 3: Adjusted For CTI.+Age + Marital status + Insurance + gender + bmi + Sofa score + Hypertension + Coronary Heart Disease; Model4: Adjusted For CTI.+Age + Marital status + Insurance + gender + bmi + Sofa score + Hypertension + Coronary Heart Disease + Lactate + BUN + RBC + PLT. 3.3 Restricted Cubic Spline (RCS) Analysis of CTI and In-Hospital, 60-Day, and 90-Day Mortality Rates Restricted cubic spline (RCS) analysis was used to assess the dose-response relationship between the CTI index and in-hospital, 60-day, and 90-day mortality rates in sepsis patients. The results are shown in Fig. 2 . The risk of in-hospital mortality, 60-day mortality, and 90-day mortality was linearly positively correlated with the CTI index (Pnon-linearity = 0.225 and Pnon-linearity = 0.254, and Pnon-linearity = 0.223, respectively), indicating that the short-term mortality risk in sepsis patients increases linearly with rising CTI levels. 3.4 Subgroup Analysis Additionally, to confirm the relationship between the CTI index and in-hospital mortality, 60-day mortality, and 90-day mortality, stratified analyses were conducted based on age, gender, BMI, SOFA score, hypertension, diabetes, obesity, and heart disease. As shown in Fig. 3 , based on the subgroup analysis results, in the fully adjusted model, an elevated CTI index was positively associated with increased risk of in-hospital, 60-day, and 90-day mortality (HR > 1) in the majority of the included subgroups, and this association remained consistent in direction after multivariable adjustment. Notably, no significant interactions were observed (all interaction P values > 0.05), indicating that the CTI index has consistent prognostic value across all clinical strata evaluated. 3.5 Boruta Algorithm The results of evaluating variables associated with adverse outcomes in sepsis patients using the Boruta feature selection algorithm are shown in Fig. 4 . The important variables screened were CTI, PLT, and SOFA. 3.6 Kaplan-Meier survival curve analysis of the CTI index and in-hospital, 60-day, and 90-day mortality rates Kaplan-Meier analysis plots were constructed to compare survival rates at hospital discharge, 60 days, and 90 days among the four patient groups. As shown in Fig. 5 , patients in the low CTI group (Q1) had significantly higher cumulative survival probabilities at 60 days and 90 days compared to those in Q2, Q3, and Q4. Additionally, survival probabilities for critically ill sepsis patients deteriorated progressively from Q1 to Q4. The log-rank test results indicated that the differences in survival curves between the four groups at 60 days and 90 days were statistically significant (all P < 0.05). CTI was associated with the risk of in-hospital mortality, 60-day mortality, and 90-day mortality in sepsis patients. 4 Discussion This study, based on the MIMIC-IV database, revealed an association between the C-reactive protein-triglyceride-glucose index (CTI) and the short-term mortality risk in critically ill patients with sepsis. In this study, multidimensional analysis confirmed that the CTI index is positively correlated with short-term mortality in sepsis patients, with mortality risk increasing as the CTI index increases. Restricted cubic spline (RCS) analysis validated a linear positive correlation between the two, indicating a linear dose-response relationship. Patients with high CTI indices had significantly lower survival probabilities than those with low CTI indices. Therefore, the CTI index may serve as a decision-making tool for clinicians managing sepsis patients. In 2017, a total of 48.9 million cases of sepsis were reported globally, with 11 million deaths attributed to sepsis, accounting for nearly 20% of global deaths. Sepsis remains a major cause of health loss worldwide and imposes a significant health burden on many countries and regions[ 2 ]. The inflammatory response plays a crucial role in the development of sepsis. Excessive inflammatory responses and inflammatory mediators during sepsis have long been considered the primary cause of high mortality rates. Specifically, as the inflammatory response intensifies, cellular damage also increases, leading to organ dysfunction[ 12 ]. Systemic inflammation and insulin resistance play important roles in the development of sepsis. C-reactive protein (CRP) is a non-specific acute-phase protein and serves as a non-specific inflammatory marker, reaching levels up to 10,000 times higher than those in healthy individuals during the acute phase of severe infection, sepsis, or major tissue injury [ 13 ]. Therefore, in clinical practice, CRP is frequently measured as a diagnostic tool for infection, a marker of disease severity, and more[ 14 ]. Among various inflammatory markers, studies on the diagnostic accuracy of CRP in bacterial infections and sepsis are relatively frequent [ 15 ]. In a retrospective study by Cui et al. [ 16 ] involving 59 sepsis patients, it was found that CRP has clinical value in the diagnosis and prognosis of sepsis. In one study, it was found that patients with CRP > 100 mg/L had increased hospital stay duration and mortality risk [ 17 ]. Additionally, in a survey study of 1,464 sepsis patients by Jiang et al. [ 18 ], it was found that persistently elevated or persistently decreased CRP levels were associated with higher in-hospital mortality rates. These findings are consistent with a study by Liang et al. [ 19 ], which included 146 sepsis patients, where the authors demonstrated an association between CRP and prognosis in sepsis patients. Studies across various populations have also shown that the TYG index is closely associated with sepsis patients, whether regarding the adverse outcomes of sepsis itself [ 20 ] or complications such as cardiovascular disease [ 21 , 22 ], delirium [ 23 ], and brain-related diseases [ 24 ] in sepsis patients. Insulin resistance also plays a role in the development of sepsis, with insulin levels increasing in sepsis patients while insulin sensitivity decreases. Previous studies have linked the TYG index to outcomes in sepsis patients. In a study by Xu et al. [ 25 ], the TYG index was found to be associated with 28-day mortality in sepsis patients, with a significant increase in 28-day all-cause mortality when the TYG index exceeded 9.03. CRP also plays a key role in the pathogenesis of insulin resistance (IR) by inducing local and/or systemic inflammation [ 26 ]. Both factors play a role in the progression of sepsis, and these reports indicate the need to combine inflammatory markers with IR markers. CTI was developed by Ruan and colleagues and is used to assess the prognosis of cancer patients [ 3 ]. It combines the inflammatory biomarker CRP and the IR index TyG. We hypothesize that the CTI index, which combines IR and inflammatory markers, has an impact on short-term mortality in sepsis patients. Previous studies have confirmed that the CTI index is associated with cardiovascular disease, cancer, diabetes, and other conditions [ 27 – 29 ], but the innovation lies in the first focus on sepsis, addressing the limitations of the single TyG index in assessing inflammation. Our study confirmed that the CTI index is positively linearly correlated with short-term mortality in sepsis patients. Similar to our findings, Ou et al.[ 30 ] explored the relationship between the CTI index and all-cause mortality in patients with CKM syndrome stages 0–3, and found a linear relationship between CTI and all-cause mortality. To reduce mortality in sepsis patients, we recommend that clinicians strive to lower the CTI index. Insulin resistance (IR) is defined as a pathophysiological state characterized by reduced insulin sensitivity, which affects glucose absorption and utilization [ 31 ]. Research has shown that acute blood glucose fluctuations significantly increase the risk of mortality in sepsis patients [ 32 ]. Our study also indicates that in a young male patient with multiple coexisting metabolic disorders and renal dysfunction, the CTI index will be elevated. From a mechanistic perspective, CTI serves as a composite indicator of inflammation, glucose metabolism, and lipid metabolism. Elevated CTI values reflect exacerbated IR, leading to glucose and lipid metabolism disorders, immune suppression, and mitochondrial damage in the septic state. Additionally, elevated CRP promotes lipid oxidative stress[ 33 ], while triglyceride accumulation further triggers inflammatory responses [ 34 ], forming an inflammatory-metabolic vicious cycle. This study supports this mechanism, as results showed a significantly increased prevalence of diabetes in the high CTI group. Further validation using the Boruta algorithm confirmed that CTI is a key predictor of sepsis-related mortality, surpassing conventional indicators such as LDL and BMI. Subgroup analysis revealed the population-specific predictive efficacy of CTI: it was more strongly associated in hypertensive patients (HR = 1.26 vs. 1.16 in the non-hypertensive group), consistent with Tang et al.'s findings that elevated CTI levels increase the risk of stroke in hypertensive patients, potentially due to vascular endothelial damage amplifying the pathogenic effects of metabolic-inflammatory interactions; The higher risk in the Medicare/Medicaid population reflects the potential impact of disparities in healthcare resources on outcomes. Combined with significantly elevated BUN/creatinine levels in the high CTI group, this suggests that renal dysfunction may exacerbate mortality risk through the accumulation of toxic substances. These findings support the potential of CTI as a bedside triage tool—when CTI > 11.19, intensive monitoring should be initiated, and targeted regulation of glucose-lipid metabolism and inflammatory pathways should be implemented. This study aimed to explore the correlation between the CTI index and short-term mortality in sepsis patients. The results confirmed a linear relationship between the two, which remained consistent even after adjusting for confounding variables. The CTI index is an easily obtainable indicator in clinical practice, requiring only simple extraction and calculation. which can save clinicians some effort. Additionally, the CTI index combines inflammation and insulin resistance, integrating these two pathological states into a single metric. When CRP-driven inflammation and TyG-reflected insulin resistance mutually amplify each other, CTI values significantly increase. This successfully explains why patients in the high CTI group exhibit more severe organ dysfunction, longer ICU stay times, and higher levels of renal dysfunction markers. Therefore, integrating the CTI index into the clinical management strategy framework for sepsis patients may enable better disease management and provide effective strategies. Healthcare providers can develop more effective measures based on CTI index data to improve the prognosis of sepsis patients. By obtaining early and accurate CTI values, medical staff can identify patients with higher mortality risks earlier, facilitating timely intervention. This approach holds promise for significantly reducing short-term mortality in sepsis patients, improving clinical outcomes, and having important implications for practice and research. This study also has certain limitations. First, it is a retrospective design, so causal relationships cannot be inferred, and it lacks data on antibiotic/insulin therapy, thereby limiting the assessment of CTI's intervenability. Additionally, this study is based on a single-center database, which may affect the generalizability of the conclusions. Future studies should validate the universality of CTI in prospective multicenter cohorts, explore the predictive value of dynamic CTI trajectories, and design intervention trials to determine whether CTI-guided therapy can improve survival outcomes, thereby further elucidating the key mechanisms of IR in sepsis. 5 Conclusion This study demonstrates that an increase in CTI levels is linearly correlated with poor outcomes in patients with sepsis. The linear dose-response relationship supports the inclusion of CTI in clinical risk scoring systems to identify high-risk patients early and guide individualized interventions. Abbreviations CTI:C-reactive protein-triglyceride glucose index MIMIC-IV: Medical Information Mart for Intensive Care-Ⅳ RCS:restrictive cubic spline analysis LOS in hospital:Length of stay in hospital LOS in ICU:Length of stay in ICU BIDMC:Beth Israel Deaconess Medical Center TyG:Triglyceride-glucose index CHD:Coronary Heart Disease CHF:Congestive Heart Failure SOFA :Sequential Organ Failure Assessment Declarations Ethics approval and consent to participate ： The first author (Cai Qingjiang) has successfully completed the Collaborative Institutional Training Initiative (CITI) programme, including exams on conflicts of interest and research involving only data or specimens, enabling him to successfully access, download, and use the database. (ID: 14326907) The MIMIC-IV database used in this study has been approved by the Institutional Review Boards (IRBs) of Beth Israel Deaconess Medical Centre and the Massachusetts Institute of Technology. Additionally, this study fully complies with the requirements of the Declaration of Helsinki and has obtained informed consent exemption. Consent for publication :Not applicable. Availability of data and materials ： The complete research dataset supporting these findings will be made available upon reasonable request to the corresponding author. Data availability: The details of the data screening codes for our analyses, which were provided by the authors of the MIMIC-IV database, can be found at GitHub (https://github.com/MIT-LCP/mimic-code). Conflict of interest/Comp eting interests ： Not applicable Funding ： This study was supported by the First Affiliated Hospital of Guangxi Medical University's 2024 Hospital Self-Set Scientific Research Cultivation Project—Clinical Nursing Research Climbing Plan (Project Name: Clinical Study on Early Resistance Training to Prevent Venous Thromboembolism in ICU Patients on Mechanical Ventilation; Project Number: YYZS2023018).lict of interest/Competing interests Author contributions ： The following authors conceived and designed the study: QC, YQ, BF,DH.QC,YQ,BF performed data management and conducted statistical analyses. DH ensured project and study management. QC,YQ,BFdrafted the manuscript. All authors contributed to interpretation of the data and revised the manuscript. All authors approved the final manuscript. DH is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Aknowledgements ： We are deeply grateful to the MIMIC-IV database for providing data support for this study. We would also like to thank the other authors for their support, guidance, and contributions during the research process. References Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche J, Coopersmith CM et al : The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) . JAMA-J AM MED ASSOC 2016, 315 (8):801-810. Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, Colombara DV, Ikuta KS, Kissoon N, Finfer S et al : Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study . LANCET 2020, 395 (10219):200-211. Ruan G, Xie H, Zhang H, Liu C, Ge Y, Zhang Q, Wang Z, Zhang X, Tang M, Song M et al : A Novel Inflammation and Insulin Resistance Related Indicator to Predict the Survival of Patients With Cancer . 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11:41:26","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":257300,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7299325/v1/ef23c90dc22b2c0227d6a8ce.html"},{"id":92857066,"identity":"65010ad2-6f30-461c-add4-5a4e92913100","added_by":"auto","created_at":"2025-10-06 11:41:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107798,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart on the sample selection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7299325/v1/d75ad9b4d804f4214a89ad60.png"},{"id":92857067,"identity":"c9e909fb-f6f5-41de-b4da-43669c1cc740","added_by":"auto","created_at":"2025-10-06 11:41:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72432,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship of CTI to in-hospital mortality, 60-day mortality, and 90-day mortality in a septic population.\u003c/p\u003e\n\u003cp\u003eA Restricted three-sample bar for in-hospital mortality. B Restricted triple spline for 60-day mortality. C Restrictive triple spline for 90-day mortality.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7299325/v1/1eb3af85920e399809918c82.png"},{"id":92857069,"identity":"f7cede70-42bc-477f-b384-f46bde8e8c60","added_by":"auto","created_at":"2025-10-06 11:41:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":219729,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses for the association of CTI index with in-hospital mortality,60-day mortality and ,90-day mortality.\u003c/p\u003e\n\u003cp\u003eCHD:Coronary Heart Disease,CHF:Congestive Heart Failure,SOFA: Sequential Organ Failure Assessment.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7299325/v1/bde290572fc90daeac3b57ff.png"},{"id":92857923,"identity":"894db0f3-7163-44f7-9db1-f8291814d316","added_by":"auto","created_at":"2025-10-06 11:49:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":169867,"visible":true,"origin":"","legend":"\u003cp\u003eFeature selection using the Boruta algorithm to analyze the relationship between the CTI index and in-hospital mortality. The x-axis displays the name of each variable, while the y-axis represents the Z-score of each variable. Box plots describe the Z-scores of each variable in the model calculation, where green boxes indicate important variables, blue boxes indicate tentative attributes, and yellow boxes indicate unimportant variables.\u003c/p\u003e\n\u003cp\u003eSOFA: Sequential Organ Failure Assessment; RBC: Red Blood Cell Count; BMI: Body Mass Index; LDL: Low-Density Lipoprotein; PLT: Platelet Count; HDL: High-Density Lipoprotein; CAD: Coronary Artery Disease; CHF: Congestive Heart Failure; TC: Total Cholesterol; TG: Triglyceride; GLU: Fasting Blood Glucose.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7299325/v1/a80cfd3064844937a7378ea7.png"},{"id":92857078,"identity":"dc9edab9-a3fc-424a-8693-e31a047c418d","added_by":"auto","created_at":"2025-10-06 11:41:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":176689,"visible":true,"origin":"","legend":"\u003cp\u003eIn-hospital, 60-day and 90-day Kaplan-Meier analysis plots ; KM curves showing survival for each quartile at in-hospital, 60-day and 90-day.SHR: Q1 (CTI ≤ 9.61); Q2 (9.61 \u0026lt; CTI ≤ 10.42); Q3 (10.42 \u0026lt; CTI ≤ 11.19); Q4 (CTI \u0026gt; 11.19); A In-hospital KM curve; B 60-day KM curve; C 90-day KM curve.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7299325/v1/250f6b436f86fd7cd6ae8320.png"},{"id":94993064,"identity":"e47cbc12-9863-4ec9-8465-a986dac50080","added_by":"auto","created_at":"2025-11-03 07:23:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3777938,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7299325/v1/e55631a4-3342-4185-a23f-b9bf11cec0cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between C-reactive protein-triglyceride glucose index (CTI) and short-term mortality in critically ill patients with sepsis: a prospective cohort study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although the mortality rate of sepsis has been declining with advancements in medical technology, sepsis remains one of the leading causes of global health issues [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, early identification of sepsis-related outcome indicators is essential.\u003c/p\u003e\u003cp\u003eThe C-reactive protein-triglyceride-glucose index (CTI), developed by Ruan et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], combines inflammation and insulin resistance (IR) to predict survival rates in cancer patients, confirming that CTI has good predictive ability for survival in cancer patients. In recent years, the triglyceride-glucose (TyG) index[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] has been proposed, and related studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] have found that TyG is closely associated with adverse outcomes in critically ill patients with ischemic stroke, cardiac arrest, and other conditions. Current research has also confirmed that TyG is associated with adverse outcomes in sepsis patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Currently, increasing evidence suggests that the C-reactive protein-triglyceride-glucose index (CTI) can serve as an indicator for comprehensively assessing the severity of IR and inflammation. CTI has demonstrated good predictive value in forecasting stroke incidence in hypertensive populations[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and cancer mortality in the general population[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To date, there have been no specific studies evaluating the association between CTI and mortality in sepsis patients. Given that IR and inflammation are both closely associated with the development of sepsis, it is essential to further explore the relationship between this composite index and adverse outcomes in sepsis patients. This study aims to investigate the association between the CTI index and clinical outcomes in sepsis patients, providing evidence-based support for future interventions targeting CTI to improve short-term adverse outcomes in sepsis patients.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Patients\u003c/h2\u003e\u003cp\u003e A retrospective analysis was conducted using the Medical Informatics Market for Critical Care Medicine-IV (MIMIC-IV 3.1) database to investigate the association between CTI and short-term mortality in sepsis patients. The database contains information on clearly defined and characterized patients admitted to the intensive care unit (ICU) at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2022. One author obtained access to the database and was responsible for data extraction (certification number 14326907). Since only third-party anonymous publicly available data were used, the Institutional Review Board at BIDMC approved the waiver of informed consent and the sharing of research resources. This study is a re-report of research conducted in accordance with the Declaration on the Use of Routinely Collected Health Data (RECORD).\u003c/p\u003e\u003cp\u003eScreening of sepsis patients admitted to the ICU for the first time. Inclusion criteria were as follows: The study included sepsis patients meeting the definition of sepsis according to the Third International Consensus on Sepsis and Sepsis Shock (Sepsis-3), defined as a Sequential Organ Failure Assessment (SOFA) score\u0026thinsp;\u0026ge;\u0026thinsp;2, and suspected infection during ICU hospitalization[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Exclusion criteria were as follows: (1) patients not admitted to the ICU for the first time; (2) patients with a hospital stay of less than 1 day, or an ICU stay of less than 1 day or more than 100 days; (3) insufficient data (e.g., triglycerides, fasting blood glucose, CRP, etc.). Finally, the cohort consisted of 3,693 sepsis patients aged 18 years or older at baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Variable Extraction\u003c/h2\u003e\u003cp\u003eIn this study, information was extracted using PostgresSQL software (version 15) and Navicat Premium software (version 16) by executing Structured Query Language (SQL). The baseline information extracted from the MIMIC-IV database for this study included demographic characteristics, comorbidities, laboratory parameters, clinical outcomes, and disease severity scores. The variables involved include: ① Demographic characteristics: age at admission, gender, marital status, insurance type, and body mass index (BMI); ② Comorbidities: coronary heart disease, heart failure, hypertension, diabetes, and obesity; ③ Scores: SOFA score; ④ Laboratory indicators: red blood cells, platelets, creatinine, and lactate, etc.\u003c/p\u003e\u003cp\u003eThe CTI index was calculated using the formula: CTI\u0026thinsp;=\u0026thinsp;0.412 * Ln(CRP)\u0026thinsp;+\u0026thinsp;TyG; TyG\u0026thinsp;=\u0026thinsp;Ln[fasting triglycerides (mg/dL) * fasting blood glucose (mg/dL)/2] Calculation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Since the proportion of missing values for continuous variables in all baseline data was less than 20%, we imputed missing data using the mean or median based on the distribution characteristics of the missing data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Primary and Secondary Outcomes\u003c/h2\u003e\u003cp\u003eThe primary outcome of this study was in-hospital all-cause mortality, with secondary outcomes being mortality within 60 days and 90 days of admission to the ICU. Mortality data for discharged patients were obtained from the US Social Security Death Index.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R version 4.5.0 (R Foundation) and Free Statistics software version 2.0. Normally distributed continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared using t-tests. Continuous variables with non-normal distributions were expressed as median and interquartile range (IQR) and compared using the Wilcoxon rank-sum test. Categorical variables were expressed as numbers and percentages and compared using the Pearson chi-square test. CTI was also assessed as a continuous variable to enhance the robustness of the results. Additionally, patients were divided into two groups based on whether they died during their ICU stay for analysis. To explore the nonlinear association between CTI levels and in-hospital, 60-day, and 90-day mortality rates, restricted cubic spline analysis was performed. Subsequently, following Harrell's recommendation, four knots were set at the 5th, 35th, 65th, and 95th percentiles[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Kaplan-Meier curves were used to assess the cumulative survival probability of patients in different CTI groups. In the KM curves, the Log-rank test was used to compare differences between groups (death vs. survival). To assess the association between the CTI index and the risk of in-hospital mortality, 60-day mortality, and 90-day mortality, a multivariable Cox regression analysis was conducted, calculating the hazard ratio (HR) and its corresponding 95% confidence intervals (CIs) to quantify the impact of the CTI index on these outcomes. Additionally, subgroup analyses were conducted based on gender, age (\u0026lt;\u0026thinsp;60 years, \u0026ge;\u0026thinsp;60 years), SOFA score (\u0026lt;\u0026thinsp;4, 5\u0026ndash;8, \u0026ge;\u0026thinsp;9), marital status (married, single, other), insurance type (private insurance, Medicare insurance, Medicaid insurance, other), BMI (\u0026lt;\u0026thinsp;18.5, 18.5\u0026ndash;24.9, 25\u0026ndash;29.9, \u0026ge;\u0026thinsp;29.9), and presence of comorbidities such as hypertension, diabetes, and heart disease. Through stratified comparisons and interaction tests, the consistency of the association between CTI and mortality across different subgroups was assessed. Statistical significance was defined as a two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Boruta Algorithm\u003c/h2\u003e\u003cp\u003eBoruta is a feature selection algorithm based on a random forest classifier. The goal of the Boruta feature selection algorithm is to select the feature set most relevant to the dependent variable (rather than a specific model), rather than selecting the smallest compact feature set that best fits a specific model. Selecting important features for model refinement is believed to improve model accuracy and stability. If the Z-value of an original feature is significantly higher than the maximum Z-value of the shadow feature at each step of the iteration, the feature is considered important[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]; otherwise, it is considered unimportant or provisional. The Boruta algorithm is used to assess the importance of CTI.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Baseline Characteristic Analysis\u003c/h2\u003e\n \u003cp\u003eA total of 3,693 patients were included from the MIMIC IV database. The in-hospital, 60-day, and 90-day mortality rates were 17.5%, 21.6%, and 23.8%, respectively. The baseline characteristics of this study are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients were grouped based on their CTI levels using quartiles: Q1 (CTI\u0026thinsp;\u0026le;\u0026thinsp;9.61); Q2 (9.61\u0026thinsp;\u0026lt;\u0026thinsp;CTI\u0026thinsp;\u0026le;\u0026thinsp;10.42); Q3 (10.42\u0026thinsp;\u0026lt;\u0026thinsp;CTI\u0026thinsp;\u0026le;\u0026thinsp;11.19); Q4 (CTI\u0026thinsp;\u0026gt;\u0026thinsp;11.19). The average age of participants was 60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7 years, with males accounting for 57.5% of the total. Compared with patients in the lowest CTI group (Q1), patients in the highest CTI group (Q4) had higher rates of obesity, diabetes, and SOFA scores, indicating more severe conditions. Additionally, patients with higher CTI indices exhibited elevated platelet counts, blood urea nitrogen levels, creatinine levels, and reduced HDL levels. Furthermore, longer hospital stays and ICU durations were observed in the higher CTI index groups. As shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, comparisons between non-survivors and survivors revealed that patient characteristics such as gender, marital status, hypertension, SOFA score, platelet count, red blood cell count, blood urea nitrogen (BUN), blood glucose, lactate, and C-reactive protein (CRP) were also associated with in-hospital mortality.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParticipant Characteristics and Outcomes by CTI Index\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCTI Index Quartile\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;3693)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT1 (n\u0026thinsp;=\u0026thinsp;923)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT2 (n\u0026thinsp;=\u0026thinsp;923)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT3 (n\u0026thinsp;=\u0026thinsp;923)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eT4 (n\u0026thinsp;=\u0026thinsp;924)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e/t\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egender (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2124 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e491 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e515 (55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e544 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e574 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1569 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e432 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e408 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e379 (41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge,\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn; S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.6\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsurance (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e619 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1666 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e436 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e419 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOTHER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e156 ( 4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1252 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e296 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e299 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e319 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338 (36.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital Status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARRIED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1671 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e434 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e411 (44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e425 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOTHER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e841 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSINGLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1181 (32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e289 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e310 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e295 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI,\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn; S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.9\u0026thinsp;\u0026plusmn;\u0026thinsp;45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.4\u0026thinsp;\u0026plusmn;\u0026thinsp;27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.6\u0026thinsp;\u0026plusmn;\u0026thinsp;31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.9\u0026thinsp;\u0026plusmn;\u0026thinsp;74.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.7\u0026thinsp;\u0026plusmn;\u0026thinsp;28.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2289 (62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e563 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e586 (63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e582 (63.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e558 (60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1404 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e360 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e337 (36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e341 (36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e366 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObesity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2866 (77.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e767 (83.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e747 (80.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e712 (77.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e640 (69.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e827 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHF(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2323 (62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e597 (64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e556 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e558 (60.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e612 (66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1370 (37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e326 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e367 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365 (39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e312 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAD(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2536 (68.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e637 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e617 (66.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e635 (68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e647 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1157 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e306 (33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e288 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e277 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2372 (64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e673 (72.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e624 (67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e586 (63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e489 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1321 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e299 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e337 (36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e435 (47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOFA,\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn; S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLactate,\u003c/p\u003e\n \u003cp\u003e(mmol/L,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8 (1.4, 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 (1.5, 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8 (1.5, 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 (1.5, 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 (1.4, 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBC,\u003c/p\u003e\n \u003cp\u003e(10\u003csup\u003e9\u003c/sup\u003e/L,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.5 (3.1, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5 (3.1, 3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.5 (3.1, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5 (3.1, 3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6 (3.2, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.902\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT,\u003c/p\u003e\n \u003cp\u003e(10\u003csup\u003e9\u003c/sup\u003e/L,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206.0 (151.8, 268.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e194.1 (141.7, 244.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e204.9 (148.0, 267.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214.0 (157.2, 278.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218.6 (159.4, 285.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUN,\u003c/p\u003e\n \u003cp\u003e(mg/dL,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2 (0.8, 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1 (0.8, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2 (0.8, 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (0.9, 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 (0.9, 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine,\u003c/p\u003e\n \u003cp\u003e(\u0026micro;mol/L,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119.3 (98.8, 161.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.3 (86.8, 161.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119.3 (87.0, 143.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.3 (94.9, 147.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.3 (119.3, 189.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC,\u003c/p\u003e\n \u003cp\u003e(mg/dL,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153.0 (139.0, 162.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153.0 (123.0, 173.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153.0 (130.0, 169.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153.0 (141.5, 159.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153.0 (153.0, 153.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC/HDL,\u003c/p\u003e\n \u003cp\u003e(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4 (3.2, 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4 (2.6, 3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4 (3.0, 3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4 (3.4, 3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4 (3.4, 3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e231.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL,\u003c/p\u003e\n \u003cp\u003e(mg/dL,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.0 (40.0, 47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.0 (41.0, 58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.0 (38.0, 50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.0 (38.0, 44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.0 (42.0, 44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112.596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003cp\u003e(mg/dL,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.3 (71.0, 84.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.3 (60.0, 92.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.3 (67.0, 91.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.3 (73.0, 84.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.3 (79.3, 79.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.631\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIcu_Los_Day\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn; S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospital_Los_Day\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn; S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.4\u0026thinsp;\u0026plusmn;\u0026thinsp;20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn hospital mortality(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvivors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3045 (82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e795 (86.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e768 (83.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e764 (82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e718 (77.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-survivors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e648 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e155 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 day mortality (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvivors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2895 (78.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e751 (81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e725 (78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e732 (79.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e687 (74.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-survivors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e798 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 day mortality(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvivors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2815 (76.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e737 (79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e703 (76.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e713 (77.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e662 (71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-survivors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e878 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186 (20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e262 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eData: N (%) or Mean (IQR) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation\u003c/p\u003e\n \u003cp\u003eBMI: Body Mass Index; RBC: Red Cell count; PLT: Platelet Coun; TC: Total Cholesterol; HDL: High-Density Lipoprotein; LDL:Low-Density Lipoprotein; BUN: Blood Urea Nitrogen; CHF: Congestive Heart Failure;CAD:Coronary Artery Disease;CRP: C-Reactive Protein; SOFA: Sequential Organ Failure Assessment; ICU_Los_Day: Intensive Care Unit Length of Stay;Hospital_Los_Day: Hospital Length of Stay\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline information on survivors and non-survivors\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;3693)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSurvive\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3045)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-survive\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;648)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003estatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2124 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1744 (57.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e380 (58.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1569 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1301 (42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e268 (41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge,\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn; S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsurance(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e619 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e522 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1666 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1335 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e331 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOTHER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e156 ( 4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1252 (33.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1060 (34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital Status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARRIED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1671 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1406 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e265 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOTHER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e841 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e661 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSINGLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1181 (32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e978 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI,\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn; S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.9\u0026thinsp;\u0026plusmn;\u0026thinsp;45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.3\u0026thinsp;\u0026plusmn;\u0026thinsp;47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.6\u0026thinsp;\u0026plusmn;\u0026thinsp;34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehypertension.(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2289 (62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1846 (60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e443 (68.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1404 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1199 (39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205 (31.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTI(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e923 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e795 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e923 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e768 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e923 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e764 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e924 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e718 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObesity(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2866 (77.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2359 (77.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e507 (78.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e827 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e686 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHF(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2323 (62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1909 (62.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e414 (63.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1370 (37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1136 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e234 (36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAD (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2536 (68.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2073 (68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e463 (71.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1157 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e972 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e185 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2372 (64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1943 (63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e429 (66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1321 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1102 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOFA,\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn; S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTI,\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn; S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP,\u003c/p\u003e\n \u003cp\u003e(mg/L,IQR))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.9 (5.3, 102.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.7 (5.0, 96.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.0 (6.9, 128.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst glu value,\u003c/p\u003e\n \u003cp\u003e(mg/dL,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132.0 (105.0, 173.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130.0 (104.0, 169.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.5 (109.8, 193.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elactate,\u003c/p\u003e\n \u003cp\u003e(mmol/L,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8 (1.4, 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8 (1.4, 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 (1.6, 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBC,\u003c/p\u003e\n \u003cp\u003e(10\u003csup\u003e9\u003c/sup\u003e/L,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.5 (3.1, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.6 (3.2, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4 (3.0, 3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT,\u003c/p\u003e\n \u003cp\u003e(10\u003csup\u003e9\u003c/sup\u003e/L,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206.0 (151.8, 268.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e210.2 (155.2, 271.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193.0 (130.2, 250.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUN,\u003c/p\u003e\n \u003cp\u003e(mg/dL,IQR))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2 (0.8, 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1 (0.8, 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (0.9, 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003cp\u003e(\u0026micro;mol/L,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119.3 (98.8, 161.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119.3 (95.3, 161.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.3 (109.0, 165.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG,\u003c/p\u003e\n \u003cp\u003e(mg/dL,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127.0 (88.0, 193.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126.0 (88.0, 190.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.5 (89.0, 217.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC/HDL\u003c/p\u003e\n \u003cp\u003e(mg/dL,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4 (3.2, 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4 (3.1, 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4 (3.4, 3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003cp\u003e(mg/dL,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.0 (40.0, 47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.0 (39.0, 48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.0 (44.0, 44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003cp\u003e(mg/dL,IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.3 (71.0, 84.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.3 (70.0, 87.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.3 (79.3, 79.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003cp\u003e(mg/dL,IQR))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153.0 (139.0, 162.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153.0 (136.0, 166.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153.0 (153.0, 153.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eData: N (%) or Mean (Q1\u0026ndash;Q3) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation\u003c/p\u003e\n \u003cp\u003eBMI: Body Mass Index; RBC: Red Cell count; PLT: Platelet Coun; TC: Total Cholesterol;TG:Triglyceride;HDL:High-Density Lipoprotein; LDL:Low-Density Lipoprotein; BUN: Blood Urea Nitrogen; CHF: Congestive Heart Failure;CAD:Coronary Artery Disease;CRP: C-Reactive Protein; SOFA: Sequential Organ Failure Assessment;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Association between CTI Index and In-Hospital, 60-Day, and 90-Day Mortality Rates\u003c/h2\u003e\n \u003cp\u003eThis study constructed a multivariable Cox regression model with CTI as the independent variable and patient short-term mortality as the dependent variable to investigate the relationship between CTI and in-hospital, 60-day, and 90-day mortality(Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Model 1 was unadjusted for confounding factors, Model 2 adjusted for demographic factors including gender, age, marital status, and insurance type, and Model 3 further adjusted for patient comorbidities and disease severity scores based on Model 2. Model 4 adjusted for patient laboratory indicators based on Model 3, with the following variables included: age; gender; marital status; insurance type; body mass index (BMI); SOFA score; hypertension; coronary artery disease (CAD); red blood cells (RBC); lactate; blood urea nitrogen (BUN); and platelets (PLT).\u003c/p\u003e\n \u003cp\u003eWhen CTI is treated as a continuous variable, Model 1, which does not adjust for confounding factors, shows that CTI is a risk factor for in-hospital, 60-day, and 90-day mortality in sepsis patients. This association persists in Model 2, which progressively adjusts for covariates (HR: 1.21, 95% CI: 1.14\u0026ndash;1.29, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Model 3 (HR: 1.2, 95% CI: 1.13\u0026ndash;1.28, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Model 4. In Model 4, which fully adjusted for confounding factors, each 1-unit increase in CTI was associated with a 23% increase in the risk of death.\u003c/p\u003e\n \u003cp\u003ePatients were grouped based on CTI quartiles for further analysis. In fully adjusted Model 4, the risk of death increased with rising CTI levels. This upward trend in risk remained statistically significant across Models 1 to 4 (trend tests all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariate Cox regression analysis of CTI and mortality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Rho;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Rho;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Rho;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Rho;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn hospital mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTI as continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19 (1.12\u0026thinsp;~\u0026thinsp;1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21 (1.14\u0026thinsp;~\u0026thinsp;1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (1.13\u0026thinsp;~\u0026thinsp;1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (1.15\u0026thinsp;~\u0026thinsp;1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24 (0.98\u0026thinsp;~\u0026thinsp;1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (0.97\u0026thinsp;~\u0026thinsp;1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21 (0.96\u0026thinsp;~\u0026thinsp;1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26 (1\u0026thinsp;~\u0026thinsp;1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26 (1\u0026thinsp;~\u0026thinsp;1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (1.01\u0026thinsp;~\u0026thinsp;1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24 (0.98\u0026thinsp;~\u0026thinsp;1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29 (1.02\u0026thinsp;~\u0026thinsp;1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67 (1.34\u0026thinsp;~\u0026thinsp;2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73 (1.38\u0026thinsp;~\u0026thinsp;2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68 (1.35\u0026thinsp;~\u0026thinsp;2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82 (1.45\u0026thinsp;~\u0026thinsp;2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTrend test\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 day mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTI as continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (1.06\u0026thinsp;~\u0026thinsp;1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15 (1.09\u0026thinsp;~\u0026thinsp;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (1.08\u0026thinsp;~\u0026thinsp;1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17 (1.1\u0026thinsp;~\u0026thinsp;1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17 (0.95\u0026thinsp;~\u0026thinsp;1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (0.95\u0026thinsp;~\u0026thinsp;1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (0.93\u0026thinsp;~\u0026thinsp;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19 (0.97\u0026thinsp;~\u0026thinsp;1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.91\u0026thinsp;~\u0026thinsp;1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (0.92\u0026thinsp;~\u0026thinsp;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1 (0.9\u0026thinsp;~\u0026thinsp;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15 (0.93\u0026thinsp;~\u0026thinsp;1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42 (1.16\u0026thinsp;~\u0026thinsp;1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51 (1.24\u0026thinsp;~\u0026thinsp;1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47 (1.2\u0026thinsp;~\u0026thinsp;1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58 (1.29\u0026thinsp;~\u0026thinsp;1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTrend test\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 day mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTI as continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13 (1.07\u0026thinsp;~\u0026thinsp;1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (1.1\u0026thinsp;~\u0026thinsp;1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15 (1.09\u0026thinsp;~\u0026thinsp;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (1.11\u0026thinsp;~\u0026thinsp;1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (0.99\u0026thinsp;~\u0026thinsp;1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (0.98\u0026thinsp;~\u0026thinsp;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (0.97\u0026thinsp;~\u0026thinsp;1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (1\u0026thinsp;~\u0026thinsp;1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 (0.93\u0026thinsp;~\u0026thinsp;1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (0.95\u0026thinsp;~\u0026thinsp;1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13 (0.92\u0026thinsp;~\u0026thinsp;1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17 (0.96\u0026thinsp;~\u0026thinsp;1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.\u003cstrong\u003e4\u003c/strong\u003e6 (1.21\u0026thinsp;~\u0026thinsp;1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55 (1.28\u0026thinsp;~\u0026thinsp;1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51 (1.25\u0026thinsp;~\u0026thinsp;1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62 (1.34\u0026thinsp;~\u0026thinsp;1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTrend test\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eModel 1: Unadjusted;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eModel 2: Adjusted For CTI\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Marital status\u0026thinsp;+\u0026thinsp;Insurance\u0026thinsp;+\u0026thinsp;gender;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eModel 3: Adjusted For CTI.+Age\u0026thinsp;+\u0026thinsp;Marital status\u0026thinsp;+\u0026thinsp;Insurance\u0026thinsp;+\u0026thinsp;gender\u0026thinsp;+\u0026thinsp;bmi\u0026thinsp;+\u0026thinsp;Sofa score\u0026thinsp;+\u0026thinsp;Hypertension\u0026thinsp;+\u0026thinsp;Coronary Heart Disease;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eModel4: Adjusted For CTI.+Age\u0026thinsp;+\u0026thinsp;Marital status\u0026thinsp;+\u0026thinsp;Insurance\u0026thinsp;+\u0026thinsp;gender\u0026thinsp;+\u0026thinsp;bmi\u0026thinsp;+\u0026thinsp;Sofa score\u0026thinsp;+\u0026thinsp;Hypertension\u0026thinsp;+\u0026thinsp;Coronary Heart Disease\u0026thinsp;+\u0026thinsp;Lactate\u0026thinsp;+\u0026thinsp;BUN\u0026thinsp;+\u0026thinsp;RBC\u0026thinsp;+\u0026thinsp;PLT.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Restricted Cubic Spline (RCS) Analysis of CTI and In-Hospital, 60-Day, and 90-Day Mortality Rates\u003c/h2\u003e\n \u003cp\u003eRestricted cubic spline (RCS) analysis was used to assess the dose-response relationship between the CTI index and in-hospital, 60-day, and 90-day mortality rates in sepsis patients. The results are shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The risk of in-hospital mortality, 60-day mortality, and 90-day mortality was linearly positively correlated with the CTI index (Pnon-linearity\u0026thinsp;=\u0026thinsp;0.225 and Pnon-linearity\u0026thinsp;=\u0026thinsp;0.254, and Pnon-linearity\u0026thinsp;=\u0026thinsp;0.223, respectively), indicating that the short-term mortality risk in sepsis patients increases linearly with rising CTI levels.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Subgroup Analysis\u003c/h2\u003e\n \u003cp\u003eAdditionally, to confirm the relationship between the CTI index and in-hospital mortality, 60-day mortality, and 90-day mortality, stratified analyses were conducted based on age, gender, BMI, SOFA score, hypertension, diabetes, obesity, and heart disease. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, based on the subgroup analysis results, in the fully adjusted model, an elevated CTI index was positively associated with increased risk of in-hospital, 60-day, and 90-day mortality (HR\u0026thinsp;\u0026gt;\u0026thinsp;1) in the majority of the included subgroups, and this association remained consistent in direction after multivariable adjustment. Notably, no significant interactions were observed (all interaction P values\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the CTI index has consistent prognostic value across all clinical strata evaluated.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Boruta Algorithm\u003c/h2\u003e\n \u003cp\u003eThe results of evaluating variables associated with adverse outcomes in sepsis patients using the Boruta feature selection algorithm are shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The important variables screened were CTI, PLT, and SOFA.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Kaplan-Meier survival curve analysis of the CTI index and in-hospital, 60-day, and 90-day mortality rates\u003c/h2\u003e\n \u003cp\u003eKaplan-Meier analysis plots were constructed to compare survival rates at hospital discharge, 60 days, and 90 days among the four patient groups. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, patients in the low CTI group (Q1) had significantly higher cumulative survival probabilities at 60 days and 90 days compared to those in Q2, Q3, and Q4. Additionally, survival probabilities for critically ill sepsis patients deteriorated progressively from Q1 to Q4. The log-rank test results indicated that the differences in survival curves between the four groups at 60 days and 90 days were statistically significant (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). CTI was associated with the risk of in-hospital mortality, 60-day mortality, and 90-day mortality in sepsis patients.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study, based on the MIMIC-IV database, revealed an association between the C-reactive protein-triglyceride-glucose index (CTI) and the short-term mortality risk in critically ill patients with sepsis. In this study, multidimensional analysis confirmed that the CTI index is positively correlated with short-term mortality in sepsis patients, with mortality risk increasing as the CTI index increases. Restricted cubic spline (RCS) analysis validated a linear positive correlation between the two, indicating a linear dose-response relationship. Patients with high CTI indices had significantly lower survival probabilities than those with low CTI indices. Therefore, the CTI index may serve as a decision-making tool for clinicians managing sepsis patients.\u003c/p\u003e\u003cp\u003eIn 2017, a total of 48.9\u0026nbsp;million cases of sepsis were reported globally, with 11\u0026nbsp;million deaths attributed to sepsis, accounting for nearly 20% of global deaths. Sepsis remains a major cause of health loss worldwide and imposes a significant health burden on many countries and regions[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The inflammatory response plays a crucial role in the development of sepsis. Excessive inflammatory responses and inflammatory mediators during sepsis have long been considered the primary cause of high mortality rates. Specifically, as the inflammatory response intensifies, cellular damage also increases, leading to organ dysfunction[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSystemic inflammation and insulin resistance play important roles in the development of sepsis. C-reactive protein (CRP) is a non-specific acute-phase protein and serves as a non-specific inflammatory marker, reaching levels up to 10,000 times higher than those in healthy individuals during the acute phase of severe infection, sepsis, or major tissue injury [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, in clinical practice, CRP is frequently measured as a diagnostic tool for infection, a marker of disease severity, and more[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Among various inflammatory markers, studies on the diagnostic accuracy of CRP in bacterial infections and sepsis are relatively frequent [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In a retrospective study by Cui et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] involving 59 sepsis patients, it was found that CRP has clinical value in the diagnosis and prognosis of sepsis. In one study, it was found that patients with CRP\u0026thinsp;\u0026gt;\u0026thinsp;100 mg/L had increased hospital stay duration and mortality risk [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, in a survey study of 1,464 sepsis patients by Jiang et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], it was found that persistently elevated or persistently decreased CRP levels were associated with higher in-hospital mortality rates. These findings are consistent with a study by Liang et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which included 146 sepsis patients, where the authors demonstrated an association between CRP and prognosis in sepsis patients. Studies across various populations have also shown that the TYG index is closely associated with sepsis patients, whether regarding the adverse outcomes of sepsis itself [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] or complications such as cardiovascular disease [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], delirium [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and brain-related diseases [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] in sepsis patients. Insulin resistance also plays a role in the development of sepsis, with insulin levels increasing in sepsis patients while insulin sensitivity decreases. Previous studies have linked the TYG index to outcomes in sepsis patients. In a study by Xu et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], the TYG index was found to be associated with 28-day mortality in sepsis patients, with a significant increase in 28-day all-cause mortality when the TYG index exceeded 9.03. CRP also plays a key role in the pathogenesis of insulin resistance (IR) by inducing local and/or systemic inflammation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Both factors play a role in the progression of sepsis, and these reports indicate the need to combine inflammatory markers with IR markers.\u003c/p\u003e\u003cp\u003eCTI was developed by Ruan and colleagues and is used to assess the prognosis of cancer patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It combines the inflammatory biomarker CRP and the IR index TyG. We hypothesize that the CTI index, which combines IR and inflammatory markers, has an impact on short-term mortality in sepsis patients. Previous studies have confirmed that the CTI index is associated with cardiovascular disease, cancer, diabetes, and other conditions [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], but the innovation lies in the first focus on sepsis, addressing the limitations of the single TyG index in assessing inflammation. Our study confirmed that the CTI index is positively linearly correlated with short-term mortality in sepsis patients. Similar to our findings, Ou et al.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] explored the relationship between the CTI index and all-cause mortality in patients with CKM syndrome stages 0\u0026ndash;3, and found a linear relationship between CTI and all-cause mortality. To reduce mortality in sepsis patients, we recommend that clinicians strive to lower the CTI index. Insulin resistance (IR) is defined as a pathophysiological state characterized by reduced insulin sensitivity, which affects glucose absorption and utilization [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Research has shown that acute blood glucose fluctuations significantly increase the risk of mortality in sepsis patients [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our study also indicates that in a young male patient with multiple coexisting metabolic disorders and renal dysfunction, the CTI index will be elevated. From a mechanistic perspective, CTI serves as a composite indicator of inflammation, glucose metabolism, and lipid metabolism. Elevated CTI values reflect exacerbated IR, leading to glucose and lipid metabolism disorders, immune suppression, and mitochondrial damage in the septic state. Additionally, elevated CRP promotes lipid oxidative stress[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], while triglyceride accumulation further triggers inflammatory responses [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], forming an inflammatory-metabolic vicious cycle. This study supports this mechanism, as results showed a significantly increased prevalence of diabetes in the high CTI group. Further validation using the Boruta algorithm confirmed that CTI is a key predictor of sepsis-related mortality, surpassing conventional indicators such as LDL and BMI. Subgroup analysis revealed the population-specific predictive efficacy of CTI: it was more strongly associated in hypertensive patients (HR\u0026thinsp;=\u0026thinsp;1.26 vs. 1.16 in the non-hypertensive group), consistent with Tang et al.'s findings that elevated CTI levels increase the risk of stroke in hypertensive patients, potentially due to vascular endothelial damage amplifying the pathogenic effects of metabolic-inflammatory interactions; The higher risk in the Medicare/Medicaid population reflects the potential impact of disparities in healthcare resources on outcomes. Combined with significantly elevated BUN/creatinine levels in the high CTI group, this suggests that renal dysfunction may exacerbate mortality risk through the accumulation of toxic substances. These findings support the potential of CTI as a bedside triage tool\u0026mdash;when CTI\u0026thinsp;\u0026gt;\u0026thinsp;11.19, intensive monitoring should be initiated, and targeted regulation of glucose-lipid metabolism and inflammatory pathways should be implemented.\u003c/p\u003e\u003cp\u003eThis study aimed to explore the correlation between the CTI index and short-term mortality in sepsis patients. The results confirmed a linear relationship between the two, which remained consistent even after adjusting for confounding variables. The CTI index is an easily obtainable indicator in clinical practice, requiring only simple extraction and calculation. which can save clinicians some effort. Additionally, the CTI index combines inflammation and insulin resistance, integrating these two pathological states into a single metric. When CRP-driven inflammation and TyG-reflected insulin resistance mutually amplify each other, CTI values significantly increase. This successfully explains why patients in the high CTI group exhibit more severe organ dysfunction, longer ICU stay times, and higher levels of renal dysfunction markers. Therefore, integrating the CTI index into the clinical management strategy framework for sepsis patients may enable better disease management and provide effective strategies. Healthcare providers can develop more effective measures based on CTI index data to improve the prognosis of sepsis patients. By obtaining early and accurate CTI values, medical staff can identify patients with higher mortality risks earlier, facilitating timely intervention. This approach holds promise for significantly reducing short-term mortality in sepsis patients, improving clinical outcomes, and having important implications for practice and research.\u003c/p\u003e\u003cp\u003eThis study also has certain limitations. First, it is a retrospective design, so causal relationships cannot be inferred, and it lacks data on antibiotic/insulin therapy, thereby limiting the assessment of CTI's intervenability. Additionally, this study is based on a single-center database, which may affect the generalizability of the conclusions. Future studies should validate the universality of CTI in prospective multicenter cohorts, explore the predictive value of dynamic CTI trajectories, and design intervention trials to determine whether CTI-guided therapy can improve survival outcomes, thereby further elucidating the key mechanisms of IR in sepsis.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study demonstrates that an increase in CTI levels is linearly correlated with poor outcomes in patients with sepsis. The linear dose-response relationship supports the inclusion of CTI in clinical risk scoring systems to identify high-risk patients early and guide individualized interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCTI:C-reactive protein-triglyceride glucose index\u003c/p\u003e\n\u003cp\u003eMIMIC-IV: Medical Information Mart for Intensive Care-Ⅳ\u003c/p\u003e\n\u003cp\u003eRCS:restrictive cubic spline analysis\u003c/p\u003e\n\u003cp\u003eLOS in hospital:Length of stay in hospital\u003c/p\u003e\n\u003cp\u003eLOS in ICU:Length of stay in ICU\u003c/p\u003e\n\u003cp\u003eBIDMC:Beth Israel Deaconess Medical Center\u003c/p\u003e\n\u003cp\u003eTyG:Triglyceride-glucose index\u003c/p\u003e\n\u003cp\u003eCHD:Coronary Heart Disease\u003c/p\u003e\n\u003cp\u003eCHF:Congestive Heart Failure\u003c/p\u003e\n\u003cp\u003eSOFA :Sequential Organ Failure Assessment\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics\u0026nbsp;approval\u0026nbsp;and\u0026nbsp;consent\u0026nbsp;to\u0026nbsp;participate\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author (Cai Qingjiang) has successfully completed the Collaborative Institutional Training Initiative (CITI) programme, including exams on conflicts of interest and research involving only data or specimens, enabling him to successfully access, download, and use the database. (ID: 14326907) The MIMIC-IV database used in this study has been approved by the Institutional Review Boards (IRBs) of Beth Israel Deaconess Medical Centre and the Massachusetts Institute of Technology. Additionally, this study fully complies with the requirements of the Declaration of Helsinki and has obtained informed consent exemption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e:Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe complete research dataset supporting these findings will be made available upon reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe details of the data screening codes for our analyses, which were provided by the authors of the MIMIC-IV database, can be found at GitHub (https://github.com/MIT-LCP/mimic-code).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest/Comp\u003c/strong\u003e\u003cstrong\u003eeting interests\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the First Affiliated Hospital of Guangxi Medical University's 2024 Hospital Self-Set Scientific Research Cultivation Project—Clinical Nursing Research Climbing Plan (Project Name: Clinical Study on Early Resistance Training to Prevent Venous Thromboembolism in ICU Patients on Mechanical Ventilation; Project Number: YYZS2023018).lict of interest/Competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following authors conceived and designed the study: QC, YQ, BF,DH.QC,YQ,BF performed data management and conducted statistical analyses. DH ensured project and study management. QC,YQ,BFdrafted the manuscript. All authors contributed to interpretation of the data and revised the manuscript. All authors approved the final manuscript. DH is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAknowledgements\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are deeply grateful to the MIMIC-IV database for providing data support for this study. We would also like to thank the other authors for their support, guidance, and contributions during the research process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche J, Coopersmith CM\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)\u003c/strong\u003e. \u003cem\u003eJAMA-J AM MED ASSOC\u003c/em\u003e 2016, \u003cstrong\u003e315\u003c/strong\u003e(8):801-810.\u003c/li\u003e\n\u003cli\u003eRudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, Colombara DV, Ikuta KS, Kissoon N, Finfer S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGlobal, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study\u003c/strong\u003e. \u003cem\u003eLANCET\u003c/em\u003e 2020, 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\u003cstrong\u003e332\u003c/strong\u003e(6037):1519-1523.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sepsis, C-reactive protein-triglyceride-glucose index, Inflammation, Insulin resistance, MIMIC-IV","lastPublishedDoi":"10.21203/rs.3.rs-7299325/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7299325/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThis study aimed to investigate the association between the C-reactive protein-triglyceride-glucose index (CTI) and the risk of in-hospital mortality, 60-day mortality, and 90-day mortality in critically ill patients with sepsis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis was a retrospective cohort study using data from the Medical Intensive Care Unit Marketplace IV (MIMIC IV 3.1) database of patients with sepsis. Participants were divided into four groups based on the quartiles of the CTI index. Multivariate Cox regression was used to assess the association between CTI and mortality, and Restricted Cubic Spline (RCS) analysis was employed to evaluate the dose-response relationship between the CTI index and short-term mortality risk in sepsis patients; Subgroup analysis was conducted using stratified comparisons and interaction tests to assess the consistency of the association between CTI and mortality across different subgroups; the Boruta algorithm was applied to assess the importance of CTI. Kaplan-Meier (KM) curves were used to assess the cumulative survival probability of patients in different CTI groups. In the KM curves, the Log-rank test was used to compare differences between groups (mortality vs. survival).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 3,693 patients were included. The in-hospital mortality rate, 60-day mortality rate, and 90-day mortality rate were 17.5%, 21.6%, and 23.8%, respectively. In the multivariate Cox regression analysis, when CTI was treated as a continuous variable, each unit increase in CTI was associated with a 23% increase in mortality risk in a model fully adjusted for confounding factors. Additionally, trend tests indicated that the risk of in-hospital mortality, 60-day mortality, and 90-day mortality increased with higher quartiles of the CTI index. RCS analysis confirmed a linear relationship between CTI and the risk of in-hospital, 60-day, and 90-day mortality. Based on subgroup analysis results, in the fully adjusted model, in the majority of the included subgroups, an increase in CTI index was positively associated with an increased risk of in-hospital, 60-day, and 90-day mortality (HR\u0026thinsp;\u0026gt;\u0026thinsp;1), and this association remained consistent in direction after multivariable adjustment. Notably, no significant interactions were observed (all interaction P values\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Survival curves also confirmed that patients in the low CTI level group had significantly higher cumulative survival probabilities at 60 days and 90 days compared to those in the high CTI level group. Additionally, the survival probability of critically ill sepsis patients gradually deteriorated from low to high CTI levels. Furthermore, the Boruta algorithm validated that CTI is a key indicator of outcomes in sepsis patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study confirmed that CTI is linearly associated with in-hospital mortality, 60-day mortality, and 90-day mortality in sepsis patients. Therefore, dynamic monitoring of CTI levels and timely intervention in sepsis patients may be an effective clinical strategy to reduce short-term mortality in sepsis patients.\u003c/p\u003e","manuscriptTitle":"Association between C-reactive protein-triglyceride glucose index (CTI) and short-term mortality in critically ill patients with sepsis: a prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 11:41:21","doi":"10.21203/rs.3.rs-7299325/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8e4d336-e970-405e-a861-bad3489deafd","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55687239,"name":"Health sciences/Biomarkers"},{"id":55687240,"name":"Health sciences/Diseases"},{"id":55687241,"name":"Health sciences/Health care"},{"id":55687242,"name":"Health sciences/Medical research"},{"id":55687243,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-03T07:21:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 11:41:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7299325","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7299325","identity":"rs-7299325","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}