Association between Triglyceride-Glucose Index and Risk of Sepsis-Associated Liver Injury and Mortality in Critically Ill Patients: A Retrospective Cohort Study

Association between Triglyceride-Glucose Index and Risk of Sepsis-Associated Liver Injury and Mortality in Critically Ill Patients: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between Triglyceride-Glucose Index and Risk of Sepsis-Associated Liver Injury and Mortality in Critically Ill Patients: A Retrospective Cohort Study Xiaona Yi, Xiaoya Yang, Xingkai Shen, Shanshan Huang, Meixia Zheng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6703771/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 The triglyceride-glucose (TyG) index, a surrogate marker of insulin resistance, has been implicated in metabolic dysregulation and adverse clinical outcomes. However, its association with sepsis-associated liver injury (SALI) and mortality in critically ill patients remains unclear. This study aimed to investigate the association between the TyG index and SALI risk, as well as all-cause mortality in intensive care unit (ICU) patients. Methods A retrospective cohort study was conducted using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Critically ill patients with complete TyG index measurements were included. The primary outcome was SALI incidence, and secondary outcomes included in-hospital, ICU, 28-day, 90-day, and 365-day mortality. Logistic and cox multivariate regression analyses and restricted cubic spline were applied to evaluate associations. Results Among 4343 patients 10.7% developed SALI. In-hospital, ICU, 28-day, 90-day, and 365-day mortality rates were 15.6%, 11.1%, 18.4%, 23.2%, and 28.8%, respectively. A linear positive association was observed between the TyG index and SALI risk [adjusted OR (95% CI) 1.52 (1.12~2.08), P -value 0.008]. For mortality, a nonlinear association was identified with TyG index and in-hospital mortality (P for nonlinearity=0.014), with an inflection point at TyG index=9.888. Below this threshold, each TyG index unit increase was associated with higher in-hospital mortality [HR 1.86 (1.49–2.34)， P <0.001], whereas no significant association was observed above it [HR 1.22 (0.76–1.97)]. The subgroup analysis suggests that our results are robust. Conclusions Elevated TyG index was an independent risk factor for SALI in critically ill patients and demonstrated a nonlinear association with in-hospital mortality. These findings highlight TyG as a practical biomarker for risk stratification and targeted management in the ICU setting. Triglyceride-glucose index Sepsis-associated liver injury Mortality Insulin resistance MIMIC database Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Patients in intensive care units (ICUs) are statistically associated with elevated mortality rates, which imposes a significant burden on families and society at large[ 1 ]. A critical condition influencing mortality outcomes in this setting is sepsis, defined as a life-threatening organ dysfunction resulting from a dysregulated host response to infections[ 2 , 3 ]. In the context of sepsis, the liver undergoes a series of changes, including altered immune response, disruption of metabolic pathways, coagulation abnormalities, and impaired microvascular dynamics[ 4 ]. Collectively, these changes lead to hepatic dysfunction and ultimately to sepsis-associated liver injury (SALI). Systemic inflammation, hepatocyte apoptosis, and the resulting disturbances in glucose-fat metabolism during SALI can exacerbate the patient's clinical status[ 5 , 6 ]. Furthermore, it has been documented that the incidence of jaundice or other liver dysfunction markers in ICU patients correlates with significantly grim prognoses, where mortality rates can soar above 60% in cases of pronounced liver injury[ 7 ]. Given the elevated mortality rate observed in patients diagnosed with SALI, this condition is regarded as a grave and potentially fatal health threat. Early recognition and appropriate management are essential to improve the prognosis of SALI. It has been shown that insulin resistance (IR) may exacerbate hepatic dysfunction in patients with sepsis[ 8 ]. IR refers to a condition in which the body's tissues have a reduced sensitivity to insulin, resulting in an impaired ability of insulin to effectively promote glucose uptake and utilization. IR is considered a critical contributor to various metabolic disorders, including sepsis, type 2 diabetes mellitus (T 2 DM) and cardiovascular diseases [ 9 , 10 ]. Research has identified several underlying mechanisms contributing to insulin resistance. One notable mechanism involves the accumulation of ectopic lipids in tissues such as the liver and muscles, which disrupts insulin signaling pathways. Studies have shown that lipid accumulation can lead to the activation of pro-inflammatory pathways, impairing insulin effectiveness[ 11 , 12 ]. In the liver, insulin typically promotes glucose uptake and lipid synthesis. However, in insulin-resistant states, there is a paradoxical increase in hepatic glucose production due to failed insulin signaling, leading to hyperglycemia and dyslipidemia [ 12 ]. Furthermore, the presence of inflammatory mediators has been recognized as a significant factor influencing insulin action, highlighting the strong association between inflammation and IR [ 13 , 14 ]. The triglyceride-glucose (TyG) index has garnered significant attention as a predictor of IR and related metabolic disorders. TyG index is a novel biomarker calculated from fasting blood glucose and triglyceride levels. Emerging evidence suggests that this index is not only a determinant of glycemic control in type 2 diabetes but also has implications for a variety of adverse outcomes. The TyG index has been shown to correlate with several diseases, such as the risk of AKI, the mortality of sepsis and ischemic stroke[ 15 – 17 ], emphasizing its potential as a risk marker. However, the association between the TyG index and the risk of developing SALI in critically ill patients is unclear. This study aims to investigate the association between the TyG index and the risk of SALI, as well as its impact on in-hospital mortality, ICU mortality, and mortality at 28 days, 90 days, and 365 days. Methods Data Source This retrospective cohort study utilized the Medical Information Mart for Intensive Care IV (MIMIC-IV, Version 3.1) database, a publicly accessible repository containing de-identified clinical data from critically ill patients admitted to the Beth Israel Deaconess Medical Center (Boston, MA, USA) between 2008 and 2022. Access to the database was granted after completing the Collaborative Institutional Training Initiative (CITI) certification (No. 52390976). Ethical approval, including a waiver of informed consent, was obtained from the Institutional Review Boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines[ 18 ]. Study Population Adult patients ( ≧ 18years) who were hospitalized for the first time and admitted to the ICU for the first time were included in this study[ 19 ]. Exclusion criteria comprised: (1) multiple hospital admissions; (2) missing triglyceride or fasting blood glucose measurements within 24 hours of ICU admission; (3) ICU stay < 24 hours; (4) outliers. A total of 4,343 patients met eligibility criteria (Fig. 1 ). Exposure and Outcomes The primary exposure was the TyG index, calculated as: TyG index = ln [fasting TG (mg/dL) × FBG (mg/dL)] / 2[ 20 ]. Triglyceride and glucose levels were obtained from the first laboratory tests after ICU admission. The primary outcome was SALI, defined according to the guidelines of the Surviving Sepsis Campaign[ 21 ]: patients who met the sepsis 3.0 criteria with INR > 1.5 and a total bilirubin level > 2 mg/dL (34.2 µ mol/L), whereas sepsis was defined as a diagnosis that met the sepsis 3.0 diagnosis or an increase of the sofa ≥ 2. Secondary outcomes included: In-hospital mortality, ICU mortality, 28-day, 90-day, and 365-day all-cause mortality, ascertained via hospital records and Social Security Administration databases. Variable Extraction Data were extracted using Structured Query Language (SQL) via Postgres (v13.7.2) and Navicat Premium (v17), encompassing demographics (age, sex, race, height, weight), medical history (hypertension, diabetes, congestive heart failure, chronic pulmonary disease, etc.), laboratory parameters [white blood cell count(WBC), blood urea nitrogen(BUN), high-density lipoprotein(HDL), low-density lipoprotein(LDL), prothrombin time(PT), activated partial thromboplastin time(PTT), alanine transaminase(ALT), aspartate transaminase(AST), alkaline phosphatase(ALP), cholesterol total(TC), hemoglobin a1c(HbA1c), triglycerides(TG), etc.], medications (insulin, Lipid lowering drugs, Hypoglycemic drugs, etc.), interventions [mechanical ventilation(MV), vasoactive drugs, continuous renal replacement therapy(CRRT)], severity scores [oxford acute severity of illness score (OASIS); acute physiology score II (APSII); simplified acute physiology score II (SAPS II); sequential organ failure assessment (SOFA), etc.], vital signs [heart rate(HR), respiratory rate(RR), systolic blood pressure(SBP), diastolic blood pressure(DBP), mean arterial pressure(MAP); peripheral capillary oxygen saturation(SPO 2 ), and pulse oximetry-derived oxygen saturation(SPO 2 ), etc.], and survival outcomes. Laboratory values were recorded within the first 24 hours of ICU admission. Variables with > 60% missing data were excluded to ensure analytical stability, consistent with prior methodologies[ 22 ]. Missing data were generated using multiple interpolation by generating five estimated datasets, and the missing data are shown in Supplementary Table 1. Statistical Analysis Participants were stratified into tertiles based on the TyG index (T1–T3) and further categorized by survival status. Baseline characteristics were summarized as mean ± standard deviation or median (interquartile range, IQR) for continuous variables and counts (%) for categorical variables. Group comparisons employed Fisher’s exact test or Pearson’s chi-square test, as appropriate. The association between the TyG index and the risk of SALI was assessed using the cumulative incidence rate, whereas the association between the TyG index and all-cause mortality was assessed using the Kaplan-Meier survival curves with log-rank tests. The association between TyG index and SALI risk was assessed applying logistic multifactor analysis. Cox proportional hazards models generated hazard ratios (HRs) and 95% confidence intervals (CIs) for TyG index (analyzed both continuously and categorically by tertiles). The inclusion of a covariate in the model was deemed appropriate if its addition resulted in a change of at least 10% in the initial regression coefficients, or if it was deemed necessary based on prior findings and clinical considerations. The variance spreading factor (VIF) was used to determine whether a collinear relationship existed. VIF > 2 indicates a collinear association. Three adjustment models were constructed: Model 1: Unadjusted. Model 2: Adjusted for age, sex, race, year group. Model 3: Further adjusted for HR, SBP, DBP, MAP; SPO 2 , RR, temperature, WBC, hematocrit, hemoglobin, platelets, BUN, creatinine, albumin, HDL, LDL, calcium, magnesium, potassium, sodium, phosphate, chloride, PT, PTT, ALT, AST, ALP, TC, HbA1c, PCO 2 , PO 2 , hypertension, congestive heart failure, chronic pulmonary disease, insulin, lipid lowering drugs, hypoglycemic drugs, diabetes. Tertile groupings were also tested for trends. Restricted Cubic Spline (RCS) was used to assess whether there was a nonlinear association between TyG index and outcomes indicators. Subgroup analyses grouped sex, age (< 65 or ≥ 65 years), race, diabetes, hypertension, congestive heart failure, chronic pulmonary disease, and renal disease and assessed whether there was an interaction. Subgroup analyses are presented as forest plots. All analyses were conducted using R version 4.2.2( http://www.R-project.org , R Foundation) and Free Statistics software (version 2.0). Statistical significance was set at P -value < 0.05. Results Baseline Characteristics A total of 4,343 participants were stratified into tertiles based on the TyG index (T1: lowest, T3: highest). Significant differences were observed across groups for most baseline variables. Participants in the T3 group were younger, had higher BMI, and were more likely to be male. Racial distribution varied, with fewer White individuals in T3. The incidence of SALI (sepsis-associated liver injury) was highest in T3 (13.6% vs. 8.9% in T1, p < 0.001). Mortality rates escalated with increasing TyG tertiles. The T3 group exhibited the highest in-hospital mortality, ICU mortality, 28-day mortality, 90-day mortality, and 365-day mortality. Resource utilization metrics followed this trend: T3 patients had prolonged hospital LOS (median 10.3 vs. 7.7 days) and ICU LOS (4.6 vs. 3.2 days, p < 0.001). Laboratory parameters revealed a pronounced dysmetabolic phenotype in T3, characterized by elevated glucose, triglycerides, and HbA1c, alongside reduced HDL. Inflammatory markers (WBC, lactate) and organ dysfunction indices (ALT, creatinine) were significantly elevated in T3 (p < 0.001). Acid-base derangements, including lower pH and bicarbonate, were also prominent (p < 0.001). Comorbid diabetes and acute pancreatitis were overrepresented in T3. Paradoxically, hypertension prevalence decreased across tertiles. Disease severity scores systematically increased with TyG tertiles: APSIII, SAPSII, and SOFA scores. T3 patients required more intensive therapies, including MV, vasoactive agents, and CRRT. Insulin use was markedly higher in T3, consistent with their metabolic profile ( Table 1 ). Table 1 Baseline characteristics and outcomes of participants classified by TyG index tertiles Characteristics Total (N = 4343) T1 (7.30, 8.60) T2 (8.60, 9.22) T3 (9.22, 11.98) P -value N = 1448 N = 1446 N = 1449 Demographic Age, years 64.0 (52.5, 76.0) 68.0 (55.0, 81.0) 65.0 (54.0, 77.0) 60.0 (49.0, 70.0) < 0.001 Sex, n (%) 0.004 Male 2484 (57.2) 786 (54.3) 823 (56.9) 875 (60.4) Female 1859 (42.8) 662 (45.7) 623 (43.1) 574 (39.6) Race, n (%) < 0.001 White 2412 (55.5) 839 (57.9) 824 (57) 749 (51.7) Other 1931 (44.5) 609 (42.1) 622 (43) 700 (48.3) Year Group, n (%) 0.006 2008-2010 649 (14.9) 180 (12.4) 236 (16.3) 233 (16.1) 2011-2013 605 (13.9) 197 (13.6) 222 (15.4) 186 (12.8) 2014-2016 748 (17.2) 270 (18.6) 238 (16.5) 240 (16.6) 2017-2019 1073 (24.7) 379 (26.2) 356 (24.6) 338 (23.3) 2020-2022 1268 (29.2) 422 (29.1) 394 (27.2) 452 (31.2) BMI, kg/m 2 28.5 (24.7, 33.5) 26.8 (23.3, 30.3) 28.0 (24.5, 32.5) 30.8 (26.2, 36.2) < 0.001 Outcomes SALI, n (%) 466 (10.7) 129 (8.9) 140 (9.7) 197 (13.6) < 0.001 In-hospital mortality, n (%) 676 (15.6) 161 (11.1) 204 (14.1) 311 (21.5) < 0.001 ICU mortality, n (%) 483 (11.1) 105 (7.3) 137 (9.5) 241 (16.6) < 0.001 28-day mortality, n (%) 801 (18.4) 222 (15.3) 251 (17.4) 328 (22.6) < 0.001 90-day mortality, n (%) 1009 (23.2) 290 (20) 323 (22.3) 396 (27.3) < 0.001 365-day mortality, n (%) 1252 (28.8) 367 (25.3) 424 (29.3) 461 (31.8) < 0.001 Los-hospital, day 8.9 (4.7, 16.8) 7.7 (4.2, 13.7) 9.1 (4.8, 16.4) 10.3 (5.1, 20.2) < 0.001 Los-ICU, day 3.7 (2.0, 7.5) 3.2 (1.9, 5.8) 3.6 (2.0, 7.1) 4.6 (2.2, 10.1) < 0.001 Vital signs Heart rate, bpm 102.0 (88.0, 117.0) 98.0 (86.0, 113.0) 101.0 (89.0, 115.0) 106.0 (92.0, 121.0) < 0.001 SBP, mmHg 95.0 (84.0, 109.0) 98.0 (86.0, 111.0) 96.0 (85.0, 109.0) 93.0 (82.0, 106.0) < 0.001 DBP, mmHg 50.0 (43.0, 59.0) 51.0 (44.0, 60.0) 51.0 (43.0, 59.0) 49.0 (42.0, 57.0) < 0.001 MAP, mmHg 64.0 (56.0, 73.0) 66.0 (58.0, 75.0) 65.0 (56.0, 74.0) 62.0 (54.0, 71.0) < 0.001 Respirates, bpm 27.0 (24.0, 32.0) 27.0 (24.0, 30.0) 27.0 (24.0, 31.0) 29.0 (25.0, 33.0) < 0.001 Temperature, ℃ 36.6 (36.3, 36.8) 36.5 (36.3, 36.7) 36.6 (36.3, 36.7) 36.6 (36.3, 36.8) 0.002 SPO 2 , % 93.0 (90.0, 95.0) 93.0 (91.0, 95.0) 93.0 (90.0, 95.0) 92.0 (90.0, 94.0) 0.007 Laboratory tests Hematocrit, % 34.4 (28.8, 39.0) 34.9 (29.7, 39.1) 34.6 (28.9, 39.2) 33.6 (27.9, 38.2) < 0.001 Hemoglobin, g/dL 11.3 (9.4, 13.0) 11.5 (9.8, 13.0) 11.4 (9.4, 13.1) 11.0 (9.1, 12.7) < 0.001 Platelets, 10 9 /L 187.0 (139.0, 243.2) 186.0 (142.0, 241.0) 194.0 (147.0, 245.0) 181.0 (127.0, 245.5) 0.005 WBC, 10 9 /L 12.0 (8.8, 16.4) 10.4 (7.9, 14.3) 12.0 (9.0, 16.0) 13.8 (10.1, 18.9) < 0.001 BUN, mg/dL 18.0 (13.0, 28.0) 16.0 (12.0, 23.0) 18.0 (13.0, 27.0) 21.0 (14.0, 35.0) < 0.001 Creatinine, mg/dL 1.0 (0.7, 1.4) 0.9 (0.7, 1.2) 1.0 (0.7, 1.3) 1.1 (0.8, 1.7) < 0.001 T-Bil, mg/dL 0.6 (0.4, 1.0) 0.6 (0.4, 1.0) 0.6 (0.4, 0.9) 0.6 (0.4, 1.0) 0.048 INR 1.2 (1.1, 1.4) 1.2 (1.1, 1.4) 1.2 (1.1, 1.4) 1.2 (1.1, 1.4) 0.966 Albumin, g/dL 3.0 (2.5, 3.6) 3.3 (2.7, 3.7) 3.1 (2.6, 3.7) 2.8 (2.3, 3.4) < 0.001 Glucose, mg/dL 126.0 (103.0, 166.0) 106.0 (93.0, 124.0) 126.0 (105.0, 154.0) 169.0 (129.0, 235.0) < 0.001 TG, mg/dL 110.0 (78.0, 168.0) 71.0 (57.0, 87.0) 113.0 (92.0, 138.0) 207.0 (149.0, 309.0) < 0.001 Hemoglobin A1c, % 5.7 (5.4, 6.3) 5.5 (5.2, 5.8) 5.8 (5.4, 6.3) 6.3 (5.7, 8.0) < 0.001 TC, mg/dL 152.0 (121.0, 186.0) 146.0 (117.0, 179.8) 155.0 (124.0, 187.2) 157.0 (123.0, 196.0) < 0.001 HDL, mg/dL 45.0 (35.0, 57.0) 51.0 (39.0, 64.0) 45.0 (35.0, 54.0) 39.0 (30.0, 47.0) < 0.001 LDL, mg/dL 81.0 (57.0, 111.0) 78.0 (56.0, 106.2) 84.0 (60.0, 114.0) 82.0 (55.0, 117.0) 0.002 Calcium, mg/dL 8.6 (8.1, 9.1) 8.7 (8.3, 9.2) 8.7 (8.2, 9.1) 8.4 (7.8, 9.0) < 0.001 Magnesium, mg/dL 2.0 (1.8, 2.2) 1.9 (1.8, 2.1) 2.0 (1.8, 2.1) 2.0 (1.7, 2.2) 0.003 Potassium, m Eq/L 4.1 (3.7, 4.5) 4.0 (3.7, 4.4) 4.1 (3.7, 4.5) 4.1 (3.8, 4.6) < 0.001 Sodium, m Eq/L 139.0 (136.0, 141.0) 139.0 (136.0, 141.0) 139.0 (136.0, 141.0) 138.0 (135.0, 141.0) < 0.001 Phosphate, mg/dL 3.4 (2.9, 4.2) 3.4 (2.9, 4.0) 3.4 (2.8, 4.1) 3.6 (2.9, 4.6) < 0.001 Anion gap, m Eq/L 16.0 (13.0, 18.0) 15.0 (13.0, 17.0) 15.0 (13.0, 18.0) 17.0 (14.0, 20.0) < 0.001 Bicarbonate, m Eq/L 21.0 (19.0, 24.0) 22.0 (19.0, 24.0) 22.0 (19.0, 24.0) 20.0 (17.0, 23.0) < 0.001 Chloride, m Eq/L 105.0 (102.0, 108.0) 105.0 (102.0, 108.0) 105.0 (102.0, 108.0) 105.0 (101.0, 109.0) 0.612 PT, s 13.5 (12.2, 15.9) 13.3 (12.1, 15.9) 13.4 (12.2, 15.8) 13.6 (12.2, 16.1) 0.247 PTT, s 31.6 (27.8, 45.2) 31.4 (27.9, 43.1) 31.4 (27.7, 44.6) 32.1 (27.8, 48.5) 0.267 ALT, IU/L 26.0 (16.0, 60.0) 22.0 (14.0, 44.0) 26.0 (16.0, 54.0) 34.0 (20.0, 89.0) < 0.001 ALP, IU/L 80.0 (63.0, 107.0) 77.0 (62.0, 100.0) 79.0 (62.0, 106.0) 83.0 (65.0, 116.5) < 0.001 AST, IU/L 38.0 (22.0, 99.0) 29.5 (20.0, 66.0) 37.0 (22.0, 90.0) 51.0 (26.0, 160.0) < 0.001 LDH, IU/L 312.5 (215.0, 545.8) 252.0 (192.2, 385.0) 293.0 (215.0, 525.0) 409.0 (257.0, 723.0) < 0.001 Lactate, mmol/L 2.1 (1.3, 3.9) 1.9 (1.2, 3.8) 2.1 (1.3, 3.7) 2.2 (1.4, 4.2) 0.004 pH 7.3 (7.2, 7.4) 7.3 (7.3, 7.4) 7.3 (7.3, 7.4) 7.3 (7.2, 7.4) < 0.001 PO 2 , mmHg 79.0 (61.0, 108.0) 83.0 (61.0, 118.5) 81.0 (62.0, 116.5) 76.0 (60.0, 97.0) < 0.001 PCO 2, mmHg 44.0 (38.0, 52.0) 43.0 (37.0, 49.0) 43.0 (38.0, 50.0) 46.0 (39.0, 54.0) < 0.001 Base Excess, m Eq/L -3.0 (-7.0, 0.0) -2.0 (-6.0, 0.0) -2.0 (-6.0, 0.0) -4.0 (-9.0, 0.0) < 0.001 Comorbidities Diabetes, n (%) 1493 (34.4) 275 (19) 461 (31.9) 757 (52.2) < 0.001 Hypertension, n (%) 2585 (59.5) 893 (61.7) 876 (60.6) 816 (56.3) 0.008 MI, n (%) 944 (21.7) 256 (17.7) 336 (23.2) 352 (24.3) < 0.001 CHF, n (%) 1122 (25.8) 349 (24.1) 385 (26.6) 388 (26.8) 0.182 PVD, n (%) 342 (7.9) 109 (7.5) 126 (8.7) 107 (7.4) 0.346 CVD, n (%) 2050 (47.2) 834 (57.6) 721 (49.9) 495 (34.2) < 0.001 CPD, n (%) 811 (18.7) 253 (17.5) 260 (18) 298 (20.6) 0.072 Paraplegia, n (%) 1141 (26.3) 480 (33.1) 390 (27) 271 (18.7) < 0.001 RD, n (%) 710 (16.3) 182 (12.6) 244 (16.9) 284 (19.6) < 0.001 MC, n (%) 380 (8.7) 118 (8.1) 139 (9.6) 123 (8.5) 0.345 MST, n (%) 156 (3.6) 50 (3.5) 60 (4.1) 46 (3.2) 0.349 CCI 6.0 (4.0, 8.0) 6.0 (4.0, 8.0) 6.0 (4.0, 8.0) 6.0 (3.0, 8.0) < 0.001 Sepsis, n (%) 2224 (51.2) 576 (39.8) 697 (48.2) 951 (65.6) < 0.001 AKI, n (%) 2958 (68.1) 905 (62.5) 974 (67.4) 1079 (74.5) < 0.001 Scoring systems APS III 44.0 (31.0, 65.0) 39.0 (28.0, 53.0) 43.0 (30.0, 61.0) 55.0 (38.0, 83.0) < 0.001 SAPS II 34.0 (26.0, 44.0) 32.0 (25.0, 40.0) 34.0 (25.0, 43.0) 37.0 (27.0, 49.0) < 0.001 OASIS 34.0 (27.0, 41.0) 32.0 (26.0, 37.0) 33.0 (27.0, 40.0) 37.0 (30.0, 45.0) < 0.001 SOFA score, 2.0 (0.0, 3.0) 0.0 (0.0, 2.0) 0.0 (0.0, 3.0) 2.0 (0.0, 4.0) < 0.001 Interventions Vasoactive agents, n (%) 1195 (27.5) 287 (19.8) 375 (25.9) 533 (36.8) < 0.001 CRRT, n (%) 330 (7.6) 50 (3.5) 71 (4.9) 209 (14.4) < 0.001 MV, n (%) 1867 (43.0) 417 (28.8) 592 (40.9) 858 (59.2) < 0.001 Lipid lowering drugs, n (%) 2574 (59.3) 837 (57.8) 891 (61.6) 846 (58.4) 0.08 Insulin, n (%) 1743 (40.1) 397 (27.4) 540 (37.3) 806 (55.6) < 0.001 Hypoglycemic drugs, n (%) 277 (6.4) 36 (2.5) 81 (5.6) 160 (11) < 0.001 Abbreviations: LOS, length of stay; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; SpO2, peripheral capillary oxygen saturation; PCO 2, partial pressure of carbon dioxide; PO 2, partial pressure of oxygen; WBC, white blood cell counts; TC, Cholesterol total; TG, Triglycerides; T-Bil, bilirubin total; BUN, blood urine nitrogen; ALT, alanine transaminase; AST, aspartate transaminase; ALP, alkaline phosphatase; LDH, lactate dehydrogenase; HDL, high-density lipoprotein; LDL, low-density lipoprotein; INR, international normalized ratio; PT, prothrombin time; PTT, activated partial thromboplastin time; AP, Acute Pancreatitis; AF, Atrial Fibrillation; CKD, Chronic Kidney Disease; MI, Myocardial Infarct; CHF, Congestive Heart Failure; PVD, Peripheral Vascular Disease; CVD, Cerebrovascular Disease; CPD, Chronic Pulmonary Disease; RD, Renal Disease; MC, Malignant Cancer; MST, Metastatic Solid Tumor; CCI, Charlson Comorbidity Index; AKI, Acute kidney injury; OASIS, oxford acute severity of illness score; APSII, acute physiology score II; SAPS II, simplified acute physiology score II; SOFA, sequential organ failure assessment; CRRT, continuous renal replacement therapy; MV, mechanical ventilation. Lipid lowering drugs include Cholestyramine, Ezetimibe, Fenofibrate, Gemfibrozil, Atorvastatin, Lovastatin, Pravastatin, Rosuvastatin Calcium, Simvastatin; Hypoglycemic drugs include Metformin, glimepiride, glipizide, pioglitazone, repaglinide, rosiglitazone. TyG Index and SALI Risk In this study, multivariable logistic regression analyses revealed a significant positive association between the TyG index and the risk of SALI across adjusted models. When analyzed as a continuous variable, each unit increase in TyG index was associated with elevated SALI risk in all models (Model 1: OR=1.27, 95%CI 1.13-1.42; Model 2: OR=1.13, 95%CI 1.00-1.27; Model 3: OR=1.52, 95%CI 1.12-2.08), with all P -values <0.05. When categorized into tertiles, participants in the highest TyG tertile (T3: 9.22-11.98) demonstrated significantly higher SALI risk compared to the lowest tertile (T1) in unadjusted [OR 1.61, (1.27-2.04) ]and demographically adjusted models [OR 1.37, (1.07-1.74)], though this association attenuated after full adjustment for clinical covariates in Model 3 (OR=1.58, 95%CI 0.96-2.59) ( Table 2 ). The cumulative incidence graph for SALI is shown in Fig. 2A In addition, the RCS suggested a linear association between the level of TyG index and the risk of SALI in critically ill patients (p nonlinear = 0.912), as shown in Fig. 3 Table 2 The association between TyG index groups and the risk of SALI Variable Total Event (%) Model 1 Model 2 Model 3 OR (95% CI) P- value OR (95% CI) P- value OR (95% CI) P- value TyG continuous 4343 466 (10.7) 1.27 (1.13~1.42) <0.001 1.13 (1~1.27) 0.041 1.52 (1.12~2.08) 0.008 TyG tertiles T1 (7.30, 8.60) 1448 129 (8.9) 1(Ref) 1(Ref) 1(Ref) T2 (8.60, 9.22) 1446 140 (9.7) 1.1 (0.85~1.41) 0.474 1.1 (0.85~1.42) 0.457 1.3 (0.88~1.93) 0.192 T3 (9.22, 11.98) 1449 197 (13.6) 1.61 (1.27~2.04) <0.001 1.37 (1.07~1.74) 0.011 1.58 (0.96~2.59) 0.073 P for trend <0.001 0.01 0.072 OR: Odds Ratio; CI: confidential interval Model 1: unadjusted Model 2: adjusted for age, sex, race, year group Model 3: adjusted for age, sex, race, year group, heartrate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, respiration rate, temperature, peripheral capillary oxygen saturation, white blood cell count, hematocrit, hemoglobin, platelets, blood urea nitrogen, creatinine, albumin, cholesterol total, high-density lipoprotein, low-density lipoprotein, lactate dehydrogenase, calcium, magnesium, potassium, sodium, phosphate, chloride, prothrombin time, activated partial thromboplastin time, alanine transaminase, aspartate transaminase, alkaline phosphatase, lactate, pH, partial pressure of carbon dioxide, partial pressure of oxygen, hemoglobin a1c, diabetes, hypertension, congestive heart failure, chronic pulmonary disease, renal disease, Charlson comorbidity index, insulin, lipid-lowering drugs, hypoglycemic drugs, mechanical ventilation, vasoactive agents TyG Index and All-cause Mortality A Kaplan - Meier survival analysis was performed to compare all-cause mortality in patients based on TyG index tertiles. Patients with higher TyG index had significantly higher in-hospital mortality, ICU mortality, and all-cause mortality at 28-day, 90-day, and 365-days than those with lower TyG index ( Fig. 2 , Fig. S1 ). As shown in Table 3 , the elevated TyG index was significantly associated with increased risks of all-cause mortality across multiple time points. When analyzed as a continuous variable, each unit increase in TyG index consistently predicted higher mortality risks in unadjusted and adjusted models, with hazard ratios (HRs) ranging from 1.15 to 1.62 (all P for trend <0.001). In tertile-based analyses, the highest TyG tertile exhibited the strongest mortality risk compared to the lowest tertile. For example, in fully adjusted Model 3, T3 demonstrated HRs of 2.05 (CI:1.57–2.69) for in-hospital mortality, 2.20 (1.60–3.02) for ICU mortality, and 1.63 (1.33–1.98) for 365-day mortality. A dose-response relationship was evident across tertiles ( P for trend <0.001 in all models). Notably, these associations remained robust after adjusting demographics, vital signs, laboratory parameters, comorbidities, and medications (Model 3), suggesting TyG index is an independent risk factor for increased all-cause mortality. The strength of association attenuated over longer follow-up periods (e.g., 365-day mortality HR=1.63 for T3 vs. in-hospital HR=2.05) but remained statistically significant ( Table S3 ). Table 3 The association between TyG index and all-cause mortality Variable Total Event (%) Follow-up Time Model 1 Model 2 Model 3 HR (95%CI) P -value HR (95%CI) P -value HR (95%CI) P -value In-hospital mortality TyG continuous 4343 676 (15.6) 1388321.51623629 1.42 (1.31~1.55) <0.001 1.57 (1.43~1.71) <0.001 1.6 (1.37~1.87) <0.001 TyG tertiles T1 1448 161 (11.1) 486092.688635698 1(Ref) 1(Ref) 1(Ref) T2 1446 204 (14.1) 464651.41691932 1.29 (1.05~1.58) 0.017 1.36 (1.1~1.67) 0.004 1.25 (1~1.57) 0.046 T3 1449 311 (21.5) 437577.41068127 2.04 (1.68~2.46) <0.001 2.34 (1.92~2.84) <0.001 2.05 (1.57~2.69) <0.001 P for trend <0.001 <0.001 <0.001 ICU mortality TyG continuous 4343 483 (11.1) 1388321.51623629 1.53 (1.39~1.69) <0.001 1.63 (1.47~1.81) <0.001 1.62 (1.35~1.95) <0.001 TyG tertiles T1 1448 105 (7.3) 486092.688635698 1(Ref) 1(Ref) 1(Ref) T2 1446 137 (9.5) 464651.41691932 1.32 (1.03~1.71) 0.031 1.37 (1.06~1.77) 0.015 1.22 (0.93~1.6) 0.161 T3 1449 241 (16.6) 437577.41068127 2.41 (1.91~3.03) <0.001 2.6 (2.06~3.28) <0.001 2.2 (1.6~3.02) <0.001 P for trend <0.001 <0.001 <0.001 TyG index: T1 (7.30, 8.60), T2 (8.60, 9.22), T3 (9.22, 11.98). HR: Odds Ratio; CI: confidential interval Model 1: unadjusted Model 2: adjusted for age, sex, race, year group Model 3: adjusted for age, sex, race, year group, heartrate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, peripheral capillary oxygen saturation, respirates, temperature, white blood cell count, hematocrit, hemoglobin, platelets, blood urea nitrogen, creatinine, albumin, high-density lipoprotein, low-density lipoprotein, calcium, magnesium, potassium, sodium, phosphate, chloride, prothrombin time, activated partial thromboplastin time, alanine transaminase, aspartate transaminase, alkaline phosphatase, pH, partial pressure of carbon dioxide partial pressure of oxygen, hypertension, congestive heart failure, chronic pulmonary disease, insulin, lipid lowering drugs, hypoglycemic drugs, cholesterol total, hemoglobina1c, diabetes RCS analysis revealed a nonlinear association between TyG index and in-hospital mortality (P for nonlinearity 0.042) ( Fig.3A ). The piecewise Cox regression analysis revealed a nonlinear dose-response association between the TyG index and in-hospital mortality. The threshold was identified at 9.888 units (95% CI: 9.819-9.958) through maximum likelihood estimation. Below this threshold, each unit increase demonstrated a significant 86.4% elevation in risk (HR = 1.864, 95% CI: 1.489-2.335, P < 0.001). Above the threshold, the association attenuated substantially (HR = 1.222, 95% CI: 0.756-1.973, P = 0.413) without statistical significance. The likelihood ratio test confirmed superior model fits for the piecewise model over linear assumptions ( P 0.005), supported by significant nonlinearity testing (P = 0.014). These findings suggest the existence of a saturation effect beyond 9.888 units, where incremental exposure no longer confers proportional risk escalation ( Table 4 ). Table 4 Threshold effect analysis of TyG index and in-hospital mortality Item HR (95%CI) P- value Model 1 One line effect 1.6 (1.37, 1.87) <0.001 Model 2 Turning point (K) 9.888 TyG index < K 1.864 (1.489,2.335) < 0.001 TyG index≥K 1.222 (0.756,1.973) 0.4133 Likelihood Ratio test 0.005 Non-linear Test 0.014 95% CI for turning point (9.819, 9.958) HR, hazard ratio; CI, confidential interval. Model 1, linear analysis; Model 2, non-linear analysis. Adjusted for age, sex, race, year group, heartrate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, peripheral capillary oxygen saturation, respirates, temperature, white blood cell count, hematocrit, hemoglobin, platelets, blood urea nitrogen, creatinine, albumin, high-density lipoprotein, low-density lipoprotein, calcium, magnesium, potassium, sodium, phosphate, chloride, prothrombin time, activated partial thromboplastin time, alanine transaminase, aspartate transaminase, alkaline phosphatase, pH, partial pressure of carbon dioxide，partial pressure of oxygen, hypertension, congestive heart failure, chronic pulmonary disease, insulin, lipid lowering drugs, hypoglycemic drugs, cholesterol total, hemoglobin a1c, diabetes However, RCS analysis showed no non-curvilinear relationship between the TyG index and ICU mortality, all-cause mortality at 28-day, 90-day and 365-day in critically ill patients, as shown in Fig. 3 and Fig.S2 . Subgroup Analyses In addition, to confirm the association between the TyG index and the risk of SALI, in-hospital mortality, ICU mortality, and 28-day, 90-day, and 365-day mortality, stratified analyses were performed according to age, gender, race, diabetes, hypertension, congestive heart failure, chronic pulmonary disease, and renal disease. Subgroup analyses revealed no statistically significant interaction effects across most strata (P for interaction >0.05). There was a positive correlation between TyG index and SALI risk across subgroups with stable results ( Fig. 4A ). In the subgroup analysis between TyG index and all-cause mortality, there was no interaction in most subgroups, suggesting that the positive association between TyG index and all-cause mortality was also stable ( Fig. 4, Fig. S3 ). Discussion This retrospective cohort study, encompassing 4,343 critically ill patients with sepsis from the MIMIC-IV database, revealed a significant association between elevated TyG index and adverse clinical outcomes. Specifically, a higher TyG index was linearly associated with increased risks of SALI, ICU mortality, and 28-day, 90-day, and 365-day all-cause mortality. Notably, while the association between the TyG index and SALI, as well as long-term mortality, followed a linear dose-response pattern, the association with in-hospital mortality exhibited a nonlinear trend, suggesting potential threshold effects. These findings underscore the TyG index as a robust marker in critically ill patients, reflecting both metabolic dysregulation and unfavorable prognosis severity. Our findings indicating that TyG is associated with a high risk of SALI are consistent with previous studies emphasizing that TyG index is an independent risk factor for adverse clinical outcomes in critical illnesses [23,24]. Previous studies have consistently illustrated the association between an elevated TyG index and an increased risk of in-hospital mortality among critically ill patients. Specifically, Liao et al. indicated that each unit increase in the TyG index is correlated with an approximate 30% increase in the risk of mortality in hospitalized patients, reinforcing the predictive power of this index [25]. Furthermore, Zhang[23] et al. conducted a multicenter observational study that further substantiated the TyG index as an independent predictor of hospital and ICU mortality in patients suffering from critical conditions like stroke[26]. The association between the TyG index and all-cause mortality was supported by findings that extend the utility of this index into sepsis, a condition characterized by profound metabolic and inflammatory disturbances[27]. Shi et al. demonstrated that elevated TyG index levels correlate with severe outcomes in sepsis-associated encephalopathy, emphasizing its prognostic significance in a context where metabolic dysregulation is evident[28]. Interestingly, the linear relationship between the TyG index and long-term mortality observed in this study contrasts with its nonlinear association in the context of in-hospital mortality and provides clinicians with a new perspective. This divergence could be indicative of the complexities of the acute phase in critically ill settings. Notably, during early sepsis, fluctuations in glucose and lipid metabolism may confound the TyG index's predictive accuracy. Cheng et al. highlighted that acute illnesses could result in variations in stress-induced hyperglycemia, complicating the reliability of the TyG index across different time points in clinical scenarios[29]. The impact of immediate life-threatening complications, such as septic shock, could also dilute the TyG index’s utility in short-term predictive settings, suggesting that while the TyG index remains an excellent long-term prognostic marker, its immediate predictive value may be limited during acute worsening phases [30]. While the TyG index remains a relevant marker in assessing mortality risks across various critical conditions, the nuances of its predictive capacity warrant further investigation. The disparities between its short-term and long-term associations with mortality emphasize the need for tailored clinical approaches that consider the dynamic nature of metabolic processes during critical illness. The TyG index serves as a promising indicator for assessing insulin resistance (IR), which contributes to adverse clinical outcomes through several interconnected pathways. It is recognized that IR exacerbates systemic inflammation and oxidative stress, both of which are crucial factors in the progression of sepsis. Elevated cytokine levels, particularly interleukins such as IL-6, are associated with endothelial dysfunction and the cytokine storms that characterize sepsis. These processes lead to significant organ dysfunction, as cytokines can activate endothelial cells and promote inflammatory responses[31–33]. Chronic hyperglycemia and dyslipidemia, often resulting from insulin resistance, further aggravate endothelial impairment, complicating the clinical management of septic patients by fostering a vicious inflammatory cycle[34,35]. Moreover, SALI patients is likely impacted by IR-driven lipotoxicity and mitochondrial dysfunction. The TyG index has been shown to correlate with hepatic steatosis, which can compound the severity of liver injury during sepsis[36]. In patients with sepsis, higher TyG levels correlate with elevated SOFA scores, underscoring the amplifying effect of metabolic disturbances on multi-organ failure[37,38]. IR manifests as a significant contributor to hepatic stress responses, complicating the liver's function and its capacity to respond to sepsis adequately. The concurrent presence of cardiovascular disease and sepsis can significantly elevate mortality risks, as patients may experience compounded effects from both conditions [39]. Understanding the link between insulin resistance and cardiovascular outcomes can illuminate the pathways through which TyG index elevations may predetermine patient prognoses in critically ill settings. Consequently, the TyG index reflects acute metabolic stressors, revealing its potential utility as a prognostic marker in septic patients. In summary, the TyG index serves not only as a measure of insulin resistance but also as an indicator of broader metabolic derangements that can exacerbate inflammatory responses in sepsis. By integrating insights from the literature on cytokine signaling, endothelial dysfunction, and multi-organ failure, it is evident that managing insulin resistance in septic patients could be vital for improving clinical outcomes. The TyG index, derived from triglyceride and glucose measurements, is gaining recognition as a valuable clinical tool for early risk stratification in sepsis patients. Its utility lies in empowering clinicians to identify high-risk patients who may benefit from intensified monitoring or personalized interventions. Evidence suggests that patients with elevated TyG index levels are at a heightened risk of mortality, particularly in the context of sepsis [24,40–42]. By pinpointing such high-risk individuals, healthcare providers can implement tailored approaches, optimize glycemic control or integrating anti-inflammatory therapies to enhance patient outcomes[43]. Moreover, the TyG index exhibits a nonlinear relationship with in-hospital mortality, underscoring the necessity for dynamic assessments during the acute phase of sepsis. Researchers have noted that static baseline measurements may fail to accurately capture short-term risks, thereby hampering timely clinical decisions [44]. Continuous monitoring of the TyG index allows for a more nuanced understanding of a patient's evolving condition, leading to more precise interventions[42,45]. The significance of this dynamic assessment is highlighted by studies demonstrating the association between high TyG levels and adverse outcomes such as increased mortality associated with sepsis and critical illness[42,46,47]. In a bid to enhance prognostic accuracy in critical care settings, integrating the TyG index with established severity scores, such as SOFA or Acute Physiology and Chronic Health Evaluation (APACHE II), represents a promising approach. This combination could facilitate better resource allocation within ICUs, as previous research indicates that the TyG index enhances the predictive capacity of mortality in critically ill patients [41,46,48]. By establishing a comprehensive risk assessment framework, clinicians can better prepare for potential adverse events, tailor interventions, and ultimately improve patient outcomes. Therefore, the incorporation of the TyG index assists in risk stratification and encourages dynamic patient management strategies that are crucial for improving prognosis in sepsis and other critical conditions. Limitations Several limitations existed in this research. Firstly, causal inferences couldn't be made due to the retrospective design. Secondly, the TyG index was obtained only at ICU admission, failing to reflect the dynamic disease progression. Future research could explore the dynamic changes of the TyG index. Thirdly, the lack of detailed diet and lifestyle data in the MIMIC-IV database restricted the analysis of modifiable risk factors. However, this study had a large sample size and was rigorously adjusted for confounders to minimize bias. Lastly, the single-center nature of MIMIC-IV might limit its applicability to diverse populations. Large-scale prospective studies are needed in the future to explore the key mechanisms of insulin resistance. Conclusions In critically ill patients, an elevated TyG index is positively correlated with increased SALI risk and all-cause mortality. It's an independent risk factor for both SALI occurrence and all-cause mortality in critical illness, and it has a nonlinear correlation with in-hospital mortality. However, other large-scale prospective studies will be needed in the future to validate these findings. Abbreviations SALI, Sepsis-associated liver injury IR, Insulin resistance TyG, Triglyceride-glucose MIMIC-IV, Medical Information Mart for Intensive Care IV ICU, Intensive care unit STROBE, Strengthening the reporting of observational studies in epidemiology SAPS II, Simplified Acute Physiology Score II OASIS, Oxford acute severity of illness score TG, Triglyceride FBG, Fasting blood glucose OR, Odds Ratio HR, Hazard ratio CI, Confidence interval SD, Standard deviation IQR, Interquartile range AKI, Acute kidney injury CRRT, continuous renal replacement therapy SOFA, sequential organ failure assessment RCS, Restricted Cubic Spline Declarations Acknowledgment We would like to express our gratitude to all the participants for their valuable contributions to this study. Data Availability Data are available from the MIMIC-IV database (https://mimic-iv.mit.edu/). Funding Medical and Health Research Project of Zhejiang Province, No.2023KY1044. Ethical Statement The study was approved by Massachusetts Institute of Technology Affiliates. (ID: 52390976). The study conformed to the provisions of the Declaration of Helsinki (revised in 2013). The studies involving human participants were reviewed and approved by the Institutional Review Board of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. The Medical Ethics Committee agreed to waive the ethics review because anonymous data was used in this study (ID: KY2025ML034). Author Contributions Concept and design: YJ, XY, and MZ. Acquisition, analysis, or interpretation of data: XY, HM, XS. Drafting of the manuscript: XY, XyY. Critical revision of the manuscript for important intellectual content: SH, TL. Statistical analysis: XY, XS. Supervision: YJ, XY. References Vincent J-L, Marshall JC, Ñamendys-Silva SA, François B, Martin-Loeches I, Lipman J et al. 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Cardiovasc Diabetol. 2024;23:111. Additional Declarations No competing interests reported. Supplementary Files Fig.S1KMcurve.pdf Fig.S2RCS.pdf Fig.S3forestplot.pdf SupplementaryFigurelegends.docx TableS1Missingdata.docx TableS2TheassociationbetweenTyGindexgroupsandmortality.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6703771","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471996832,"identity":"59697598-ffff-41cd-9955-3ffa1c72e3a6","order_by":0,"name":"Xiaona Yi","email":"","orcid":"","institution":"The Affiliated LiHuiLi Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Xiaona","middleName":"","lastName":"Yi","suffix":""},{"id":471996833,"identity":"bf5885ab-52dc-4ee7-a3f4-3d5bd00ac356","order_by":1,"name":"Xiaoya Yang","email":"","orcid":"","institution":"Hengshui People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoya","middleName":"","lastName":"Yang","suffix":""},{"id":471996834,"identity":"d916ace3-4841-42fc-bed5-df7ef0cf49ab","order_by":2,"name":"Xingkai Shen","email":"","orcid":"","institution":"The Affiliated LiHuiLi Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Xingkai","middleName":"","lastName":"Shen","suffix":""},{"id":471996836,"identity":"3a07e362-02f4-41f3-95e1-54a2f111b7de","order_by":3,"name":"Shanshan Huang","email":"","orcid":"","institution":"The Affiliated LiHuiLi Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Huang","suffix":""},{"id":471996839,"identity":"5385bd38-875e-4aea-a9d1-0f333f3cf331","order_by":4,"name":"Meixia Zheng","email":"","orcid":"","institution":"The Affiliated LiHuiLi Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Meixia","middleName":"","lastName":"Zheng","suffix":""},{"id":471996841,"identity":"a98a7d00-7535-41d4-b852-9d8c644008a0","order_by":5,"name":"Haiyan Mao","email":"","orcid":"","institution":"The Affiliated LiHuiLi Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Mao","suffix":""},{"id":471996844,"identity":"1361e4d7-300b-4cc6-8607-8dbda963efb1","order_by":6,"name":"Tong Lin","email":"","orcid":"","institution":"The Affiliated LiHuiLi Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Lin","suffix":""},{"id":471996845,"identity":"e2d28690-6186-4254-8f9b-8cadb75c47a9","order_by":7,"name":"Yuhong Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACxmYGBmaGCgk5NvbGxgcfiNdyxsaYj+dws+EMYm1iZmxLS5wnkd4mzUGU8nbeY9IFbIcZ2yQfNkgzMNjJ6TYQdBhfmvQMnsPMbNKJDcYFDMnGZgcIauExk+aROMwG0pI8g+FA4jbitBgc5mGTPNhwmId4LQlpEmwSjI3NxGoxtuY5YGPAxpPYzDjDgAi/GPafMbzN+0+ifn778ec/PlTYyRHW0sDAIoHgGhBQDgLywKghKpmMglEwCkbBCAYAxGA7kDaMhPEAAAAASUVORK5CYII=","orcid":"","institution":"The Affiliated LiHuiLi Hospital of Ningbo University","correspondingAuthor":true,"prefix":"","firstName":"Yuhong","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2025-05-20 05:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6703771/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6703771/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84857614,"identity":"5df3b04b-fc91-46b4-a910-3a741578cb42","added_by":"auto","created_at":"2025-06-18 06:25:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100612,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study. MIMIC-IV, Medical Information Mart for Intensive Care IV; ICU, Intensive Care Unit\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6703771/v1/c147c0a1686fb5d61c9a10a0.png"},{"id":84858455,"identity":"8e500db9-32c7-42aa-90b4-c7f34a6eab9b","added_by":"auto","created_at":"2025-06-18 06:33:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":266153,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative Incidence of SALI and Kaplan-Meier Survival Plot for all-cause mortality. \u003cstrong\u003eA\u003c/strong\u003e Risk of SALI; \u003cstrong\u003eB\u003c/strong\u003e, In-hospital mortality;\u003cstrong\u003e C\u003c/strong\u003e, ICU mortality. TyG index: T1 (7.30, 8.60), T2 (8.60, 9.22), T3 (9.22, 11.98)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6703771/v1/750f8875400cb2424d016bb8.png"},{"id":84857621,"identity":"11b8a69b-f248-4142-8b3f-78bd471c45d6","added_by":"auto","created_at":"2025-06-18 06:25:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":241716,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between the TyG index and outcomes in critically ill patients. \u003cstrong\u003eA\u003c/strong\u003e Risk of SALI; \u003cstrong\u003eB\u003c/strong\u003e In-hospital mortality; \u003cstrong\u003eC\u003c/strong\u003e ICU mortality. Solid and dashed lines represent the predicted values and 95% confidence intervals, respectively. TyG index, triglyceride-glucose index\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6703771/v1/406888b3be58ff488eb77323.png"},{"id":84858456,"identity":"bec03d89-5574-4c4e-93e1-0b155f3125fd","added_by":"auto","created_at":"2025-06-18 06:33:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":210294,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between TyG index and outcomes in critically ill patients. \u003cstrong\u003eA\u003c/strong\u003e Risk of SALI; \u003cstrong\u003eB\u003c/strong\u003ein-hospital mortality;\u003cstrong\u003e C\u003c/strong\u003e ICU mortality. OR: Odds Ratio; HR, hazard ratio; CI, confidential interval.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6703771/v1/3ebada4134b3fcf5b281eff4.png"},{"id":101753840,"identity":"54780d05-35d7-4792-95d6-e2431c65d276","added_by":"auto","created_at":"2026-02-03 10:40:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2112087,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6703771/v1/98b8798c-104b-48e2-b5a5-b44293b31c31.pdf"},{"id":84857616,"identity":"7b52efcd-6cbc-4a67-9cfc-f049ba8ed8fb","added_by":"auto","created_at":"2025-06-18 06:25:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":346178,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1KMcurve.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6703771/v1/f5ee7348e629fb02a8d56b52.pdf"},{"id":84857619,"identity":"e843abc9-b169-4a29-b79e-89f2c1ba37ec","added_by":"auto","created_at":"2025-06-18 06:25:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":514968,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S2RCS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6703771/v1/3bb8d2db12615aa006e1139c.pdf"},{"id":84858459,"identity":"67d62aa1-b071-48c1-b772-d4fd977ae73d","added_by":"auto","created_at":"2025-06-18 06:33:33","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":597159,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S3forestplot.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6703771/v1/85d2db1fc0114b4f4f468773.pdf"},{"id":84857627,"identity":"d081ce11-509e-4076-a162-5e4a4ce227fb","added_by":"auto","created_at":"2025-06-18 06:25:32","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14281,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigurelegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-6703771/v1/3e28e7324e5d756bb1ca0622.docx"},{"id":84857622,"identity":"217e580a-24fe-4ae2-9f7b-920ad411c462","added_by":"auto","created_at":"2025-06-18 06:25:32","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18670,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1Missingdata.docx","url":"https://assets-eu.researchsquare.com/files/rs-6703771/v1/4e40912d5f5ccd9a54025019.docx"},{"id":84858461,"identity":"cbd82966-a73f-4cc7-8533-b79e37066cf8","added_by":"auto","created_at":"2025-06-18 06:33:33","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":21234,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2TheassociationbetweenTyGindexgroupsandmortality.docx","url":"https://assets-eu.researchsquare.com/files/rs-6703771/v1/058ba5d77cc872ebd28c3ace.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Triglyceride-Glucose Index and Risk of Sepsis-Associated Liver Injury and Mortality in Critically Ill Patients: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePatients in intensive care units (ICUs) are statistically associated with elevated mortality rates, which imposes a significant burden on families and society at large[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A critical condition influencing mortality outcomes in this setting is sepsis, defined as a life-threatening organ dysfunction resulting from a dysregulated host response to infections[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the context of sepsis, the liver undergoes a series of changes, including altered immune response, disruption of metabolic pathways, coagulation abnormalities, and impaired microvascular dynamics[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Collectively, these changes lead to hepatic dysfunction and ultimately to sepsis-associated liver injury (SALI). Systemic inflammation, hepatocyte apoptosis, and the resulting disturbances in glucose-fat metabolism during SALI can exacerbate the patient's clinical status[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, it has been documented that the incidence of jaundice or other liver dysfunction markers in ICU patients correlates with significantly grim prognoses, where mortality rates can soar above 60% in cases of pronounced liver injury[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Given the elevated mortality rate observed in patients diagnosed with SALI, this condition is regarded as a grave and potentially fatal health threat. Early recognition and appropriate management are essential to improve the prognosis of SALI. It has been shown that insulin resistance (IR) may exacerbate hepatic dysfunction in patients with sepsis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIR refers to a condition in which the body's tissues have a reduced sensitivity to insulin, resulting in an impaired ability of insulin to effectively promote glucose uptake and utilization. IR is considered a critical contributor to various metabolic disorders, including sepsis, type 2 diabetes mellitus (T\u003csub\u003e2\u003c/sub\u003eDM) and cardiovascular diseases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Research has identified several underlying mechanisms contributing to insulin resistance. One notable mechanism involves the accumulation of ectopic lipids in tissues such as the liver and muscles, which disrupts insulin signaling pathways. Studies have shown that lipid accumulation can lead to the activation of pro-inflammatory pathways, impairing insulin effectiveness[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In the liver, insulin typically promotes glucose uptake and lipid synthesis. However, in insulin-resistant states, there is a paradoxical increase in hepatic glucose production due to failed insulin signaling, leading to hyperglycemia and dyslipidemia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, the presence of inflammatory mediators has been recognized as a significant factor influencing insulin action, highlighting the strong association between inflammation and IR [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The triglyceride-glucose (TyG) index has garnered significant attention as a predictor of IR and related metabolic disorders.\u003c/p\u003e \u003cp\u003eTyG index is a novel biomarker calculated from fasting blood glucose and triglyceride levels. Emerging evidence suggests that this index is not only a determinant of glycemic control in type 2 diabetes but also has implications for a variety of adverse outcomes. The TyG index has been shown to correlate with several diseases, such as the risk of AKI, the mortality of sepsis and ischemic stroke[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], emphasizing its potential as a risk marker. However, the association between the TyG index and the risk of developing SALI in critically ill patients is unclear.\u003c/p\u003e \u003cp\u003eThis study aims to investigate the association between the TyG index and the risk of SALI, as well as its impact on in-hospital mortality, ICU mortality, and mortality at 28 days, 90 days, and 365 days.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study utilized the Medical Information Mart for Intensive Care IV (MIMIC-IV, Version 3.1) database, a publicly accessible repository containing de-identified clinical data from critically ill patients admitted to the Beth Israel Deaconess Medical Center (Boston, MA, USA) between 2008 and 2022. Access to the database was granted after completing the Collaborative Institutional Training Initiative (CITI) certification (No. 52390976). Ethical approval, including a waiver of informed consent, was obtained from the Institutional Review Boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eAdult patients (\u0026thinsp;≧\u0026thinsp;18years) who were hospitalized for the first time and admitted to the ICU for the first time were included in this study[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Exclusion criteria comprised: (1) multiple hospital admissions; (2) missing triglyceride or fasting blood glucose measurements within 24 hours of ICU admission; (3) ICU stay\u0026thinsp;\u0026lt;\u0026thinsp;24 hours; (4) outliers. A total of 4,343 patients met eligibility criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eExposure and Outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary exposure was the TyG index, calculated as: TyG index\u0026thinsp;=\u0026thinsp;ln [fasting TG (mg/dL) \u0026times; FBG (mg/dL)] / 2[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Triglyceride and glucose levels were obtained from the first laboratory tests after ICU admission.\u003c/p\u003e \u003cp\u003eThe primary outcome was SALI, defined according to the guidelines of the Surviving Sepsis Campaign[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]: patients who met the sepsis 3.0 criteria with INR\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and a total bilirubin level\u0026thinsp;\u0026gt;\u0026thinsp;2 mg/dL (34.2 \u0026micro; mol/L), whereas sepsis was defined as a diagnosis that met the sepsis 3.0 diagnosis or an increase of the sofa\u0026thinsp;\u0026ge;\u0026thinsp;2.\u003c/p\u003e \u003cp\u003eSecondary outcomes included: In-hospital mortality, ICU mortality, 28-day, 90-day, and 365-day all-cause mortality, ascertained via hospital records and Social Security Administration databases.\u003c/p\u003e\n\u003ch3\u003eVariable Extraction\u003c/h3\u003e\n\u003cp\u003eData were extracted using Structured Query Language (SQL) via Postgres (v13.7.2) and Navicat Premium (v17), encompassing demographics (age, sex, race, height, weight), medical history (hypertension, diabetes, congestive heart failure, chronic pulmonary disease, etc.), laboratory parameters [white blood cell count(WBC), blood urea nitrogen(BUN), high-density lipoprotein(HDL), low-density lipoprotein(LDL), prothrombin time(PT), activated partial thromboplastin time(PTT), alanine transaminase(ALT), aspartate transaminase(AST), alkaline phosphatase(ALP), cholesterol total(TC), hemoglobin a1c(HbA1c), triglycerides(TG), etc.], medications (insulin, Lipid lowering drugs, Hypoglycemic drugs, etc.), interventions [mechanical ventilation(MV), vasoactive drugs, continuous renal replacement therapy(CRRT)], severity scores [oxford acute severity of illness score (OASIS); acute physiology score II (APSII); simplified acute physiology score II (SAPS II); sequential organ failure assessment (SOFA), etc.], vital signs [heart rate(HR), respiratory rate(RR), systolic blood pressure(SBP), diastolic blood pressure(DBP), mean arterial pressure(MAP); peripheral capillary oxygen saturation(SPO\u003csub\u003e2\u003c/sub\u003e), and pulse oximetry-derived oxygen saturation(SPO\u003csub\u003e2\u003c/sub\u003e), etc.], and survival outcomes. Laboratory values were recorded within the first 24 hours of ICU admission.\u003c/p\u003e \u003cp\u003eVariables with \u0026gt;\u0026thinsp;60% missing data were excluded to ensure analytical stability, consistent with prior methodologies[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Missing data were generated using multiple interpolation by generating five estimated datasets, and the missing data are shown in \u003cem\u003eSupplementary Table\u0026nbsp;1.\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eParticipants were stratified into tertiles based on the TyG index (T1\u0026ndash;T3) and further categorized by survival status. Baseline characteristics were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range, IQR) for continuous variables and counts (%) for categorical variables. Group comparisons employed Fisher\u0026rsquo;s exact test or Pearson\u0026rsquo;s chi-square test, as appropriate.\u003c/p\u003e \u003cp\u003eThe association between the TyG index and the risk of SALI was assessed using the cumulative incidence rate, whereas the association between the TyG index and all-cause mortality was assessed using the Kaplan-Meier survival curves with log-rank tests. The association between TyG index and SALI risk was assessed applying logistic multifactor analysis. Cox proportional hazards models generated hazard ratios (HRs) and 95% confidence intervals (CIs) for TyG index (analyzed both continuously and categorically by tertiles). The inclusion of a covariate in the model was deemed appropriate if its addition resulted in a change of at least 10% in the initial regression coefficients, or if it was deemed necessary based on prior findings and clinical considerations. The variance spreading factor (VIF) was used to determine whether a collinear relationship existed. VIF\u0026thinsp;\u0026gt;\u0026thinsp;2 indicates a collinear association. Three adjustment models were constructed: Model 1: Unadjusted. Model 2: Adjusted for age, sex, race, year group. Model 3: Further adjusted for HR, SBP, DBP, MAP; SPO\u003csub\u003e2\u003c/sub\u003e, RR, temperature, WBC, hematocrit, hemoglobin, platelets, BUN, creatinine, albumin, HDL, LDL, calcium, magnesium, potassium, sodium, phosphate, chloride, PT, PTT, ALT, AST, ALP, TC, HbA1c, PCO\u003csub\u003e2\u003c/sub\u003e, PO\u003csub\u003e2\u003c/sub\u003e, hypertension, congestive heart failure, chronic pulmonary disease, insulin, lipid lowering drugs, hypoglycemic drugs, diabetes. Tertile groupings were also tested for trends.\u003c/p\u003e \u003cp\u003eRestricted Cubic Spline (RCS) was used to assess whether there was a nonlinear association between TyG index and outcomes indicators. Subgroup analyses grouped sex, age (\u0026lt;\u0026thinsp;65 or \u0026ge;\u0026thinsp;65 years), race, diabetes, hypertension, congestive heart failure, chronic pulmonary disease, and renal disease and assessed whether there was an interaction. Subgroup analyses are presented as forest plots.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using R version 4.2.2(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, R Foundation) and Free Statistics software (version 2.0). Statistical significance was set at \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 4,343 participants were stratified into tertiles based on the TyG index (T1: lowest, T3: highest). Significant differences were observed across groups for most baseline variables. Participants in the T3 group were younger, had higher BMI, and were more likely to be male. Racial distribution varied, with fewer White individuals in T3. The incidence of SALI (sepsis-associated liver injury) was highest in T3 (13.6% vs. 8.9% in T1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mortality rates escalated with increasing TyG tertiles. The T3 group exhibited the highest in-hospital mortality, ICU mortality, 28-day mortality, 90-day mortality, and 365-day mortality. Resource utilization metrics followed this trend: T3 patients had prolonged hospital LOS (median 10.3 vs. 7.7 days) and ICU LOS (4.6 vs. 3.2 days, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Laboratory parameters revealed a pronounced dysmetabolic phenotype in T3, characterized by elevated glucose, triglycerides, and HbA1c, alongside reduced HDL. Inflammatory markers (WBC, lactate) and organ dysfunction indices (ALT, creatinine) were significantly elevated in T3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Acid-base derangements, including lower pH and bicarbonate, were also prominent (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Comorbid diabetes and acute pancreatitis were overrepresented in T3. Paradoxically, hypertension prevalence decreased across tertiles. Disease severity scores systematically increased with TyG tertiles: APSIII, SAPSII, and SOFA scores. T3 patients required more intensive therapies, including MV, vasoactive agents, and CRRT. Insulin use was markedly higher in T3, consistent with their metabolic profile (\u003cstrong\u003eTable\u0026nbsp;1\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;1\u003c/strong\u003e Baseline characteristics and outcomes of participants classified by TyG index tertiles\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"665\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N = 4343)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(7.30, 8.60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eT2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(8.60, 9.22)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eT3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(9.22, 11.98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN = 1448\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN = 1446\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN = 1449\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.0 (52.5, 76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68.0 (55.0, 81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.0 (54.0, 77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.0 (49.0, 70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2484 (57.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e786 (54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e823 (56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e875 (60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1859 (42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e662 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e623 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e574 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRace, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2412 (55.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e839 (57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e824 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e749 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1931 (44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e609 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e622 (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e700 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYear Group, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;2008-2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e649 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e236 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e233 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;2011-2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e605 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e197 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e222 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e186 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;2014-2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e748 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e270 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e238 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e240 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;2017-2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1073 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e379 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e356 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e338 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;2020-2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1268 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e422 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e394 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e452 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.5 (24.7, 33.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.8 (23.3, 30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.0 (24.5, 32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.8 (26.2, 36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSALI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e466 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e129 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e140 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e197 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIn-hospital mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e676 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e161 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e204 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e311 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eICU mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e483 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e137 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e241 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e28-day mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e801 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e222 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e251 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e328 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e90-day mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1009 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e290 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e323 (22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e396 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e365-day mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1252 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e367 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e424 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e461 (31.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLos-hospital, day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.9 (4.7, 16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.7 (4.2, 13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.1 (4.8, 16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.3 (5.1, 20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLos-ICU, day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.7 (2.0, 7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.2 (1.9, 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.6 (2.0, 7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.6 (2.2, 10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVital signs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeart rate, bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102.0 (88.0, 117.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98.0 (86.0, 113.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e101.0 (89.0, 115.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106.0 (92.0, 121.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95.0 (84.0, 109.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98.0 (86.0, 111.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96.0 (85.0, 109.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.0 (82.0, 106.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.0 (43.0, 59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.0 (44.0, 60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.0 (43.0, 59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49.0 (42.0, 57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMAP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.0 (56.0, 73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66.0 (58.0, 75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65.0 (56.0, 74.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.0 (54.0, 71.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRespirates, bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.0 (24.0, 32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.0 (24.0, 30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.0 (24.0, 31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.0 (25.0, 33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTemperature, ℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.6 (36.3, 36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.5 (36.3, 36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.6 (36.3, 36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.6 (36.3, 36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSPO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.0 (90.0, 95.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.0 (91.0, 95.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.0 (90.0, 95.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92.0 (90.0, 94.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory tests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHematocrit, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.4 (28.8, 39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.9 (29.7, 39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.6 (28.9, 39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.6 (27.9, 38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.3 (9.4, 13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.5 (9.8, 13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.4 (9.4, 13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.0 (9.1, 12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePlatelets, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e187.0 (139.0, 243.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e186.0 (142.0, 241.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e194.0 (147.0, 245.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e181.0 (127.0, 245.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWBC, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.0 (8.8, 16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.4 (7.9, 14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.0 (9.0, 16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.8 (10.1, 18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBUN, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.0 (13.0, 28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.0 (12.0, 23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.0 (13.0, 27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.0 (14.0, 35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0 (0.7, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9 (0.7, 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0 (0.7, 1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.1 (0.8, 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eT-Bil, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6 (0.4, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6 (0.4, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6 (0.4, 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6 (0.4, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2 (1.1, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2 (1.1, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2 (1.1, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2 (1.1, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlbumin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.0 (2.5, 3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.3 (2.7, 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.1 (2.6, 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.8 (2.3, 3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlucose, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126.0 (103.0, 166.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106.0 (93.0, 124.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126.0 (105.0, 154.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e169.0 (129.0, 235.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTG, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e110.0 (78.0, 168.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71.0 (57.0, 87.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e113.0 (92.0, 138.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e207.0 (149.0, 309.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHemoglobin A1c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.7 (5.4, 6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.5 (5.2, 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.8 (5.4, 6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.3 (5.7, 8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTC, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e152.0 (121.0, 186.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e146.0 (117.0, 179.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e155.0 (124.0, 187.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e157.0 (123.0, 196.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHDL, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45.0 (35.0, 57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.0 (39.0, 64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45.0 (35.0, 54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.0 (30.0, 47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLDL, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.0 (57.0, 111.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.0 (56.0, 106.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.0 (60.0, 114.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82.0 (55.0, 117.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCalcium, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.6 (8.1, 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.7 (8.3, 9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.7 (8.2, 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.4 (7.8, 9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMagnesium, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0 (1.8, 2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.9 (1.8, 2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0 (1.8, 2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0 (1.7, 2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePotassium, m Eq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.1 (3.7, 4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.0 (3.7, 4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.1 (3.7, 4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.1 (3.8, 4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSodium, m Eq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e139.0 (136.0, 141.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e139.0 (136.0, 141.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e139.0 (136.0, 141.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e138.0 (135.0, 141.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhosphate, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.4 (2.9, 4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.4 (2.9, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.4 (2.8, 4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.6 (2.9, 4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAnion gap, m Eq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.0 (13.0, 18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.0 (13.0, 17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.0 (13.0, 18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17.0 (14.0, 20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBicarbonate, m Eq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.0 (19.0, 24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.0 (19.0, 24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.0 (19.0, 24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.0 (17.0, 23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChloride, m Eq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105.0 (102.0, 108.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105.0 (102.0, 108.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105.0 (102.0, 108.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105.0 (101.0, 109.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePT, s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.5 (12.2, 15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.3 (12.1, 15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.4 (12.2, 15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.6 (12.2, 16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePTT, s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.6 (27.8, 45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.4 (27.9, 43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.4 (27.7, 44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.1 (27.8, 48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALT, IU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.0 (16.0, 60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.0 (14.0, 44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.0 (16.0, 54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.0 (20.0, 89.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALP, IU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.0 (63.0, 107.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.0 (62.0, 100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.0 (62.0, 106.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.0 (65.0, 116.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAST, IU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.0 (22.0, 99.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.5 (20.0, 66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.0 (22.0, 90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.0 (26.0, 160.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLDH, IU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e312.5 (215.0, 545.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e252.0 (192.2, 385.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e293.0 (215.0, 525.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e409.0 (257.0, 723.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLactate, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.1 (1.3, 3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.9 (1.2, 3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.1 (1.3, 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.2 (1.4, 4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.3 (7.2, 7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.3 (7.3, 7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.3 (7.3, 7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.3 (7.2, 7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePO\u003csub\u003e2\u003c/sub\u003e, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.0 (61.0, 108.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.0 (61.0, 118.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.0 (62.0, 116.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.0 (60.0, 97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePCO\u003csub\u003e2,\u003c/sub\u003e mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44.0 (38.0, 52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.0 (37.0, 49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.0 (38.0, 50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46.0 (39.0, 54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBase Excess, m Eq/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.0 (-7.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.0 (-6.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.0 (-6.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.0 (-9.0, 0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1493 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e275 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e461 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e757 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2585 (59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e893 (61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e876 (60.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e816 (56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e944 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e256 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e336 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e352 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCHF, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1122 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e349 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e385 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e388 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePVD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e342 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e109 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCVD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2050 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e834 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e721 (49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e495 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCPD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e811 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e253 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e260 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e298 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eParaplegia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1141 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e480 (33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e390 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e271 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e710 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e182 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e244 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e284 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMC, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e380 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e118 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e139 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMST, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e156 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCCI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.0 (3.0, 8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSepsis, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2224 (51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e576 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e697 (48.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e951 (65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAKI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2958 (68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e905 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e974 (67.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1079 (74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eScoring systems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAPS III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44.0 (31.0, 65.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.0 (28.0, 53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.0 (30.0, 61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55.0 (38.0, 83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSAPS II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.0 (26.0, 44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.0 (25.0, 40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.0 (25.0, 43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.0 (27.0, 49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOASIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.0 (27.0, 41.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.0 (26.0, 37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.0 (27.0, 40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.0 (30.0, 45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSOFA score,\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0 (0.0, 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0 (0.0, 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0 (0.0, 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0 (0.0, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eInterventions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVasoactive agents, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1195 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e287 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e375 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e533 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRRT, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e330 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e209 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMV, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1867 (43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e417 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e592 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e858 (59.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLipid lowering drugs, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2574 (59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e837 (57.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e891 (61.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e846 (58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInsulin, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1743 (40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e397 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e540 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e806 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypoglycemic drugs, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e277 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e160 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eAbbreviations: LOS, length of stay; SBP, systolic blood pressure; DBP, diastolic blood pressure; MAP, mean arterial pressure; SpO2, peripheral capillary oxygen saturation; PCO\u003csub\u003e2,\u003c/sub\u003e partial pressure of carbon dioxide; PO\u003csub\u003e2,\u003c/sub\u003e partial pressure of oxygen; WBC, white blood cell counts; TC, Cholesterol total; TG, Triglycerides; T-Bil, bilirubin total; BUN, blood urine nitrogen; ALT, alanine transaminase; AST, aspartate transaminase; ALP, alkaline phosphatase; LDH, lactate dehydrogenase; HDL, high-density lipoprotein; LDL, low-density lipoprotein; INR, international normalized ratio; PT, prothrombin time; PTT, activated partial thromboplastin time; AP, Acute Pancreatitis; AF, Atrial Fibrillation; CKD, Chronic Kidney Disease; MI, Myocardial Infarct; CHF, Congestive Heart Failure; PVD, Peripheral Vascular Disease; CVD, Cerebrovascular Disease; CPD, Chronic Pulmonary Disease; RD, Renal Disease; MC, Malignant Cancer; MST, Metastatic Solid Tumor; CCI, Charlson Comorbidity Index; AKI, Acute kidney injury; OASIS, oxford acute severity of illness score; APSII, acute physiology score II; SAPS II, simplified acute physiology score II; SOFA, sequential organ failure assessment; CRRT, continuous renal replacement therapy; MV, mechanical ventilation. Lipid lowering drugs include Cholestyramine, Ezetimibe, Fenofibrate, Gemfibrozil, Atorvastatin, Lovastatin, Pravastatin, Rosuvastatin Calcium, Simvastatin; Hypoglycemic drugs include Metformin, glimepiride, glipizide, pioglitazone, repaglinide, rosiglitazone.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTyG Index and SALI Risk\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn this study, multivariable logistic regression analyses revealed a significant positive association between the TyG index and the risk of SALI across adjusted models. When analyzed as a continuous variable, each unit increase in TyG index was associated with elevated SALI risk in all models (Model 1: OR=1.27, 95%CI 1.13-1.42; Model 2: OR=1.13, 95%CI 1.00-1.27; Model 3: OR=1.52, 95%CI 1.12-2.08), with all \u003cem\u003eP\u003c/em\u003e-values \u0026lt;0.05. When categorized into tertiles, participants in the highest TyG tertile (T3: 9.22-11.98) demonstrated significantly higher SALI risk compared to the lowest tertile (T1) in unadjusted [OR 1.61, (1.27-2.04) ]and demographically adjusted models [OR 1.37, (1.07-1.74)], though this association attenuated after full adjustment for clinical covariates in Model 3 (OR=1.58, 95%CI 0.96-2.59) (\u003cstrong\u003eTable 2\u003c/strong\u003e). The cumulative incidence graph for SALI is shown in \u003cstrong\u003eFig. 2A\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn addition, the RCS suggested a linear association between the level of TyG index and the risk of SALI in critically ill patients (p nonlinear = 0.912), as shown in \u003cstrong\u003eFig. 3\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eThe association between TyG index groups and the risk of SALI\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12.9457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 6.5255%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7.4727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22.3129%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20.7341%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 21.8919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4199%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0507%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.9457%;\"\u003e\n \u003cp\u003eTyG continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5255%;\"\u003e\n \u003cp\u003e4343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.4727%;\"\u003e\n \u003cp\u003e466 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e1.27 (1.13~1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4199%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e1.13 (1~1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0507%;\"\u003e\n \u003cp\u003e1.52 (1.12~2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.9457%;\"\u003e\n \u003cp\u003eTyG tertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5255%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.4727%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.9457%;\"\u003e\n \u003cp\u003eT1 (7.30, 8.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5255%;\"\u003e\n \u003cp\u003e1448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.4727%;\"\u003e\n \u003cp\u003e129 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4199%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0507%;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.9457%;\"\u003e\n \u003cp\u003eT2 (8.60, 9.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5255%;\"\u003e\n \u003cp\u003e1446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.4727%;\"\u003e\n \u003cp\u003e140 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e1.1 (0.85~1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4199%;\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e1.1 (0.85~1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0507%;\"\u003e\n \u003cp\u003e1.3 (0.88~1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.9457%;\"\u003e\n \u003cp\u003eT3 (9.22, 11.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5255%;\"\u003e\n \u003cp\u003e1449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.4727%;\"\u003e\n \u003cp\u003e197 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e1.61 (1.27~2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4199%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e1.37 (1.07~1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0507%;\"\u003e\n \u003cp\u003e1.58 (0.96~2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.9457%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5255%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.4727%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.4199%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.8929%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0507%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.8412%;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eOR: Odds Ratio; CI: confidential interval\u003c/p\u003e\n \u003cp\u003eModel 1: unadjusted\u003c/p\u003e\n \u003cp\u003eModel 2: adjusted for age, sex, race, year group\u003c/p\u003e\n \u003cp\u003eModel 3: adjusted for age, sex, race, year group, heartrate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, respiration rate, temperature, peripheral capillary oxygen saturation, white blood cell count, hematocrit, hemoglobin, platelets, blood urea nitrogen, creatinine, albumin, cholesterol total, high-density lipoprotein, low-density lipoprotein, lactate dehydrogenase, calcium, magnesium, potassium, sodium, phosphate, chloride, prothrombin time, activated partial thromboplastin time, alanine transaminase, aspartate transaminase, alkaline phosphatase, lactate, pH, partial pressure of carbon dioxide, partial pressure of oxygen, hemoglobin a1c, diabetes, hypertension, congestive heart failure, chronic pulmonary disease, renal disease, Charlson comorbidity index, insulin, lipid-lowering drugs, hypoglycemic drugs, mechanical ventilation, vasoactive agents\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTyG Index and All-cause Mortality \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eA Kaplan - Meier survival analysis was performed to compare all-cause mortality in patients based on TyG index tertiles. Patients with higher TyG index had significantly higher in-hospital mortality, ICU mortality, and all-cause mortality at 28-day, 90-day, and 365-days than those with lower TyG index (\u003cstrong\u003eFig. 2\u003c/strong\u003e,\u003cem\u003e\u0026nbsp;Fig. S1\u003c/em\u003e).\u003c/p\u003e\n \u003cp\u003eAs shown in \u003cstrong\u003eTable 3\u003c/strong\u003e, the elevated TyG index was significantly associated with increased risks of all-cause mortality across multiple time points. When analyzed as a continuous variable, each unit increase in TyG index consistently predicted higher mortality risks in unadjusted and adjusted models, with hazard ratios (HRs) ranging from 1.15 to 1.62 (all \u003cem\u003eP\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003efor trend \u0026lt;0.001). In tertile-based analyses, the highest TyG tertile exhibited the strongest mortality risk compared to the lowest tertile. For example, in fully adjusted Model 3, T3 demonstrated HRs of 2.05 (CI:1.57\u0026ndash;2.69) for in-hospital mortality, 2.20 (1.60\u0026ndash;3.02) for ICU mortality, and 1.63 (1.33\u0026ndash;1.98) for 365-day mortality. A dose-response relationship was evident across tertiles (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt;0.001 in all models). Notably, these associations remained robust after adjusting demographics, vital signs, laboratory parameters, comorbidities, and medications (Model 3), suggesting TyG index is an independent risk factor for increased all-cause mortality. The strength of association attenuated over longer follow-up periods (e.g., 365-day mortality HR=1.63 for T3 vs. in-hospital HR=2.05) but remained statistically significant (\u003cem\u003eTable S3\u003c/em\u003e).\u003c/p\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eThe association between TyG index and all-cause mortality\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 5.1058%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-up Time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 8.7903%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 8.7903%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 8.7903%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 46.4783%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn-hospital mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.1058%;\"\u003e\n \u003cp\u003eTyG continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e4343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e676 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e1388321.51623629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1.42 (1.31~1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1.57 (1.43~1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1.6 (1.37~1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.1058%;\"\u003e\n \u003cp\u003eTyG tertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.1058%;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e1448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e161 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e486092.688635698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.1058%;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e1446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e204 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e464651.41691932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1.29 (1.05~1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1.36 (1.1~1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1.25 (1~1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.1058%;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e1449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e311 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e437577.41068127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e2.04 (1.68~2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e2.34 (1.92~2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e2.05 (1.57~2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.1058%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 46.4783%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICU mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.1058%;\"\u003e\n \u003cp\u003eTyG continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e4343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e483 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e1388321.51623629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1.53 (1.39~1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1.63 (1.47~1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1.62 (1.35~1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.1058%;\"\u003e\n \u003cp\u003eTyG tertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.1058%;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e1448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e105 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e486092.688635698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.1058%;\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e1446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e137 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e464651.41691932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1.32 (1.03~1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1.37 (1.06~1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e1.22 (0.93~1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.1058%;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e1449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e241 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e437577.41068127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e2.41 (1.91~3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e2.6 (2.06~3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e2.2 (1.6~3.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.1058%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.5792%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.9477%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.0009%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.4742%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.3161%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eTyG index: T1 (7.30, 8.60), T2 (8.60, 9.22), T3 (9.22, 11.98). HR: Odds Ratio; CI: confidential interval\u003c/p\u003e\n \u003cp\u003eModel 1: unadjusted\u003c/p\u003e\n \u003cp\u003eModel 2: adjusted for age, sex, race, year group\u003c/p\u003e\n \u003cp\u003eModel 3: adjusted for age, sex, race, year group, heartrate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, peripheral capillary oxygen saturation, respirates, temperature, white blood cell count, hematocrit, hemoglobin, platelets, blood urea nitrogen, creatinine, albumin, high-density lipoprotein, low-density lipoprotein, calcium, magnesium, potassium, sodium, phosphate, chloride, prothrombin time, activated partial thromboplastin time, alanine transaminase, aspartate transaminase, alkaline phosphatase, pH, partial pressure of carbon dioxide partial pressure of oxygen, hypertension, congestive heart failure, chronic pulmonary disease, insulin, lipid lowering drugs, hypoglycemic drugs, cholesterol total, hemoglobina1c, diabetes\u003c/p\u003e\n \u003cp\u003eRCS analysis revealed a nonlinear association between TyG index and in-hospital mortality (P for nonlinearity 0.042) (\u003cstrong\u003eFig.3A\u003c/strong\u003e). The piecewise Cox regression analysis revealed a nonlinear dose-response association between the TyG index and in-hospital mortality. The threshold was identified at 9.888 units (95% CI: 9.819-9.958) through maximum likelihood estimation. Below this threshold, each unit increase demonstrated a significant 86.4% elevation in risk (HR = 1.864, 95% CI: 1.489-2.335, P \u0026lt; 0.001). Above the threshold, the association attenuated substantially (HR = 1.222, 95% CI: 0.756-1.973, P = 0.413) without statistical significance. The likelihood ratio test confirmed superior model fits for the piecewise model over linear assumptions (\u003cem\u003eP\u003c/em\u003e 0.005), supported by significant nonlinearity testing (P = 0.014). These findings suggest the existence of a saturation effect beyond 9.888 units, where incremental exposure no longer confers proportional risk escalation (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eThreshold effect analysis of TyG index and in-hospital mortality\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"563\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eOne line effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e1.6 (1.37, 1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eTurning point (K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e9.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eTyG index \u0026lt; K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e1.864 (1.489,2.335)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eTyG index\u0026ge;K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e1.222 (0.756,1.973)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.4133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eLikelihood Ratio test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003eNon-linear Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 226px;\"\u003e\n \u003cp\u003e95% CI for turning point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e(9.819, 9.958)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eHR, hazard ratio; CI, confidential interval. Model 1, linear analysis; Model 2, non-linear analysis. Adjusted for age, sex, race, year group, heartrate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, peripheral capillary oxygen saturation, respirates, temperature, white blood cell count, hematocrit, hemoglobin, platelets, blood urea nitrogen, creatinine, albumin, high-density lipoprotein, low-density lipoprotein, calcium, magnesium, potassium, sodium, phosphate, chloride, prothrombin time, activated partial thromboplastin time, alanine transaminase, aspartate transaminase, alkaline phosphatase, pH, partial pressure of carbon dioxide，partial pressure of oxygen, hypertension, congestive heart failure, chronic pulmonary disease, insulin, lipid lowering drugs, hypoglycemic drugs, cholesterol total, hemoglobin a1c, diabetes\u003c/p\u003eHowever, RCS analysis showed no non-curvilinear relationship between the TyG index and ICU mortality, all-cause mortality at 28-day, 90-day and 365-day in critically ill patients, as shown in \u003cstrong\u003eFig. 3\u0026nbsp;\u003c/strong\u003eand\u003cem\u003e\u0026nbsp;Fig.S2\u003c/em\u003e.\u003cp\u003e\u003cstrong\u003eSubgroup Analyses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn addition, to confirm the association between the TyG index and the risk of SALI, in-hospital mortality, ICU mortality, and 28-day, 90-day, and 365-day mortality, stratified analyses were performed according to age, gender, race, diabetes, hypertension, congestive heart failure, chronic pulmonary disease, and renal disease.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSubgroup analyses revealed no statistically significant interaction effects across most strata (P for interaction \u0026gt;0.05). There was a positive correlation between TyG index and SALI risk across subgroups with stable results (\u003cstrong\u003eFig. 4A\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eIn the subgroup analysis between TyG index and all-cause mortality, there was no interaction in most subgroups, suggesting that the positive association between TyG index and all-cause mortality was also stable (\u003cstrong\u003eFig. 4,\u0026nbsp;\u003c/strong\u003e\u003cem\u003eFig. S3\u003c/em\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective cohort study, encompassing 4,343 critically ill patients with sepsis from the MIMIC-IV database, revealed a significant association between elevated TyG index and adverse clinical outcomes. Specifically, a higher TyG index was linearly associated with increased risks of SALI, ICU mortality, and 28-day, 90-day, and 365-day all-cause mortality. Notably, while the association between the TyG index and SALI, as well as long-term mortality, followed a linear dose-response pattern, the association with in-hospital mortality exhibited a nonlinear trend, suggesting potential threshold effects. These findings underscore the TyG index as a robust marker in critically ill patients, reflecting both metabolic dysregulation and unfavorable prognosis severity.\u003c/p\u003e\n\u003cp\u003eOur findings indicating that TyG is associated with a high risk of SALI are consistent with previous studies emphasizing that TyG index is an independent risk factor for adverse clinical outcomes in critical illnesses [23,24]. Previous studies have consistently illustrated the association between an elevated TyG index and an increased risk of in-hospital mortality among critically ill patients. Specifically, Liao et al. indicated that each unit increase in the TyG index is correlated with an approximate 30% increase in the risk of mortality in hospitalized patients, reinforcing the predictive power of this index [25]. Furthermore, Zhang[23] et al. conducted a multicenter observational study that further substantiated the TyG index as an independent predictor of hospital and ICU mortality in patients suffering from critical conditions like stroke[26].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe association between the TyG index and all-cause mortality was supported by findings that extend the utility of this index into sepsis, a condition characterized by profound metabolic and inflammatory disturbances[27]. Shi et al. demonstrated that elevated TyG index levels correlate with severe outcomes in sepsis-associated encephalopathy, emphasizing its prognostic significance in a context where metabolic dysregulation is evident[28]. Interestingly, the linear relationship between the TyG index and long-term mortality observed in this study contrasts with its nonlinear association in the context of in-hospital mortality and provides clinicians with a new perspective. This divergence could be indicative of the complexities of the acute phase in critically ill settings. Notably, during early sepsis, fluctuations in glucose and lipid metabolism may confound the TyG index's predictive accuracy. Cheng et al. highlighted that acute illnesses could result in variations in stress-induced hyperglycemia, complicating the reliability of the TyG index across different time points in clinical scenarios[29]. The impact of immediate life-threatening complications, such as septic shock, could also dilute the TyG index’s utility in short-term predictive settings, suggesting that while the TyG index remains an excellent long-term prognostic marker, its immediate predictive value may be limited during acute worsening phases [30]. While the TyG index remains a relevant marker in assessing mortality risks across various critical conditions, the nuances of its predictive capacity warrant further investigation. The disparities between its short-term and long-term associations with mortality emphasize the need for tailored clinical approaches that consider the dynamic nature of metabolic processes during critical illness.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The TyG index serves as a promising indicator for assessing insulin resistance (IR), which contributes to adverse clinical outcomes through several interconnected pathways. It is recognized that IR exacerbates systemic inflammation and oxidative stress, both of which are crucial factors in the progression of sepsis. Elevated cytokine levels, particularly interleukins such as IL-6, are associated with endothelial dysfunction and the cytokine storms that characterize sepsis. These processes lead to significant organ dysfunction, as cytokines can activate endothelial cells and promote inflammatory responses[31–33]. Chronic hyperglycemia and dyslipidemia, often resulting from insulin resistance, further aggravate endothelial impairment, complicating the clinical management of septic patients by fostering a vicious inflammatory cycle[34,35]. Moreover, SALI patients is likely impacted by IR-driven lipotoxicity and mitochondrial dysfunction. The TyG index has been shown to correlate with hepatic steatosis, which can compound the severity of liver injury during sepsis[36]. In patients with sepsis, higher TyG levels correlate with elevated SOFA scores, underscoring the amplifying effect of metabolic disturbances on multi-organ failure[37,38]. IR manifests as a significant contributor to hepatic stress responses, complicating the liver's function and its capacity to respond to sepsis adequately. The concurrent presence of cardiovascular disease and sepsis can significantly elevate mortality risks, as patients may experience compounded effects from both conditions [39]. Understanding the link between insulin resistance and cardiovascular outcomes can illuminate the pathways through which TyG index elevations may predetermine patient prognoses in critically ill settings. Consequently, the TyG index reflects acute metabolic stressors, revealing its potential utility as a prognostic marker in septic patients. In summary, the TyG index serves not only as a measure of insulin resistance but also as an indicator of broader metabolic derangements that can exacerbate inflammatory responses in sepsis. By integrating insights from the literature on cytokine signaling, endothelial dysfunction, and multi-organ failure, it is evident that managing insulin resistance in septic patients could be vital for improving clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The TyG index, derived from triglyceride and glucose measurements, is gaining recognition as a valuable clinical tool for early risk stratification in sepsis patients. Its utility lies in empowering clinicians to identify high-risk patients who may benefit from intensified monitoring or personalized interventions. Evidence suggests that patients with elevated TyG index levels are at a heightened risk of mortality, particularly in the context of sepsis [24,40–42]. By pinpointing such high-risk individuals, healthcare providers can implement tailored approaches, optimize glycemic control or integrating anti-inflammatory therapies to enhance patient outcomes[43]. Moreover, the TyG index exhibits a nonlinear relationship with in-hospital mortality, underscoring the necessity for dynamic assessments during the acute phase of sepsis. Researchers have noted that static baseline measurements may fail to accurately capture short-term risks, thereby hampering timely clinical decisions [44]. Continuous monitoring of the TyG index allows for a more nuanced understanding of a patient's evolving condition, leading to more precise interventions[42,45]. The significance of this dynamic assessment is highlighted by studies demonstrating the association between high TyG levels and adverse outcomes such as increased mortality associated with sepsis and critical illness[42,46,47]. In a bid to enhance prognostic accuracy in critical care settings, integrating the TyG index with established severity scores, such as SOFA or Acute Physiology and Chronic Health Evaluation (APACHE II), represents a promising approach. This combination could facilitate better resource allocation within ICUs, as previous research indicates that the TyG index enhances the predictive capacity of mortality in critically ill patients [41,46,48]. By establishing a comprehensive risk assessment framework, clinicians can better prepare for potential adverse events, tailor interventions, and ultimately improve patient outcomes. Therefore, the incorporation of the TyG index assists in risk stratification and encourages dynamic patient management strategies that are crucial for improving prognosis in sepsis and other critical conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations existed in this research. Firstly, causal inferences couldn't be made due to the retrospective design. Secondly, the TyG index was obtained only at ICU admission, failing to reflect the dynamic disease progression. Future research could explore the dynamic changes of the TyG index. Thirdly, the lack of detailed diet and lifestyle data in the MIMIC-IV database restricted the analysis of modifiable risk factors. However, this study had a large sample size and was rigorously adjusted for confounders to minimize bias. Lastly, the single-center nature of MIMIC-IV might limit its applicability to diverse populations. Large-scale prospective studies are needed in the future to explore the key mechanisms of insulin resistance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn critically ill patients, an elevated TyG index is positively correlated with increased SALI risk and all-cause mortality. It's an independent risk factor for both SALI occurrence and all-cause mortality in critical illness, and it has a nonlinear correlation with in-hospital mortality. However, other large-scale prospective studies will be needed in the future to validate these findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSALI, Sepsis-associated liver injury\u003c/p\u003e\n\u003cp\u003eIR, Insulin resistance\u003c/p\u003e\n\u003cp\u003eTyG, Triglyceride-glucose\u003c/p\u003e\n\u003cp\u003eMIMIC-IV, Medical Information Mart for Intensive Care IV\u003c/p\u003e\n\u003cp\u003eICU, Intensive care unit\u003c/p\u003e\n\u003cp\u003eSTROBE, Strengthening the reporting of observational studies in epidemiology\u003c/p\u003e\n\u003cp\u003eSAPS II, Simplified Acute Physiology Score II\u003c/p\u003e\n\u003cp\u003eOASIS, Oxford acute severity of illness score\u003c/p\u003e\n\u003cp\u003eTG, Triglyceride\u003c/p\u003e\n\u003cp\u003eFBG, Fasting blood glucose\u003c/p\u003e\n\u003cp\u003eOR, Odds Ratio\u003c/p\u003e\n\u003cp\u003eHR, Hazard ratio\u003c/p\u003e\n\u003cp\u003eCI, Confidence interval\u003c/p\u003e\n\u003cp\u003eSD, Standard deviation\u003c/p\u003e\n\u003cp\u003eIQR, Interquartile range\u003c/p\u003e\n\u003cp\u003eAKI, Acute kidney injury\u003c/p\u003e\n\u003cp\u003eCRRT, continuous renal replacement therapy\u003c/p\u003e\n\u003cp\u003eSOFA, sequential organ failure assessment\u003c/p\u003e\n\u003cp\u003eRCS, Restricted Cubic Spline\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to all the participants for their valuable contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available from the MIMIC-IV database (https://mimic-iv.mit.edu/). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMedical and Health Research Project of Zhejiang Province, No.2023KY1044.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by Massachusetts Institute of Technology Affiliates. (ID: 52390976). The study conformed to the provisions of the Declaration of Helsinki (revised in 2013). The studies involving human participants were reviewed and approved by the Institutional Review Board of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. The Medical Ethics Committee agreed to waive the ethics review because anonymous data was used in this study (ID: KY2025ML034).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept and design: YJ, XY, and MZ. Acquisition, analysis, or interpretation of data: XY, HM, XS. Drafting of the manuscript: XY, XyY. Critical revision of the manuscript for important intellectual content: SH, TL. Statistical analysis: XY, XS. 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Cardiovasc Diabetol. 2024;23:111.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[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":"Triglyceride-glucose index, Sepsis-associated liver injury, Mortality, Insulin resistance, MIMIC database","lastPublishedDoi":"10.21203/rs.3.rs-6703771/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6703771/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e The triglyceride-glucose (TyG) index, a surrogate marker of insulin resistance, has been implicated in metabolic dysregulation and adverse clinical outcomes. However, its association with sepsis-associated liver injury (SALI) and mortality in critically ill patients remains unclear. This study aimed to investigate the association between the TyG index and SALI risk, as well as all-cause mortality in intensive care unit (ICU) patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e A retrospective cohort study was conducted using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Critically ill patients with complete TyG index measurements were included. The primary outcome was SALI incidence, and secondary outcomes included in-hospital, ICU, 28-day, 90-day, and 365-day mortality. Logistic and cox multivariate regression analyses and restricted cubic spline were applied to evaluate associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Among 4343 patients 10.7% developed SALI. In-hospital, ICU, 28-day, 90-day, and 365-day mortality rates were 15.6%, 11.1%, 18.4%, 23.2%, and 28.8%, respectively. A linear positive association was observed between the TyG index and SALI risk [adjusted OR (95% CI) 1.52 (1.12~2.08), \u003cem\u003eP\u003c/em\u003e-value 0.008]. For mortality, a nonlinear association was identified with TyG index and in-hospital mortality (P for nonlinearity=0.014), with an inflection point at TyG index=9.888. Below this threshold, each TyG index unit increase was associated with higher in-hospital mortality [HR 1.86 (1.49–2.34)，\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001], whereas no significant association was observed above it [HR 1.22 (0.76–1.97)]. The subgroup analysis suggests that our results are robust.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e Elevated TyG index was an independent risk factor for SALI in critically ill patients and demonstrated a nonlinear association with in-hospital mortality. These findings highlight TyG as a practical biomarker for risk stratification and targeted management in the ICU setting.\u003c/p\u003e","manuscriptTitle":"Association between Triglyceride-Glucose Index and Risk of Sepsis-Associated Liver Injury and Mortality in Critically Ill Patients: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 06:25:27","doi":"10.21203/rs.3.rs-6703771/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":"8d1ffde5-6a70-413f-b3ac-f4583df42e6f","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T11:11:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 06:25:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6703771","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6703771","identity":"rs-6703771","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}