Association of triglyceride-glucose index fluctuation with in-hospital all-cause mortality in critically ill patients: A Multidatabase Retrospective Study

Association of triglyceride-glucose index fluctuation with in-hospital all-cause mortality in critically ill patients: A Multidatabase Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association of triglyceride-glucose index fluctuation with in-hospital all-cause mortality in critically ill patients: A Multidatabase Retrospective Study Zuzhi Chen, Xiang Xiang, Haoran Xu, Ting Zhao, Weiguang Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7838255/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose: This study aims to evaluate the relationship between TyG variability and in-hospital mortality across two ICU databases (MIMIC-IV and SWH) and to determine if TyG variability provides more prognostic value than baseline TyG. Methods: This retrospective observational study utilized data from the MIMIC-IV (2008-2019) and SWH (2016-2023) ICU cohorts. TyG metrics, including baseline (TyG-BL), median (TyG-Median), mean (TyG-Average), standard deviation (TyG-STD), range (TyG-Range), and coefficient of variation (TyG-CV), were calculated. The association between TyG metrics and all-cause in-hospital mortality was evaluated using multivariable logistic regression. Nonlinear relationships were explored using restricted cubic splines (RCS). Results: A total of 2,208 ICU patients were included (MIMIC: n = 1,707; SWH: n = 501). In MIMIC, TyG variability metrics (TyG-STD, TyG-Range) were independently associated with mortality, with TyG-STD showing an OR of 1.66 (95% CI 1.06–2.61, P = 0.027) and TyG-Range an OR of 1.24 (95% CI 1.05–1.46, P = 0.011). TyG variability metrics in SWH showed similar trends, but associations attenuated after full adjustment. Kaplan-Meier analysis demonstrated clear survival curve separation for TyG variability metrics in MIMIC, while the SWH cohort showed weaker separation. RCS analysis revealed a nonlinear relationship between TyG metrics and mortality risk in MIMIC, with a steeper increase in risk at higher TyG values. Conclusions: TyG variability, rather than baseline TyG values, is independently associated with in-hospital mortality in critically ill patients, with a stronger association observed in the MIMIC cohort. These findings suggest that TyG variability reflects metabolic instability and may serve as a better predictor of ICU mortality. Future prospective studies are needed to validate TyG variability as a predictive tool in ICU risk assessments. Clinical trial number: not applicable. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors TyG index insulin resistance ICU mortality metabolic variability restricted cubic splines MIMIC-IV SWH Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Despite advances in organ support and infection management, patients in the intensive care unit (ICU) continue to experience high morbidity, mortality, and healthcare utilization[ 1 ]. Early and accurate risk assessment is therefore essential to guide therapy [ 2 – 4 ]. However, biomarkers that are robust, repeatable, and generalizable for mortality prediction in the ICU remain limited [ 5 – 8 ]. Insulin resistance (IR) is a common and clinically important metabolic disturbance in critical illness, driven by stress responses, systemic inflammation, and immune dysregulation, and it is associated with adverse outcomes [ 9 ]. Prior work indicates that insulin sensitivity fluctuates dynamically during the course of critical illness and may be 50–70% lower than in healthy individuals [ 10 ]. Standardized IR assessments-such as the hyperinsulinemic–euglycemic clamp or HOMA-IR, which requires insulin measurements-are impractical for routine ICU use, limiting their bedside applicability [ 11 – 15 ]. The triglyceride-glucose (TyG) index, calculated from routine glucose and triglyceride tests, has emerged as a convenient surrogate of IR and has been linked to cardiometabolic risk and mortality across diverse populations [ 16 ]. Several studies suggest that TyG performs as well as or better than HOMA-IR for identifying metabolic syndrome (e.g., area under the ROC curve = 0.84 vs 0.68) [ 17 ]. Prospective cohorts also indicate that cumulative/long-term TyG exposure correlates with increased cardiovascular risk [ 16 , 18 ]. In critical care, admission (baseline) TyG has been associated with mortality; however, evidence remains limited regarding whether time-series changes and variability in TyG during the ICU/hospitalization period (e.g., standard deviation, coefficient of variation, range) provide incremental prognostic information[ 12 , 19 , 20 ]. Moreover, the consistency and reproducibility of TyG-related metrics across different health systems, patient populations, and care acuity have not been well characterized. Against this background, we constructed during ICU hospitalization a panel of TyG metrics that capture both level (baseline, median, mean) and temporal variability (standard deviation, coefficient of variation, range). We then evaluated their associations with in-hospital all-cause mortality using multivariable models and explored potential nonlinear exposure–risk relationships via restricted cubic splines. In parallel, we applied an identical analytic workflow to two independent data sources-MIMIC-IV (USA) and a SWH (China)-to compare consistency and reproducibility of findings across clinical contexts. 2. Methods 2.1 Data source and study population This retrospective observational study used de-identified electronic health record data from two sources: (i) MIMIC-IV (version 2.0) contains hospital and ICU data from Beth Israel Deaconess Medical Center (Boston, USA) for admissions from 2008 to 2019, prior to the COVID-19 pandemic; all personal identifiers in MIMIC-IV are irreversibly de-identified. (ii) SWH cohort: data were obtained from the Clinical Big Data Center of the First Affiliated Hospital of the Army Medical University (Southwest Hospital, Chongqing, China) for hospitalizations between 2016 and 2023; patient identifiers were de-identified prior to analysis. To enhance comparability, patients with confirmed COVID-19 were excluded from both datasets. The study was approved by the Ethics Committee of the First Affiliated Hospital of the Army Medical University (People’s Liberation Army) (approval No. KY2024116) with a waiver of informed consent due to de-identified data, and it was registered in the China Clinical Trial Registry (ChiCTR2400086782, registration date: July 10, 2024). All procedures adhered to relevant regulations and the Declaration of Helsinki. Inclusion criteria: age > 18 years; first ICU admission during the index hospitalization; ICU length of stay > 24 hours; ≥2 paired measurements of blood glucose and triglycerides to compute TyG variability. Exclusion criteria: (1) confirmed COVID-19; (2) missing key demographic or outcome information. Only the first eligible hospitalization for patients with multiple admissions was retained. The unit of analysis was the first ICU admission within the index hospitalization. 2.2 Data collection Data from MIMIC-IV were extracted using structured SQL queries (PostgreSQL 11.0). Data from SWH were exported from the institutional data platform and processed with standardized scripts for statistical analysis. Variable definitions and coding were harmonized a priori across the two datasets. Demographics were obtained at admission. Vital signs were averaged over the first 24 hours after ICU admission. For laboratory tests other than blood glucose and triglycerides, the first value after ICU admission was used. Height and weight were taken from measurements within 24 hours before ICU admission, when available. The SOFA score was computable in MIMIC-IV based on components within the first 24 hours after ICU admission; SOFA was not uniformly available in SWH. Other covariates included hemoglobin, platelet count, red blood cell count, red cell distribution width, white blood cell count, neutrophil-to-lymphocyte ratio (NLR), albumin, bicarbonate (in MIMIC-IV), creatinine, sodium, calcium, potassium, prothrombin time (PT), activated partial thromboplastin time (PTT), alanine aminotransferase (ALT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), and lipids. To mitigate potential bias, following Zhang et al. [ 21 ], variables with > 50% missingness were excluded from multivariable modeling. For remaining missing data, under the missing-at-random assumption we applied multiple imputation by chained equations with m = 10 imputed datasets and 10 iterations each. All covariates were included in the imputation model; the outcome was not imputed. Parameter estimates were combined using Rubin’s rules. 2.3 Assessment of the TyG index and related parameters The TyG index was calculated as ln[TG (mg/dl) × FPG (mg/dl)/2]. To ensure unit consistency, results reported in mmol/L were converted to mg/dL (glucose × 18; triglycerides × 88.57). Because fasting status was not consistently recorded, we used routinely measured serum glucose and triglyceride values. To construct the TyG time series during the ICU stay, each glucose measurement was paired with the temporally closest triglyceride measurement; if the sampling interval between the pair exceeded 6 hours, no TyG value was generated for that time point. For each patient’s ICU TyG series, we then derived the following metrics: TyG-BL (first TyG value after ICU admission), TyG-Median (median value of the TyG index sequence), TyG- Average (mean of the TyG index sequence), TyG-STD (standard deviation of the TyG index sequence), TyG-Range (range of the TyG index sequence, maximum - minimum), and TyG-CV (coefficient of variation, SD/mean). TyG-BL, TyG-Median, and TyG-Mean primarily reflect the absolute level, whereas TyG-STD, TyG-Range, and TyG-CV reflect variability/dispersion over time. 2.4 Primary and secondary outcomes The primary outcome was in-hospital all-cause mortality during the index hospitalization; the secondary outcome was ICU mortality. For survival summaries, the time origin was ICU admission. 2.5 Statistical analysis Continuous variables were first assessed for normality and homogeneity of variance. Normally distributed variables with equal variances are presented as mean ± standard deviation (Mean ± SD) and compared using the Student’s t-test or one-way ANOVA. Non-normally distributed or heteroscedastic variables are presented as median [Q1, Q3] and compared using the Mann-Whitney U test or Kruskal-Wallis test. Categorical variables are summarized as n (%) and compared using the χ² test or Fisher’s exact test. All tests were two-sided, with P < 0.05 indicating statistical significance. To examine the association between TyG level/variability and the primary outcome, we classified patients separately within MIMIC-IV and SWH into tertiles (Groups 1–3: low/middle/high) according to the distribution of each TyG metric in the corresponding dataset. With ICU admission as time zero and discharge as censoring, Kaplan-Meier curves were used to estimate the cumulative incidence of in-hospital death across tertiles, and differences were assessed by the log-rank test. For effect estimation with continuous exposures, we fitted multivariable logistic regression models and reported odds ratios (ORs) with 95% confidence intervals (CIs). Pre-specified adjustment sets were: Model 1, unadjusted (TyG metric only); Model 2, adjusted for age, sex, and BMI; and Model 3, further adjusted for clinical and laboratory covariates supported by prior evidence-SOFA (MIMIC-IV only), hemoglobin, platelet count, red blood cell count, RDW (MIMIC-IV), white blood cell count, NLR, albumin, bicarbonate (MIMIC-IV), creatinine, sodium, calcium, potassium, PT, PTT, ALT, ALP, AST, LDH, and lipids (LDL/HDL in SWH). Because covariate availability differed between datasets, models were fit and reported separately for MIMIC-IV and SWH. To assess potential nonlinear relationships between each continuous TyG metric and the outcome, we incorporated restricted cubic splines (RCS) into the regression framework, placing knots at the 5th, 35th, 65th, and 95th percentiles as recommended by Harrell [ 22 , 23 ]; the sample median served as the reference value. Multicollinearity was evaluated using the variance inflation factor (VIF), with VIF < 5 considered acceptable. Analyses were performed using Python 3.7.5 and R 4.3.3. Two-sided P < 0.05 was considered statistically significant. 3. Results 3.1 Baseline characteristics of study population As shown in Fig. 1 and Table 1 , a total of 2,208 ICU patients were included (MIMIC, n = 1,707; SWH, n = 501). The sex distribution was similar in both cohorts (male 61.2% vs 61.1%). Patients in MIMIC were older (60.6 [47.7–70.5] years) than those in SWH (56.7 [41.1–70.3] years). ICU length of stay was comparable (MIMIC 8.6 [3.4–16.1] vs SWH 8.9 [5.2–15.4] days), as was hospital length of stay (MIMIC 22.7 [13.4–37.1] vs SWH 21.0 [13.0–39.0] days). In-hospital mortality was 23.2% in MIMIC and 14.6% in SWH. Anthropometrics indicated higher weight and BMI in MIMIC (BMI 28.9 [24.5–34.2] vs 23.6 [23.1–23.8] kg/m²). Table 1 Demographic and Clinical Characteristics of Patients in the MIMIC and SWH Cohorts Variable MIMIC (N = 1707) SWH (N = 501) Gender, n (%) Male 1045 (61.2) 306 (61.1) Female 662 (38.8) 195 (38.9) Admission Age (Years) 60.6 [47.7, 70.5] 56.7 [41.1, 70.3] LOS (ICU) (Days) 8.6 [3.4, 16.1] 8.9 [5.2, 15.4] LOS (Hospital) (Days) 22.7 [13.4, 37.1] 21.0 [13.0, 39.0] Height (cm) 170.1 [165.0, 175.0] 162.3 [162.0, 163.0] Weight (kg) 83.9 [69.5, 101.0] 62.2 [60.0, 62.2] BMI (kg/m 2 ) 28.9 [24.5, 34.2] 23.6 [23.1, 23.8] Hospital Expire Flag, n (%) No 1311 (76.8) 428 (85.4) Yes 396 (23.2) 73 (14.6) SOFA Index (Score) 6.0 [3.0, 9.0] - Hemoglobin (g/dL) 10.1 [8.5, 11.8] 8.8 [7.9, 10.3] Platelet (10³/µL) 179.0 [113.0, 253.0] 155.0 [83.0, 232.0] RBC (10⁶/µL) 3.4 [2.8, 4.0] 3.0 [2.6, 3.5] RDW (%) 15.0 [13.8, 17.0] - WBC (10³/µL) 11.3 [7.8, 16.3] 9.7 [6.4, 13.8] Neutrophils (10³/µL) 10.9 [6.6, 13.0] 7.9 [5.0, 11.6] Lymphocytes (10³/µL) 1.2 [0.7, 1.4] 0.9 [0.6, 1.3] NLR 7.9 [5.9, 14.0] 8.4 [5.2, 15.7] Albumin (g/dL) 2.7 [2.4, 3.1] 3.3 [3.0, 3.6] Bicarbonate (mEq/L) 22.0 [19.0, 25.0] - Creatinine (mg/dL) 1.1 [0.7, 1.8] 0.9 [0.6, 1.9] Sodium (mEq/L) 138.0 [135.0, 142.0] 140.0 [136.6, 144.0] Calcium (mg/dL) 8.1 [7.5, 8.6] 1.8 [1.8, 2.1] Potassium (mEq/L) 4.1 [3.7, 4.6] 4.0 [3.7, 4.3] PT (Seconds) 14.5 [12.9, 17.2] 12.6 [11.7, 14.2] PTT (Seconds) 32.0 [27.9, 39.4] 32.5 [28.4, 39.5] ALT (U/L) 32.0 [16.0, 94.5] 26.1 [12.8, 53.4] ALP (U/L) 83.0 [59.0, 116.0] 90.0 [67.0, 141.1] AST (U/L) 47.0 [25.0, 140.0] 43.7 [25.4, 96.1] LD_LDH (U/L) 381.0 [243.0, 615.2] 518.4 [293.2, 906.3] LDL (mg/dL) - 1.7 [1.1, 2.3] HDL (mg/dL) - 0.6 [0.4, 0.9] CKMB (ng/mL) - 18.8 [10.4, 28.1] Glucose (mg/dL) 127.0 [105.0, 161.5] 1.8 [1.2, 2.9] Triglyceride (mg/dL) 170.0 [110.5, 275.5] 7.9 [6.2, 10.4] Heart Rate (bpm) 91.9 [78.8, 105.4] 92.3 [83.2, 101.0] SBP (mmHg) 114.9 [104.3, 124.2] 127.0 [117.0, 135.5] DBP (mmHg) 65.6 [58.4, 71.1] 70.9 [66.0, 77.0] MBP (mmHg) 81.9 [74.5, 87.8] 89.5 [83.7, 96.0] Respiratory Rate (breaths/min) 20.4 [17.7, 23.6] 20.0 [19.0, 20.7] Temperature (°C) 37.0 [36.7, 37.4] 37.0 [36.7, 37.5] SpO 2 (%) 96.9 [95.3, 98.3] 98.3 [98.1, 99.2] This table presents the demographic and clinical characteristics of patients in the MIMIC and SWH cohorts. Continuous variables are reported as median (Q1, Q3), while categorical variables are expressed as counts (n) and percentages (%). For some variables marked with a " - " (dash), data collection was not performed or was unavailable in that cohort. Abbreviations: LOS: Length of Stay; ICU: Intensive Care Unit; BMI: Body Mass Index; SOFA: Sequential Organ Failure Assessment; RDW: Red Cell Distribution Width; WBC: White Blood Cell count; RBC: Red Blood Cell count; NLR: Neutrophil-to-Lymphocyte Ratio; PT: Prothrombin Time; PTT: Partial Thromboplastin Time; ALT: Alanine Aminotransferase; ALP: Alkaline Phosphatase; AST: Aspartate Aminotransferase; LD_LDH: Lactate Dehydrogenase; LDL: Low-Density Lipoprotein; HDL: High-Density Lipoprotein; CKMB: Creatine Kinase-MB; SpO 2 : Oxygen Saturation; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; MBP: Mean Blood Pressure. Table 2 Baseline Characteristics and Clinical Outcomes of ICU Patients Stratified by TyG Index Quantile in the MIMIC and SWH Databases Variable MIMIC P-Value SWH P-Value TyG-index Low (N = 562) Middle (N = 563) High (N = 581) P-Value Low (N = 165) Middle (N = 165) High (N = 170) Gender, n (%) 0.202 0.309 Male 232 (41.3) 220 (39.1) 210 (36.1) 59 (35.8) 72 (43.6) 64 (37.6) Female 330 (58.7) 343 (60.9) 371 (63.9) 106 (64.2) 93 (56.4) 106 (62.4) Admission Age (Years) 64.2 [53.5,75.3] 61.0 [49.2,70.7] 56.5 [43.1,65.9] < 0.001 63.8 [48.7,73.1] 58.7 [41.1,70.3] 50.5 [36.5,63.6] < 0.001 LOS (ICU) (Days) 6.6 [2.6,13.9] 8.7 [3.5,16.6] 10.4 [4.7,18.0] < 0.001 9.9 [5.6,15.8] 8.2 [4.9,14.0] 9.6 [5.5,15.7] 0.23 LOS (Hospital) (Days) 23.9 [14.2,38.1] 24.3 [14.3,38.1] 20.4 [11.6,34.4] 0.001 23.0 [14.0,40.0] 19.0 [13.0,34.0] 22.0 [14.0,39.8] 0.242 Height (cm) 170.1 [165.0,173.0] 170.1 [165.0,175.0] 170.1 [165.0,175.0] 0.195 162.3 [161.0,162.3] 162.3 [162.0,163.0] 162.3 [161.2,163.4] 0.925 Weight (kg) 76.3 [63.0,91.5] 83.9 [70.9,100.9] 91.5 [75.3,107.0] < 0.001 62.2 [58.0,62.2] 62.2 [61.0,62.2] 62.2 [60.2,63.5] 0.224 BMI (kg/m2) 26.5 [22.3,30.9] 28.9 [24.8,33.9] 31.1 [26.3,36.9] < 0.001 23.6 [22.3,23.6] 23.6 [23.2,24.3] 23.6 [23.6,24.2] 0.004 Hospital Expire Flag 0.912 0.484 No 435 (77.4) 430 (76.4) 445 (76.6) 139 (84.2) 139 (84.2) 150 (88.2) Yes 127 (22.6) 133 (23.6) 136 (23.4) 26 (15.8) 26 (15.8) 20 (11.8) SOFA Index (Score) 6.0 [3.0,8.0] 6.0 [3.0,9.0] 7.0 [4.0,10.0] < 0.001 - - - - Hemoglobin (g/dL) 10.0 [8.5,11.7] 10.0 [8.5,11.7] 10.3 [8.5,12.1] 0.18 9.0 [8.1,10.5] 8.7 [7.9,10.2] 8.7 [7.7,10.1] 0.347 Platelet (10³/µL) 180.5 [110.0,257.0] 185.0 [120.0,261.0] 173.0 [110.0,247.0] 0.338 160.0 [83.0,232.0] 172.0 [86.0,246.0] 141.0 [80.0,217.0] 0.232 RBC (10⁶/µL) 3.3 [2.8,3.9] 3.4 [2.8,4.0] 3.5 [2.9,4.0] 0.078 3.0 [2.7,3.6] 3.0 [2.6,3.5] 2.9 [2.5,3.4] 0.128 RDW (%) 15.4 [13.9,17.2] 15.0 [13.9,16.8] 14.8 [13.7,16.8] 0.013 - - - - WBC (10³/µL) 10.6 [7.5,15.3] 11.9 [7.8,16.4] 11.6 [7.9,17.3] 0.028 9.2 [6.2,13.4] 9.6 [6.9,13.1] 10.1 [6.4,15.2] 0.507 Neutrophils (10³/µL) 10.6 [5.9,11.9] 10.9 [6.7,12.9] 10.9 [7.1,13.8] 0.003 7.6 [4.6,11.5] 7.7 [5.0,11.0] 8.1 [5.1,12.6] 0.518 Lymphocytes (10³/µL) 1.2 [0.7,1.4] 1.3 [0.7,1.5] 1.3 [0.6,1.5] 0.352 0.8 [0.5,1.3] 0.9 [0.6,1.3] 0.9 [0.6,1.4] 0.465 NLR 7.9 [5.6,13.7] 7.9 [5.9,13.4] 7.9 [6.3,14.8] 0.255 8.4 [5.1,16.2] 8.4 [5.0,15.3] 8.5 [5.7,16.0] 0.692 Albumin (g/dL) 2.7 [2.4,3.1] 2.7 [2.3,3.1] 2.7 [2.4,3.0] 0.748 3.3 [3.0,3.6] 3.3 [3.0,3.6] 3.3 [3.0,3.6] 0.987 Bicarbonate (mEq/L) 22.0 [20.0,25.0] 22.0 [19.0,25.0] 21.0 [17.0,24.0] < 0.001 - - - - Creatinine (mg/dL) 1.0 [0.7,1.5] 1.1 [0.7,1.8] 1.2 [0.8,2.1] < 0.001 0.9 [0.6,1.5] 0.9 [0.6,1.9] 1.0 [0.6,2.2] 0.341 Sodium (mEq/L) 138.5 [135.0,141.0] 139.0 [136.0,142.0] 138.0 [135.0,141.0] 0.006 139.7 [136.8,143.4] 141.0 [136.8,144.8] 139.4 [136.5,143.4] 0.217 Calcium (mg/dL) 8.1 [7.6,8.6] 8.1 [7.6,8.6] 8.0 [7.5,8.5] 0.002 1.8 [1.8,2.1] 1.8 [1.8,2.1] 1.8 [1.8,2.1] 0.419 Potassium (mEq/L) 4.0 [3.7,4.5] 4.1 [3.8,4.6] 4.2 [3.8,4.7] 0.017 4.0 [3.7,4.3] 4.1 [3.8,4.4] 4.1 [3.7,4.3] 0.477 PT (Seconds) 15.0 [13.1,18.3] 14.3 [12.8,17.0] 14.2 [12.7,16.7] < 0.001 12.8 [11.8,14.8] 12.7 [11.7,14.1] 12.5 [11.6,13.9] 0.21 PTT (Seconds) 33.3 [28.8,42.7] 31.6 [27.6,39.2] 31.2 [27.6,39.2] < 0.001 32.7 [28.2,43.8] 32.5 [28.8,37.6] 32.3 [28.4,39.4] 0.947 ALT (U/L) 26.0 [14.0,70.0] 30.0 [15.0,82.0] 39.0 [21.0,130.0] < 0.001 27.2 [12.5,62.7] 27.3 [14.3,49.9] 24.0 [11.5,49.4] 0.411 ALP (U/L) 81.5 [59.0,109.8] 83.0 [58.0,115.0] 85.0 [58.0,125.0] 0.657 88.0 [62.0,142.0] 86.0 [68.0,121.0] 95.5 [71.0,150.9] 0.106 AST (U/L) 41.0 [23.0,111.5] 42.0 [22.0,108.0] 64.0 [31.0,197.0] < 0.001 38.2 [25.1,91.8] 44.8 [26.3,95.8] 48.0 [25.2,101.6] 0.439 LD_LDH (U/L) 314.5 [213.0,615.2] 354.0 [238.5,615.2] 469.0 [292.0,615.2] < 0.001 425.0 [263.0,765.1] 501.7 [283.8,906.3] 615.1 [351.1,906.3] 0.002 LDL (mg/dL) - - - - 1.5 [1.0,2.0] 1.7 [1.1,2.3] 1.9 [1.2,2.6] 0.015 HDL (mg/dL) - - - - 0.6 [0.4,0.9] 0.6 [0.4,0.9] 0.6 [0.4,0.9] 0.797 CKMB (ng/mL) - - - - 18.6 [9.7,28.1] 17.6 [10.5,28.1] 23.1 [10.8,28.1] 0.278 Glucose (mg/dL) 119.0 [101.0,145.8] 127.0 [107.0,157.0] 139.0 [110.0,180.0] < 0.001 1.1 [0.8,1.5] 1.7 [1.3,2.4] 3.3 [2.2,4.5] < 0.001 Triglyceride (mg/dL) 99.0 [76.0,133.0] 172.0 [133.0,225.5] 315.0 [227.0,470.0] < 0.001 6.8 [5.7,8.7] 7.9 [6.2,10.4] 9.2 [7.0,11.9] < 0.001 Heart Rate (bpm) 88.1 [76.8,101.2] 91.5 [78.8,103.8] 96.7 [83.0,109.0] < 0.001 91.7 [80.5,100.5] 91.3 [82.4,100.4] 94.2 [86.8,103.7] 0.016 SBP (mmHg) 113.1 [102.1,124.3] 115.4 [104.5,124.0] 115.5 [105.5,124.5] 0.036 124.4 [114.6,135.0] 127.1 [118.3,138.0] 127.1 [119.0,134.5] 0.11 DBP (mmHg) 64.7 [57.0,69.9] 65.6 [59.4,70.5] 65.6 [59.0,72.8] 0.011 69.3 [64.7,75.7] 72.0 [66.6,78.0] 71.0 [66.8,77.1] 0.051 MBP (mmHg) 80.2 [73.5,87.1] 82.2 [75.0,87.7] 82.2 [75.6,88.8] 0.008 87.1 [81.5,95.7] 90.5 [84.9,97.0] 89.6 [84.8,95.2] 0.025 Respiratory Rate (breaths/min) 19.6 [17.2,22.4] 20.2 [17.6,23.5] 21.6 [18.7,25.1] < 0.001 20.0 [18.9,20.8] 20.0 [18.9,20.6] 20.0 [19.2,21.0] 0.297 Temperature (°C) 36.9 [36.7,37.2] 37.0 [36.7,37.3] 37.1 [36.8,37.5] < 0.001 37.0 [36.7,37.5] 37.0 [36.7,37.5] 37.0 [36.7,37.5] 0.856 SpO2 (%) 97.2 [95.5,98.4] 97.1 [95.6,98.6] 96.5 [94.8,98.0] < 0.001 98.2 [98.0,99.2] 98.4 [98.1,99.2] 98.4 [98.1,99.0] 0.898 This table presents the demographic and clinical characteristics of patients in the MIMIC and SWH cohorts. Continuous variables are reported as median (Q1, Q3), while categorical variables are expressed as counts (n) and percentages (%). For some variables marked with a " - " (dash), data collection was not performed or was unavailable in that cohort. Abbreviations: LOS: Length of Stay; ICU: Intensive Care Unit; BMI: Body Mass Index; SOFA: Sequential Organ Failure Assessment; RDW: Red Cell Distribution Width; WBC: White Blood Cell count; RBC: Red Blood Cell count; NLR: Neutrophil-to-Lymphocyte Ratio; PT: Prothrombin Time; PTT: Partial Thromboplastin Time; ALT: Alanine Aminotransferase; ALP: Alkaline Phosphatase; AST: Aspartate Aminotransferase; LD_LDH: Lactate Dehydrogenase; LDL: Low-Density Lipoprotein; HDL: High-Density Lipoprotein; CKMB: Creatine Kinase-MB; SpO 2 : Oxygen Saturation; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; MBP: Mean Blood Pressure. 3.2 Associations of TyG metrics with in-hospital mortality As shown in Fig. 2 , across both datasets, the variability-type TyG metrics showed a consistent direction of association with in-hospital all-cause mortality. In MIMIC ( n = 1,707), after full adjustment for age, sex, BMI, and prespecified clinical/laboratory covariates (Model 3 in Methods), TyG-STD (OR 1.66, 95% CI 1.06–2.61, P = 0.027) and TyG-Range (OR 1.24, 95% CI 1.05–1.46, P = 0.011) remained independently associated with mortality; TyG-CV showed a borderline association (OR 1.14, 95% CI 0.98–1.33, P = 0.084). Among level-type metrics, TyG-Average remained significant (OR 1.21, 95% CI 1.02–1.44, P = 0.030), whereas TyG-Median was borderline and TyG-BL was not significant. In SWH ( n = 501), variability metrics were risk factors in unadjusted/partially adjusted models, but the associations attenuated and became non-significant after full adjustment (e.g., TyG-STD OR 2.18, 95% CI 0.84–5.66, P = 0.108; TyG-Range OR 1.24, 95% CI 0.84–1.84, P = 0.275; TyG-CV OR 1.31, 95% CI 0.96–1.80, P = 0.092). In contrast, TyG-BL was inversely associated with mortality (OR 0.70, 95% CI 0.51–0.97, P = 0.033). 3.3 Kaplan–Meier Analysis of In-Hospital Mortality Across TyG Tertiles When TyG metrics were stratified into cohort-specific tertiles, higher TyG values were observed more often in younger patients and were associated with stepwise increases in BMI, glucose, and triglycerides. In MIMIC, higher TyG was also associated with longer ICU length of stay, modestly higher heart and respiratory rates and temperature, lower bicarbonate, and higher ALT/AST/LDH; in SWH, LDH increased with TyG and mean blood pressure was slightly higher. Despite these patterns, crude in-hospital mortality proportions across baseline-TyG tertiles were similar within each cohort (approximately 23% in MIMIC and 12–16% in SWH). Kaplan–Meier analyses showed that in MIMIC, tertiles of both level-type (Average, Median) and variability-type (SD, Range, CV) TyG metrics exhibited clear separation of survival curves with progressively higher cumulative incidence of in-hospital death (most log-rank P 0.05). In SWH, the overall direction was similar but separation was weaker; only CV tertiles differed significantly (log-rank P = 0.017), whereas Baseline, Average, Median, SD, and Range did not. 3.4 Nonlinear Associations of TyG Level and Variability with In-Hospital Mortality In MIMIC, multivariable logistic models incorporating restricted cubic splines revealed significant, often nonlinear associations between TyG metrics and in-hospital mortality: TyG-Average (overall P = 0.038; nonlinearity P = 0.009), TyG-Median (0.004; 0.006), TyG-STD (< 0.001; 0.013), TyG-Range (< 0.001; 0.009), and TyG-CV (0.003; 0.021). The risk increased with higher values, with a steeper slope in the upper range, suggesting threshold/saturation-like behavior. TyG-BL showed no association (overall P = 0.542). In SWH, overall and nonlinearity tests for all TyG metrics were not significant ( P > 0.05), and the spline curves were relatively flat with wide confidence bands, consistent with smaller sample size and differences in covariate availability. 4. Discussion In this study, we used data from the MIMIC and SWH databases to examine the relationship between TyG index variability and all-cause in-hospital mortality. We found that indicators of TyG variability (TyG-Range, TyG-STD, TyG-CV) were independently associated with in-hospital mortality, whereas a single (baseline) TyG value had limited risk stratification ability and showed a negative correlation after extensive adjustment in the SWH database. RCS revealed a non-linear exposure–risk relationship in MIMIC, with a steeper increase in risk at higher values. In contrast, the SWH curve was flatter, which can be attributed to the smaller sample size and differences in covariate availability. Overall, dynamic/time-series information provided a better reflection of short-term prognosis in critically ill patients compared to a single baseline value. Previous studies have shown that insulin resistance (IR) in ICU populations is associated with stress, inflammation, and immune dysregulation, all linked to poor outcomes [ 24 , 25 ]. However, standard IR assessments, such as the clamp test or HOMA-IR, are not routinely feasible in ICU settings [ 26 ]. Our work, which utilized routine clinical tests (glucose, triglycerides), developed the TyG index and its time-series parameters, supporting the evidence that variability, rather than merely the level, is more closely associated with mortality risk. This provides a new perspective on dynamic metabolic risk characterization in the ICU. As an alternative marker for IR, TyG has been validated in multiple studies and populations, with predictive performance equal to or superior to HOMA-IR, further supporting its feasibility for ICU applications [ 27 – 30 ]. TyG reflects glucose and lipid utilization/energy status, and its variability may represent a composite of factors including stress hormone fluctuations, inflammation-mediated hepatic lipid metabolism alterations, unstable nutritional input, and adjustments in insulin/lipid-lowering therapy [ 30 , 31 ]. Similar to blood glucose variability, which is known to correlate with mortality risk in the ICU independent of average blood glucose levels [ 32 , 33 ], TyG variability captures a broader metabolic instability phenotype (oxidative stress, endothelial dysfunction, immune imbalance) beyond transient levels [ 34 – 36 ]. In MIMIC, we observed a steeper slope in the high-value segment, consistent with a "threshold/saturation" effect [ 37 ]. In contrast, baseline TyG in SWH was negatively correlated, which could be influenced by initial illness, nutritional and insulin/lipid-lowering treatments, malnutrition, or residual confounding. This may also relate to differences in sample size and sampling timepoints[ 32 , 38 , 39 ]. Metabolic phenotype differences may alter the clinical meaning of "absolute TyG values." MIMIC patients were generally heavier, with a higher proportion of overweight/obese individuals (BMI distribution shifted right), and higher TyG values likely reflected increased insulin resistance and lipid burden [ 40 , 41 ]. In contrast, SWH patients were generally smaller in stature, and in this context, "low/high" absolute TyG values may reflect different metabolic reserves and nutritional states: very low glucose/triglycerides may indicate malnutrition or low metabolic reserves, presenting an "apparent protection" effect [ 42 ]. Furthermore, differences in ICU admission phases and treatment pathways were noted. Table 1 shows that vital signs on the first day of admission were more unstable in MIMIC, while SWH patients were more stable, consistent with the high-intensity resuscitation followed by ICU transfer at SWH. In this context, baseline TyG may be influenced by sampling time: higher TyG in MIMIC may reflect uncontrolled stress/metabolic pressure, while higher TyG in SWH may partially represent recovery post-resuscitation and improved metabolic energy and nutritional input. In contrast, variability indices capture information throughout the entire hospitalization, being less prone to bias from single-time measurements, thus exhibiting more consistent cross-database performance. Kaplan-Meier analysis by tertiles showed that most TyG variability indices presented the lowest risk in the middle group, with higher risks at both ends, suggesting the potential existence of a U-shaped or threshold/saturation effect. RCS further showed that the risk increased sharply in the higher value segment in MIMIC, consistent with the biological intuition that "greater metabolic fluctuation, higher risk." Combining both types of analysis, we speculate that relative stability within a certain range (neither excessive fluctuation nor extremely stable "low energy/low fat" states) might correspond to better short-term prognosis. This finding provides a basis for incorporating metabolic stability, beyond focusing solely on absolute levels, into risk assessments [ 39 ]. Although this study does not provide causal inference and has not defined therapeutic targets, the results support the need for prospective studies to evaluate the potential benefits of reducing metabolic fluctuations (e.g., more consistent nutritional support, integrated glucose-lipid monitoring, rational use of corticosteroids/vasopressors) and exploring non-linear risk inflection points and actionable thresholds. Evidence of cumulative/long-term TyG exposure increasing cardiovascular event risk further supports the biological rationale for "stable metabolic exposure" in general and specific populations [ 43 , 44 ]. This study was conducted using data from two databases with different healthcare systems and care models, enhancing the reproducibility and robustness of the results. Variables were standardized prior to the study, and key lab parameters were normalized to reduce inter-database heterogeneity. The study analyzed TyG levels and time-series fluctuations using RCS to explore non-linear exposure-risk relationships, avoiding model misfit due to simple linear assumptions. Missing data were handled with MICE multiple imputation under the MAR assumption, with separate model construction for each database, in accordance with STROBE guidelines. This retrospective study cannot fully eliminate residual confounding (e.g., nutritional pathways, insulin/lipid-lowering treatments). TyG, derived from non-fasting routine tests, may introduce measurement variability, and the choice of pairing window could affect fluctuation estimates. Covariate availability differed between databases, potentially leading to differential adjustment. The relatively small SWH sample size resulted in reduced statistical precision. Logistic regression was used for in-hospital mortality, but survival or competing risk models may have been more appropriate. Multiple TyG indices were assessed, raising the risk of Type I error; further validation is needed. Finally, the external validity is limited by differences in ICU patient composition and protocols, requiring confirmation in broader populations and prospective studies [ 45 ]. 5. Conclusion This study found that TyG variability (TyG_Range, TyG_STD, TyG_CV) is independently associated with in-hospital mortality, whereas a single baseline TyG value has limited predictive ability. Dynamic time-series information provided a more accurate short-term prognosis for critically ill patients than static measurements. Our findings suggest that metabolic instability, reflected by TyG variability, may be a better predictor of mortality risk in the ICU than static values, emphasizing the need to consider metabolic stability in risk assessments. These results warrant further prospective studies to validate TyG variability as a predictive tool for adverse outcomes in ICU patients. Abbreviations TyG: Triglyceride-Glucose IR: Insulin Resistance ICU: Intensive Care Unit MIMIC-IV: Medical Information Mart for Intensive Care, version IV SWH: Southwest Hospital SOFA: Sequential Organ Failure Assessment NLR: Neutrophil-to-Lymphocyte Ratio ALT: Alanine Aminotransferase AST: Aspartate Aminotransferase ALP: Alkaline Phosphatase LDH: Lactate Dehydrogenase PT: Prothrombin Time PTT: Activated Partial Thromboplastin Time HOMA-IR: Homeostasis Model Assessment of Insulin Resistance RCS: Restricted Cubic Splines CV: Coefficient of Variation BL: Baseline BMI: Body Mass Index OR: Odds Ratio CI: Confidence Interval SD: Standard Deviation Declarations Ethics Approval and Consent to Participate The study was approved by Ethics Committee of the First Affiliated Hospital of the PLA Army Military Medical University (Approval number: B KY2024116). All participants were informed about the study protocol and provided written informed consent to participate in the study. I confirm that all methods were performed in accordance with the relevant guidelines. All procedures were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Consent for Publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article. The data not published within this article are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by Science and Technology Research Project of the Education Commission of Chongqing City (KJQN202512837) and Medical Research Project of Chengdu Health Commission of Sichuan Province (2021061) Authors' contributions Zuzhi Chen conceptualized the research, wrote the methodology, conducted the survey and formal analysis, visualized the data and wrote the original draft, and reviewed and edited the manuscript. Xiang Xiang visualized the data and wrote the original draft, and reviewed and edited the manuscript. Changlin Yin conceived the concept for the study, assisted with the research methodology, supervised Zuzhi Chen, Xiang Xiang, and Haoran Xu, and reviewed and edited the paper. Haoran Xu is the second reviewer, responsible for the selection and full text evaluation. Ting Zhao and Weiguang Zhang contributed to the creation and review of the images and tables. Xiaofei Xie and Zhi Dou reviewed and edited the manuscript. 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Sci Rep 2025, 15:17019. Liao Y, Zhang R, Shi S, et al: Triglyceride-glucose index linked to all-cause mortality in critically ill patients: a cohort of 3026 patients. Cardiovasc Diabetol 2022, 21:128. Cui H, Liu Q, Wu Y, et al: Cumulative triglyceride-glucose index is a risk for CVD: a prospective cohort study. Cardiovasc Diabetol 2022, 21:22. Wang X, Feng B, Huang Z, et al: Relationship of cumulative exposure to the triglyceride-glucose index with ischemic stroke: a 9-year prospective study in the Kailuan cohort. Cardiovasc Diabetol 2022, 21:66. Schuster NA, Rijnhart JJM, Twisk JWR, et al: Modeling non-linear relationships in epidemiological data: The application and interpretation of spline models. Front Epidemiol 2022, 2:975380. Additional Declarations No competing interests reported. Supplementary Files floatimage1.jpeg Graphical Abstarct Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Dec, 2025 Reviews received at journal 05 Dec, 2025 Reviews received at journal 13 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers invited by journal 27 Oct, 2025 Editor invited by journal 15 Oct, 2025 Editor assigned by journal 13 Oct, 2025 Submission checks completed at journal 13 Oct, 2025 First submitted to journal 11 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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16:48:10","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":148982,"visible":true,"origin":"","legend":"","description":"","filename":"665d827c9393445b90beefef24390c3e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7838255/v1/a606b6242307ccbbcbbc41fc.xml"},{"id":95321298,"identity":"80710d1c-f5cb-47d5-9a5f-2389ef8d75fd","added_by":"auto","created_at":"2025-11-06 16:48:10","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161602,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7838255/v1/9cfc6afcecbdb4f5edef0a15.html"},{"id":95321282,"identity":"bd3d7310-b4a0-4262-8010-05cdb464baaa","added_by":"auto","created_at":"2025-11-06 16:48:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":437545,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of study participants.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7838255/v1/9a0169d816cf36b0e54d3510.jpeg"},{"id":95321283,"identity":"cf4ca181-b077-4bf5-8139-3fcb6cba7450","added_by":"auto","created_at":"2025-11-06 16:48:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations of TyG metrics with in-hospital all-cause mortality in the MIMIC and SWH cohorts. \u003c/strong\u003eTop panel: MIMIC; bottom panel: SWH. Points show odds ratios (ORs) with 95% CIs for the association between each continuous TyG metric and in-hospital all-cause death; the x-axis is on a log scale. Metrics: TyG_BL (Baseline, first TyG after ICU admission), TyG_Median, TyG_ Average, TyG_Range (max-min), TyG_SD (standard deviation), and TyG_CV (coefficient of variation, SD/mean). Models were fit separately within each cohort: Model 1, unadjusted; Model 2, adjusted for age, sex, BMI; Model 3, further adjusted for clinical/laboratory covariates per Methods-SOFA (MIMIC only), hemoglobin, platelet count, RBC, RDW (MIMIC only), WBC, NLR, albumin, bicarbonate (MIMIC only), creatinine, sodium, calcium, potassium, PT, PTT, ALT, ALP, AST, LDH, and lipids (LDL/HDL in SWH). P values are from two-sided Wald tests. An OR \u0026gt;1 indicates higher mortality risk with increasing values of the corresponding TyG metric. Abbreviations: TyG, triglyceride–glucose index; ICU, intensive care unit; SD, standard deviation; CV, coefficient of variation.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7838255/v1/68329adbcbc48ca457a3acf5.png"},{"id":95321281,"identity":"ec737263-800b-4aee-a190-3d134d390487","added_by":"auto","created_at":"2025-11-06 16:48:10","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130554,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier cumulative incidence of in-hospital all-cause mortality across tertiles of TyG metrics in the MIMIC and SWH cohorts.\u003c/strong\u003e A–F: MIMIC; G–L: SWH. Metrics by panel—A/G: TyG Baseline (first value after ICU admission); B/H: TyG Averange; C/I: TyG Median; D/J: TyG STD; E/K: TyG Range (max−min); F/L: TyG CV. Curves compare Group 1 (lowest tertile), Group 2 (middle tertile), and Group 3 (highest tertile) for each TyG metric: Baseline (first TyG after ICU admission), Mean (Average), Median, SD (standard deviation), Range (max − min), and CV (SD/mean). The outcome is in-hospital all-cause death; the y-axis shows cumulative incidence = 1 − S(t), time is in days from ICU admission, and observations are censored at hospital discharge. P-values are from two-sided log-rank tests without covariate adjustment. Tertile cut-points were defined separately within each dataset. Abbreviations: TyG, triglyceride–glucose index; SD, standard deviation; CV, coefficient of variation; KM, Kaplan–Meier.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7838255/v1/4aa6c8ec51f56c9e55fcb57f.jpeg"},{"id":95523736,"identity":"062adaff-a7d9-4c51-be5e-54dbf9684e63","added_by":"auto","created_at":"2025-11-10 10:00:28","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":189828,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic spline relationships between TyG metrics and in-hospital all-cause mortality in the MIMIC and SWH cohorts.\u003c/strong\u003e A–F: MIMIC; G–L: SWH. Metrics by panel-A/G: TyG Baseline (first value after ICU admission); B/H: TyG Averange; C/I: TyG Median; D/J: TyG STD; E/K: TyG Range (max-min); F/L: TyG CV. Curves display adjusted odds ratios (OR) from multivariable logistic regression with restricted cubic splines; ribbons indicate 95% CIs. The reference is the cohort-specific median of each TyG metric. Knots were placed at the 5th, 35th, 65th, and 95th percentiles (Harrell’s recommendation). P for overall tests the overall association; P for nonlinearity tests deviation from linearity (two-sided). Background histograms show the empirical distribution of each TyG metric (right y-axis = density). Models were fit separately in each cohort and adjusted as in Model 3 (age, sex, BMI, and prespecified clinical/laboratory covariates; SOFA, RDW, and bicarbonate available in MIMIC only). Abbreviations: TyG, triglyceride–glucose index; SD, standard deviation; CV, coefficient of variation; ICU, intensive care unit.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7838255/v1/cde5e00427bbeecac20cae22.jpeg"},{"id":105223740,"identity":"15e9e065-d80b-482b-996d-d1e88c58a7e3","added_by":"auto","created_at":"2026-03-23 16:09:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2140535,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7838255/v1/d04dcfef-ccc4-4d59-bf86-ec6f913c67f8.pdf"},{"id":95524224,"identity":"d2f2580e-46e9-4ea1-9641-47e4e15254e3","added_by":"auto","created_at":"2025-11-10 10:02:31","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":258679,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstarct\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7838255/v1/fde181bf5c1864f3903b0bcc.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of triglyceride-glucose index fluctuation with in-hospital all-cause mortality in critically ill patients: A Multidatabase Retrospective Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDespite advances in organ support and infection management, patients in the intensive care unit (ICU) continue to experience high morbidity, mortality, and healthcare utilization[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Early and accurate risk assessment is therefore essential to guide therapy [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, biomarkers that are robust, repeatable, and generalizable for mortality prediction in the ICU remain limited [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInsulin resistance (IR) is a common and clinically important metabolic disturbance in critical illness, driven by stress responses, systemic inflammation, and immune dysregulation, and it is associated with adverse outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Prior work indicates that insulin sensitivity fluctuates dynamically during the course of critical illness and may be 50\u0026ndash;70% lower than in healthy individuals [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Standardized IR assessments-such as the hyperinsulinemic\u0026ndash;euglycemic clamp or HOMA-IR, which requires insulin measurements-are impractical for routine ICU use, limiting their bedside applicability [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe triglyceride-glucose (TyG) index, calculated from routine glucose and triglyceride tests, has emerged as a convenient surrogate of IR and has been linked to cardiometabolic risk and mortality across diverse populations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Several studies suggest that TyG performs as well as or better than HOMA-IR for identifying metabolic syndrome (e.g., area under the ROC curve\u0026thinsp;=\u0026thinsp;0.84 vs 0.68) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Prospective cohorts also indicate that cumulative/long-term TyG exposure correlates with increased cardiovascular risk [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In critical care, admission (baseline) TyG has been associated with mortality; however, evidence remains limited regarding whether time-series changes and variability in TyG during the ICU/hospitalization period (e.g., standard deviation, coefficient of variation, range) provide incremental prognostic information[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, the consistency and reproducibility of TyG-related metrics across different health systems, patient populations, and care acuity have not been well characterized.\u003c/p\u003e\u003cp\u003eAgainst this background, we constructed during ICU hospitalization a panel of TyG metrics that capture both level (baseline, median, mean) and temporal variability (standard deviation, coefficient of variation, range). We then evaluated their associations with in-hospital all-cause mortality using multivariable models and explored potential nonlinear exposure\u0026ndash;risk relationships via restricted cubic splines. In parallel, we applied an identical analytic workflow to two independent data sources-MIMIC-IV (USA) and a SWH (China)-to compare consistency and reproducibility of findings across clinical contexts.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data source and study population\u003c/h2\u003e\u003cp\u003eThis retrospective observational study used de-identified electronic health record data from two sources: (i) MIMIC-IV (version 2.0) contains hospital and ICU data from Beth Israel Deaconess Medical Center (Boston, USA) for admissions from 2008 to 2019, prior to the COVID-19 pandemic; all personal identifiers in MIMIC-IV are irreversibly de-identified. (ii) SWH cohort: data were obtained from the Clinical Big Data Center of the First Affiliated Hospital of the Army Medical University (Southwest Hospital, Chongqing, China) for hospitalizations between 2016 and 2023; patient identifiers were de-identified prior to analysis.\u003c/p\u003e\u003cp\u003eTo enhance comparability, patients with confirmed COVID-19 were excluded from both datasets. The study was approved by the Ethics Committee of the First Affiliated Hospital of the Army Medical University (People\u0026rsquo;s Liberation Army) (approval No. KY2024116) with a waiver of informed consent due to de-identified data, and it was registered in the China Clinical Trial Registry (ChiCTR2400086782, registration date: July 10, 2024). All procedures adhered to relevant regulations and the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003eInclusion criteria: age\u0026thinsp;\u0026gt;\u0026thinsp;18 years; first ICU admission during the index hospitalization; ICU length of stay\u0026thinsp;\u0026gt;\u0026thinsp;24 hours; \u0026ge;2 paired measurements of blood glucose and triglycerides to compute TyG variability. Exclusion criteria: (1) confirmed COVID-19; (2) missing key demographic or outcome information. Only the first eligible hospitalization for patients with multiple admissions was retained. The unit of analysis was the first ICU admission within the index hospitalization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data collection\u003c/h2\u003e\u003cp\u003eData from MIMIC-IV were extracted using structured SQL queries (PostgreSQL 11.0). Data from SWH were exported from the institutional data platform and processed with standardized scripts for statistical analysis. Variable definitions and coding were harmonized a priori across the two datasets. Demographics were obtained at admission. Vital signs were averaged over the first 24 hours after ICU admission. For laboratory tests other than blood glucose and triglycerides, the first value after ICU admission was used. Height and weight were taken from measurements within 24 hours before ICU admission, when available.\u003c/p\u003e\u003cp\u003eThe SOFA score was computable in MIMIC-IV based on components within the first 24 hours after ICU admission; SOFA was not uniformly available in SWH. Other covariates included hemoglobin, platelet count, red blood cell count, red cell distribution width, white blood cell count, neutrophil-to-lymphocyte ratio (NLR), albumin, bicarbonate (in MIMIC-IV), creatinine, sodium, calcium, potassium, prothrombin time (PT), activated partial thromboplastin time (PTT), alanine aminotransferase (ALT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), and lipids.\u003c/p\u003e\u003cp\u003eTo mitigate potential bias, following Zhang et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], variables with \u0026gt;\u0026thinsp;50% missingness were excluded from multivariable modeling. For remaining missing data, under the missing-at-random assumption we applied multiple imputation by chained equations with m\u0026thinsp;=\u0026thinsp;10 imputed datasets and 10 iterations each. All covariates were included in the imputation model; the outcome was not imputed. Parameter estimates were combined using Rubin\u0026rsquo;s rules.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Assessment of the TyG index and related parameters\u003c/h2\u003e\u003cp\u003eThe TyG index was calculated as ln[TG (mg/dl) \u0026times; FPG (mg/dl)/2]. To ensure unit consistency, results reported in mmol/L were converted to mg/dL (glucose \u0026times; 18; triglycerides \u0026times; 88.57). Because fasting status was not consistently recorded, we used routinely measured serum glucose and triglyceride values. To construct the TyG time series during the ICU stay, each glucose measurement was paired with the temporally closest triglyceride measurement; if the sampling interval between the pair exceeded 6 hours, no TyG value was generated for that time point. For each patient\u0026rsquo;s ICU TyG series, we then derived the following metrics: TyG-BL (first TyG value after ICU admission), TyG-Median (median value of the TyG index sequence), TyG- Average (mean of the TyG index sequence), TyG-STD (standard deviation of the TyG index sequence), TyG-Range (range of the TyG index sequence, maximum - minimum), and TyG-CV (coefficient of variation, SD/mean). TyG-BL, TyG-Median, and TyG-Mean primarily reflect the absolute level, whereas TyG-STD, TyG-Range, and TyG-CV reflect variability/dispersion over time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Primary and secondary outcomes\u003c/h2\u003e\u003cp\u003eThe primary outcome was in-hospital all-cause mortality during the index hospitalization; the secondary outcome was ICU mortality. For survival summaries, the time origin was ICU admission.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were first assessed for normality and homogeneity of variance. Normally distributed variables with equal variances are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) and compared using the Student\u0026rsquo;s t-test or one-way ANOVA. Non-normally distributed or heteroscedastic variables are presented as median [Q1, Q3] and compared using the Mann-Whitney U test or Kruskal-Wallis test. Categorical variables are summarized as n (%) and compared using the χ\u0026sup2; test or Fisher\u0026rsquo;s exact test. All tests were two-sided, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating statistical significance.\u003c/p\u003e\u003cp\u003eTo examine the association between TyG level/variability and the primary outcome, we classified patients separately within MIMIC-IV and SWH into tertiles (Groups 1\u0026ndash;3: low/middle/high) according to the distribution of each TyG metric in the corresponding dataset. With ICU admission as time zero and discharge as censoring, Kaplan-Meier curves were used to estimate the cumulative incidence of in-hospital death across tertiles, and differences were assessed by the log-rank test. For effect estimation with continuous exposures, we fitted multivariable logistic regression models and reported odds ratios (ORs) with 95% confidence intervals (CIs). Pre-specified adjustment sets were: Model 1, unadjusted (TyG metric only); Model 2, adjusted for age, sex, and BMI; and Model 3, further adjusted for clinical and laboratory covariates supported by prior evidence-SOFA (MIMIC-IV only), hemoglobin, platelet count, red blood cell count, RDW (MIMIC-IV), white blood cell count, NLR, albumin, bicarbonate (MIMIC-IV), creatinine, sodium, calcium, potassium, PT, PTT, ALT, ALP, AST, LDH, and lipids (LDL/HDL in SWH). Because covariate availability differed between datasets, models were fit and reported separately for MIMIC-IV and SWH.\u003c/p\u003e\u003cp\u003eTo assess potential nonlinear relationships between each continuous TyG metric and the outcome, we incorporated restricted cubic splines (RCS) into the regression framework, placing knots at the 5th, 35th, 65th, and 95th percentiles as recommended by Harrell [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; the sample median served as the reference value. Multicollinearity was evaluated using the variance inflation factor (VIF), with VIF\u0026thinsp;\u0026lt;\u0026thinsp;5 considered acceptable. Analyses were performed using Python 3.7.5 and R 4.3.3. Two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline characteristics of study population\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 2,208 ICU patients were included (MIMIC, n\u0026thinsp;=\u0026thinsp;1,707; SWH, n\u0026thinsp;=\u0026thinsp;501). The sex distribution was similar in both cohorts (male 61.2% vs 61.1%). Patients in MIMIC were older (60.6 [47.7\u0026ndash;70.5] years) than those in SWH (56.7 [41.1\u0026ndash;70.3] years). ICU length of stay was comparable (MIMIC 8.6 [3.4\u0026ndash;16.1] vs SWH 8.9 [5.2\u0026ndash;15.4] days), as was hospital length of stay (MIMIC 22.7 [13.4\u0026ndash;37.1] vs SWH 21.0 [13.0\u0026ndash;39.0] days). In-hospital mortality was 23.2% in MIMIC and 14.6% in SWH. Anthropometrics indicated higher weight and BMI in MIMIC (BMI 28.9 [24.5\u0026ndash;34.2] vs 23.6 [23.1\u0026ndash;23.8] kg/m\u0026sup2;).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and Clinical Characteristics of Patients in the MIMIC and SWH Cohorts\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMIMIC (N\u0026thinsp;=\u0026thinsp;1707)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSWH (N\u0026thinsp;=\u0026thinsp;501)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1045 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e306 (61.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e662 (38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e195 (38.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission Age (Years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.6 [47.7, 70.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.7 [41.1, 70.3]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOS (ICU) (Days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.6 [3.4, 16.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.9 [5.2, 15.4]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOS (Hospital) (Days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.7 [13.4, 37.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.0 [13.0, 39.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170.1 [165.0, 175.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162.3 [162.0, 163.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.9 [69.5, 101.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.2 [60.0, 62.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.9 [24.5, 34.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.6 [23.1, 23.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital Expire Flag, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1311 (76.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e428 (85.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e396 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73 (14.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOFA Index (Score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.0 [3.0, 9.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.1 [8.5, 11.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.8 [7.9, 10.3]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet (10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e179.0 [113.0, 253.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155.0 [83.0, 232.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC (10⁶/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.4 [2.8, 4.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.0 [2.6, 3.5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.0 [13.8, 17.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.3 [7.8, 16.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.7 [6.4, 13.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils (10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.9 [6.6, 13.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.9 [5.0, 11.6]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes (10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2 [0.7, 1.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9 [0.6, 1.3]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.9 [5.9, 14.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.4 [5.2, 15.7]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.7 [2.4, 3.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.3 [3.0, 3.6]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBicarbonate (mEq/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.0 [19.0, 25.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1 [0.7, 1.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9 [0.6, 1.9]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium (mEq/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138.0 [135.0, 142.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140.0 [136.6, 144.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.1 [7.5, 8.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.8 [1.8, 2.1]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium (mEq/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.1 [3.7, 4.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.0 [3.7, 4.3]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT (Seconds)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.5 [12.9, 17.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.6 [11.7, 14.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTT (Seconds)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.0 [27.9, 39.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.5 [28.4, 39.5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.0 [16.0, 94.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.1 [12.8, 53.4]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.0 [59.0, 116.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.0 [67.0, 141.1]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.0 [25.0, 140.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.7 [25.4, 96.1]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLD_LDH (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e381.0 [243.0, 615.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e518.4 [293.2, 906.3]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.7 [1.1, 2.3]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6 [0.4, 0.9]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKMB (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.8 [10.4, 28.1]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127.0 [105.0, 161.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.8 [1.2, 2.9]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170.0 [110.5, 275.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.9 [6.2, 10.4]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart Rate (bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.9 [78.8, 105.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92.3 [83.2, 101.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114.9 [104.3, 124.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127.0 [117.0, 135.5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.6 [58.4, 71.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.9 [66.0, 77.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.9 [74.5, 87.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e89.5 [83.7, 96.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory Rate (breaths/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.4 [17.7, 23.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.0 [19.0, 20.7]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.0 [36.7, 37.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.0 [36.7, 37.5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.9 [95.3, 98.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.3 [98.1, 99.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eThis table presents the demographic and clinical characteristics of patients in the MIMIC and SWH cohorts. Continuous variables are reported as median (Q1, Q3), while categorical variables are expressed as counts (n) and percentages (%). For some variables marked with a \" - \" (dash), data collection was not performed or was unavailable in that cohort. Abbreviations: LOS: Length of Stay; ICU: Intensive Care Unit; BMI: Body Mass Index; SOFA: Sequential Organ Failure Assessment; RDW: Red Cell Distribution Width; WBC: White Blood Cell count; RBC: Red Blood Cell count; NLR: Neutrophil-to-Lymphocyte Ratio; PT: Prothrombin Time; PTT: Partial Thromboplastin Time; ALT: Alanine Aminotransferase; ALP: Alkaline Phosphatase; AST: Aspartate Aminotransferase; LD_LDH: Lactate Dehydrogenase; LDL: Low-Density Lipoprotein; HDL: High-Density Lipoprotein; CKMB: Creatine Kinase-MB; SpO\u003csub\u003e2\u003c/sub\u003e: Oxygen Saturation; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; MBP: Mean Blood Pressure.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics and Clinical Outcomes of ICU Patients Stratified by TyG Index Quantile in the MIMIC and SWH Databases\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMIMIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eSWH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow (N\u0026thinsp;=\u0026thinsp;562)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMiddle (N\u0026thinsp;=\u0026thinsp;563)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh (N\u0026thinsp;=\u0026thinsp;581)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow (N\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMiddle (N\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHigh (N\u0026thinsp;=\u0026thinsp;170)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.309\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e232 (41.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e220 (39.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e210 (36.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e59 (35.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72 (43.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e64 (37.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e330 (58.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e343 (60.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e371 (63.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e106 (64.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e93 (56.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e106 (62.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission Age (Years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.2 [53.5,75.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.0 [49.2,70.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.5 [43.1,65.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e63.8 [48.7,73.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58.7 [41.1,70.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e50.5 [36.5,63.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOS (ICU) (Days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.6 [2.6,13.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.7 [3.5,16.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.4 [4.7,18.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.9 [5.6,15.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.2 [4.9,14.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.6 [5.5,15.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOS (Hospital) (Days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.9 [14.2,38.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.3 [14.3,38.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.4 [11.6,34.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.0 [14.0,40.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.0 [13.0,34.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.0 [14.0,39.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170.1 [165.0,173.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170.1 [165.0,175.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e170.1 [165.0,175.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e162.3 [161.0,162.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e162.3 [162.0,163.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e162.3 [161.2,163.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76.3 [63.0,91.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.9 [70.9,100.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.5 [75.3,107.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62.2 [58.0,62.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e62.2 [61.0,62.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e62.2 [60.2,63.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.5 [22.3,30.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.9 [24.8,33.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.1 [26.3,36.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.6 [22.3,23.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.6 [23.2,24.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.6 [23.6,24.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital Expire Flag\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.484\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e435 (77.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e430 (76.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e445 (76.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e139 (84.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e139 (84.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e150 (88.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e127 (22.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133 (23.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (23.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26 (15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOFA Index (Score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.0 [3.0,8.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0 [3.0,9.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.0 [4.0,10.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.0 [8.5,11.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.0 [8.5,11.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.3 [8.5,12.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.0 [8.1,10.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.7 [7.9,10.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.7 [7.7,10.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet (10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180.5 [110.0,257.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e185.0 [120.0,261.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e173.0 [110.0,247.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e160.0 [83.0,232.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e172.0 [86.0,246.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e141.0 [80.0,217.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC (10⁶/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.3 [2.8,3.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.4 [2.8,4.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.5 [2.9,4.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.0 [2.7,3.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.0 [2.6,3.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.9 [2.5,3.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.4 [13.9,17.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.0 [13.9,16.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.8 [13.7,16.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.6 [7.5,15.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.9 [7.8,16.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.6 [7.9,17.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.2 [6.2,13.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.6 [6.9,13.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.1 [6.4,15.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils (10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.6 [5.9,11.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.9 [6.7,12.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.9 [7.1,13.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.6 [4.6,11.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.7 [5.0,11.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.1 [5.1,12.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocytes (10\u0026sup3;/\u0026micro;L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2 [0.7,1.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3 [0.7,1.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3 [0.6,1.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8 [0.5,1.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9 [0.6,1.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.9 [0.6,1.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.465\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.9 [5.6,13.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.9 [5.9,13.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.9 [6.3,14.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.4 [5.1,16.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.4 [5.0,15.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.5 [5.7,16.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.7 [2.4,3.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.7 [2.3,3.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.7 [2.4,3.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.3 [3.0,3.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.3 [3.0,3.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.3 [3.0,3.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBicarbonate (mEq/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.0 [20.0,25.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.0 [19.0,25.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.0 [17.0,24.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 [0.7,1.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1 [0.7,1.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2 [0.8,2.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9 [0.6,1.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.9 [0.6,1.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.0 [0.6,2.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium (mEq/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138.5 [135.0,141.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139.0 [136.0,142.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138.0 [135.0,141.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e139.7 [136.8,143.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e141.0 [136.8,144.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e139.4 [136.5,143.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.1 [7.6,8.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.1 [7.6,8.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.0 [7.5,8.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.8 [1.8,2.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.8 [1.8,2.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.8 [1.8,2.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium (mEq/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.0 [3.7,4.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.1 [3.8,4.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.2 [3.8,4.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.0 [3.7,4.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.1 [3.8,4.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.1 [3.7,4.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.477\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT (Seconds)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.0 [13.1,18.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.3 [12.8,17.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.2 [12.7,16.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.8 [11.8,14.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.7 [11.7,14.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.5 [11.6,13.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTT (Seconds)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.3 [28.8,42.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.6 [27.6,39.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.2 [27.6,39.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.7 [28.2,43.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32.5 [28.8,37.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e32.3 [28.4,39.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.0 [14.0,70.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.0 [15.0,82.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.0 [21.0,130.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.2 [12.5,62.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.3 [14.3,49.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24.0 [11.5,49.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.5 [59.0,109.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.0 [58.0,115.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85.0 [58.0,125.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88.0 [62.0,142.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e86.0 [68.0,121.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e95.5 [71.0,150.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.0 [23.0,111.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.0 [22.0,108.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.0 [31.0,197.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.2 [25.1,91.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e44.8 [26.3,95.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e48.0 [25.2,101.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLD_LDH (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e314.5 [213.0,615.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e354.0 [238.5,615.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e469.0 [292.0,615.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e425.0 [263.0,765.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e501.7 [283.8,906.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e615.1 [351.1,906.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.5 [1.0,2.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.7 [1.1,2.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.9 [1.2,2.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6 [0.4,0.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6 [0.4,0.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.6 [0.4,0.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKMB (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.6 [9.7,28.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.6 [10.5,28.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.1 [10.8,28.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119.0 [101.0,145.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127.0 [107.0,157.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139.0 [110.0,180.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.1 [0.8,1.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.7 [1.3,2.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.3 [2.2,4.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.0 [76.0,133.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e172.0 [133.0,225.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e315.0 [227.0,470.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.8 [5.7,8.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.9 [6.2,10.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.2 [7.0,11.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart Rate (bpm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88.1 [76.8,101.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91.5 [78.8,103.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.7 [83.0,109.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e91.7 [80.5,100.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e91.3 [82.4,100.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e94.2 [86.8,103.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113.1 [102.1,124.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115.4 [104.5,124.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115.5 [105.5,124.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e124.4 [114.6,135.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e127.1 [118.3,138.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e127.1 [119.0,134.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.7 [57.0,69.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.6 [59.4,70.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.6 [59.0,72.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.3 [64.7,75.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72.0 [66.6,78.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e71.0 [66.8,77.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.2 [73.5,87.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.2 [75.0,87.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.2 [75.6,88.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e87.1 [81.5,95.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e90.5 [84.9,97.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e89.6 [84.8,95.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory Rate (breaths/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.6 [17.2,22.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.2 [17.6,23.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.6 [18.7,25.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.0 [18.9,20.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.0 [18.9,20.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.0 [19.2,21.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.9 [36.7,37.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.0 [36.7,37.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.1 [36.8,37.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37.0 [36.7,37.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e37.0 [36.7,37.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37.0 [36.7,37.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO2 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.2 [95.5,98.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.1 [95.6,98.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.5 [94.8,98.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.2 [98.0,99.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e98.4 [98.1,99.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e98.4 [98.1,99.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eThis table presents the demographic and clinical characteristics of patients in the MIMIC and SWH cohorts. Continuous variables are reported as median (Q1, Q3), while categorical variables are expressed as counts (n) and percentages (%). For some variables marked with a \" - \" (dash), data collection was not performed or was unavailable in that cohort. Abbreviations: LOS: Length of Stay; ICU: Intensive Care Unit; BMI: Body Mass Index; SOFA: Sequential Organ Failure Assessment; RDW: Red Cell Distribution Width; WBC: White Blood Cell count; RBC: Red Blood Cell count; NLR: Neutrophil-to-Lymphocyte Ratio; PT: Prothrombin Time; PTT: Partial Thromboplastin Time; ALT: Alanine Aminotransferase; ALP: Alkaline Phosphatase; AST: Aspartate Aminotransferase; LD_LDH: Lactate Dehydrogenase; LDL: Low-Density Lipoprotein; HDL: High-Density Lipoprotein; CKMB: Creatine Kinase-MB; SpO\u003csub\u003e2\u003c/sub\u003e: Oxygen Saturation; SBP: Systolic Blood Pressure; DBP: Diastolic Blood Pressure; MBP: Mean Blood Pressure.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Associations of TyG metrics with in-hospital mortality\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, across both datasets, the variability-type TyG metrics showed a consistent direction of association with in-hospital all-cause mortality. In MIMIC (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,707), after full adjustment for age, sex, BMI, and prespecified clinical/laboratory covariates (Model 3 in Methods), TyG-STD (OR 1.66, 95% CI 1.06\u0026ndash;2.61, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) and TyG-Range (OR 1.24, 95% CI 1.05\u0026ndash;1.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) remained independently associated with mortality; TyG-CV showed a borderline association (OR 1.14, 95% CI 0.98\u0026ndash;1.33, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.084). Among level-type metrics, TyG-Average remained significant (OR 1.21, 95% CI 1.02\u0026ndash;1.44, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), whereas TyG-Median was borderline and TyG-BL was not significant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn SWH (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;501), variability metrics were risk factors in unadjusted/partially adjusted models, but the associations attenuated and became non-significant after full adjustment (e.g., TyG-STD OR 2.18, 95% CI 0.84\u0026ndash;5.66, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.108; TyG-Range OR 1.24, 95% CI 0.84\u0026ndash;1.84, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.275; TyG-CV OR 1.31, 95% CI 0.96\u0026ndash;1.80, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.092). In contrast, TyG-BL was inversely associated with mortality (OR 0.70, 95% CI 0.51\u0026ndash;0.97, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Kaplan\u0026ndash;Meier Analysis of In-Hospital Mortality Across TyG Tertiles\u003c/h2\u003e\u003cp\u003eWhen TyG metrics were stratified into cohort-specific tertiles, higher TyG values were observed more often in younger patients and were associated with stepwise increases in BMI, glucose, and triglycerides. In MIMIC, higher TyG was also associated with longer ICU length of stay, modestly higher heart and respiratory rates and temperature, lower bicarbonate, and higher ALT/AST/LDH; in SWH, LDH increased with TyG and mean blood pressure was slightly higher. Despite these patterns, crude in-hospital mortality proportions across baseline-TyG tertiles were similar within each cohort (approximately 23% in MIMIC and 12\u0026ndash;16% in SWH).\u003c/p\u003e\u003cp\u003eKaplan\u0026ndash;Meier analyses showed that in MIMIC, tertiles of both level-type (Average, Median) and variability-type (SD, Range, CV) TyG metrics exhibited clear separation of survival curves with progressively higher cumulative incidence of in-hospital death (most log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas tertiles based solely on baseline TyG did not discriminate risk (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In SWH, the overall direction was similar but separation was weaker; only CV tertiles differed significantly (log-rank P\u0026thinsp;=\u0026thinsp;0.017), whereas Baseline, Average, Median, SD, and Range did not.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Nonlinear Associations of TyG Level and Variability with In-Hospital Mortality\u003c/h2\u003e\u003cp\u003eIn MIMIC, multivariable logistic models incorporating restricted cubic splines revealed significant, often nonlinear associations between TyG metrics and in-hospital mortality: TyG-Average (overall \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038; nonlinearity \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), TyG-Median (0.004; 0.006), TyG-STD (\u0026lt;\u0026thinsp;0.001; 0.013), TyG-Range (\u0026lt;\u0026thinsp;0.001; 0.009), and TyG-CV (0.003; 0.021). The risk increased with higher values, with a steeper slope in the upper range, suggesting threshold/saturation-like behavior. TyG-BL showed no association (overall \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.542).\u003c/p\u003e\u003cp\u003eIn SWH, overall and nonlinearity tests for all TyG metrics were not significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and the spline curves were relatively flat with wide confidence bands, consistent with smaller sample size and differences in covariate availability.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we used data from the MIMIC and SWH databases to examine the relationship between TyG index variability and all-cause in-hospital mortality. We found that indicators of TyG variability (TyG-Range, TyG-STD, TyG-CV) were independently associated with in-hospital mortality, whereas a single (baseline) TyG value had limited risk stratification ability and showed a negative correlation after extensive adjustment in the SWH database. RCS revealed a non-linear exposure\u0026ndash;risk relationship in MIMIC, with a steeper increase in risk at higher values. In contrast, the SWH curve was flatter, which can be attributed to the smaller sample size and differences in covariate availability. Overall, dynamic/time-series information provided a better reflection of short-term prognosis in critically ill patients compared to a single baseline value.\u003c/p\u003e\u003cp\u003ePrevious studies have shown that insulin resistance (IR) in ICU populations is associated with stress, inflammation, and immune dysregulation, all linked to poor outcomes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, standard IR assessments, such as the clamp test or HOMA-IR, are not routinely feasible in ICU settings [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our work, which utilized routine clinical tests (glucose, triglycerides), developed the TyG index and its time-series parameters, supporting the evidence that variability, rather than merely the level, is more closely associated with mortality risk. This provides a new perspective on dynamic metabolic risk characterization in the ICU. As an alternative marker for IR, TyG has been validated in multiple studies and populations, with predictive performance equal to or superior to HOMA-IR, further supporting its feasibility for ICU applications [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTyG reflects glucose and lipid utilization/energy status, and its variability may represent a composite of factors including stress hormone fluctuations, inflammation-mediated hepatic lipid metabolism alterations, unstable nutritional input, and adjustments in insulin/lipid-lowering therapy [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Similar to blood glucose variability, which is known to correlate with mortality risk in the ICU independent of average blood glucose levels [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], TyG variability captures a broader metabolic instability phenotype (oxidative stress, endothelial dysfunction, immune imbalance) beyond transient levels [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn MIMIC, we observed a steeper slope in the high-value segment, consistent with a \"threshold/saturation\" effect [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In contrast, baseline TyG in SWH was negatively correlated, which could be influenced by initial illness, nutritional and insulin/lipid-lowering treatments, malnutrition, or residual confounding. This may also relate to differences in sample size and sampling timepoints[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Metabolic phenotype differences may alter the clinical meaning of \"absolute TyG values.\" MIMIC patients were generally heavier, with a higher proportion of overweight/obese individuals (BMI distribution shifted right), and higher TyG values likely reflected increased insulin resistance and lipid burden [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In contrast, SWH patients were generally smaller in stature, and in this context, \"low/high\" absolute TyG values may reflect different metabolic reserves and nutritional states: very low glucose/triglycerides may indicate malnutrition or low metabolic reserves, presenting an \"apparent protection\" effect [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Furthermore, differences in ICU admission phases and treatment pathways were noted. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that vital signs on the first day of admission were more unstable in MIMIC, while SWH patients were more stable, consistent with the high-intensity resuscitation followed by ICU transfer at SWH. In this context, baseline TyG may be influenced by sampling time: higher TyG in MIMIC may reflect uncontrolled stress/metabolic pressure, while higher TyG in SWH may partially represent recovery post-resuscitation and improved metabolic energy and nutritional input.\u003c/p\u003e\u003cp\u003eIn contrast, variability indices capture information throughout the entire hospitalization, being less prone to bias from single-time measurements, thus exhibiting more consistent cross-database performance. Kaplan-Meier analysis by tertiles showed that most TyG variability indices presented the lowest risk in the middle group, with higher risks at both ends, suggesting the potential existence of a U-shaped or threshold/saturation effect. RCS further showed that the risk increased sharply in the higher value segment in MIMIC, consistent with the biological intuition that \"greater metabolic fluctuation, higher risk.\" Combining both types of analysis, we speculate that relative stability within a certain range (neither excessive fluctuation nor extremely stable \"low energy/low fat\" states) might correspond to better short-term prognosis. This finding provides a basis for incorporating metabolic stability, beyond focusing solely on absolute levels, into risk assessments [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough this study does not provide causal inference and has not defined therapeutic targets, the results support the need for prospective studies to evaluate the potential benefits of reducing metabolic fluctuations (e.g., more consistent nutritional support, integrated glucose-lipid monitoring, rational use of corticosteroids/vasopressors) and exploring non-linear risk inflection points and actionable thresholds. Evidence of cumulative/long-term TyG exposure increasing cardiovascular event risk further supports the biological rationale for \"stable metabolic exposure\" in general and specific populations [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e This study was conducted using data from two databases with different healthcare systems and care models, enhancing the reproducibility and robustness of the results. Variables were standardized prior to the study, and key lab parameters were normalized to reduce inter-database heterogeneity. The study analyzed TyG levels and time-series fluctuations using RCS to explore non-linear exposure-risk relationships, avoiding model misfit due to simple linear assumptions. Missing data were handled with MICE multiple imputation under the MAR assumption, with separate model construction for each database, in accordance with STROBE guidelines.\u003c/p\u003e\u003cp\u003eThis retrospective study cannot fully eliminate residual confounding (e.g., nutritional pathways, insulin/lipid-lowering treatments). TyG, derived from non-fasting routine tests, may introduce measurement variability, and the choice of pairing window could affect fluctuation estimates. Covariate availability differed between databases, potentially leading to differential adjustment. The relatively small SWH sample size resulted in reduced statistical precision. Logistic regression was used for in-hospital mortality, but survival or competing risk models may have been more appropriate. Multiple TyG indices were assessed, raising the risk of Type I error; further validation is needed. Finally, the external validity is limited by differences in ICU patient composition and protocols, requiring confirmation in broader populations and prospective studies [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study found that TyG variability (TyG_Range, TyG_STD, TyG_CV) is independently associated with in-hospital mortality, whereas a single baseline TyG value has limited predictive ability. Dynamic time-series information provided a more accurate short-term prognosis for critically ill patients than static measurements. Our findings suggest that metabolic instability, reflected by TyG variability, may be a better predictor of mortality risk in the ICU than static values, emphasizing the need to consider metabolic stability in risk assessments. These results warrant further prospective studies to validate TyG variability as a predictive tool for adverse outcomes in ICU patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTyG: Triglyceride-Glucose\u003c/p\u003e\n\u003cp\u003eIR: Insulin Resistance\u003c/p\u003e\n\u003cp\u003eICU: Intensive Care Unit\u003c/p\u003e\n\u003cp\u003eMIMIC-IV: Medical Information Mart for Intensive Care, version IV\u003c/p\u003e\n\u003cp\u003eSWH: Southwest Hospital\u003c/p\u003e\n\u003cp\u003eSOFA: Sequential Organ Failure Assessment\u003c/p\u003e\n\u003cp\u003eNLR: Neutrophil-to-Lymphocyte Ratio\u003c/p\u003e\n\u003cp\u003eALT: Alanine Aminotransferase\u003c/p\u003e\n\u003cp\u003eAST: Aspartate Aminotransferase\u003c/p\u003e\n\u003cp\u003eALP: Alkaline Phosphatase\u003c/p\u003e\n\u003cp\u003eLDH: Lactate Dehydrogenase\u003c/p\u003e\n\u003cp\u003ePT: Prothrombin Time\u003c/p\u003e\n\u003cp\u003ePTT: Activated Partial Thromboplastin Time\u003c/p\u003e\n\u003cp\u003eHOMA-IR: Homeostasis Model Assessment of Insulin Resistance\u003c/p\u003e\n\u003cp\u003eRCS: Restricted Cubic Splines\u003c/p\u003e\n\u003cp\u003eCV: Coefficient of Variation\u003c/p\u003e\n\u003cp\u003eBL: Baseline\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003eOR: Odds Ratio\u003c/p\u003e\n\u003cp\u003eCI: Confidence Interval\u003c/p\u003e\n\u003cp\u003eSD: Standard Deviation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was approved by\u0026nbsp;Ethics Committee of the First Affiliated Hospital of the PLA Army Military Medical University (Approval\u0026nbsp;number:\u0026nbsp;B KY2024116).\u0026nbsp;All participants were informed about the study protocol and provided written informed consent to participate in the study.\u0026nbsp;I confirm that all methods were performed in accordance with the relevant guidelines. All procedures were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article. The data not published within this article are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by\u0026nbsp;Science and Technology Research Project of the Education Commission of Chongqing City (KJQN202512837) and Medical Research Project of Chengdu Health Commission of Sichuan Province (2021061)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZuzhi Chen conceptualized the research, wrote the methodology, conducted the survey and formal analysis, visualized the data and wrote the original draft, and reviewed and edited the manuscript. Xiang Xiang visualized the data and wrote the original draft, and reviewed and edited the manuscript. Changlin Yin conceived the concept for the study, assisted with the research methodology, supervised Zuzhi Chen, Xiang Xiang, and Haoran Xu, and reviewed and edited the paper. Haoran Xu is the second reviewer, responsible for the selection and full text evaluation. Ting Zhao and Weiguang Zhang contributed to the creation and review of the images and tables. Xiaofei Xie and Zhi Dou reviewed and edited the manuscript. Yonghui Zhang reviewed and edited the manuscript. Hailin Shu reviewed and edited the manuscript. Changlin Yin and Haoran Xu are the guarantors of this work, and as such, they have full access to all the data in the study and are responsible for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to express their sincere gratitude to the patient and family for their time and co-operation\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSungono V, Hariyanto H, Soesilo TEB, et al: Cohort study of the APACHE II score and mortality for different types of intensive care unit patients. Postgrad Med J 2022, 98:914-918.\u003c/li\u003e\n \u003cli\u003eHerridge MS, Azoulay \u0026Eacute;: Outcomes after Critical Illness. N Engl J Med 2023, 388:913-924.\u003c/li\u003e\n \u003cli\u003eCerro G, Checkley W: Global analysis of critical care burden. 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Sci Rep 2024, 14:10128.\u003c/li\u003e\n \u003cli\u003eZhang Q, Xiao S, Jiao X, et al: The triglyceride-glucose index is a predictor for cardiovascular and all-cause mortality in CVD patients with diabetes or pre-diabetes: evidence from NHANES 2001-2018. Cardiovasc Diabetol 2023, 22:279.\u003c/li\u003e\n \u003cli\u003eChen F, Pan Y, Liu Z, et al: Impact of Visit-to-Visit Triglyceride-Glucose Index Variability on the Risk of Cardiovascular Disease in the Elderly. Int J Endocrinol 2022, 2022:5125884.\u003c/li\u003e\n \u003cli\u003eLi H, Zuo Y, Qian F, et al: Triglyceride-glucose index variability and incident cardiovascular disease: a prospective cohort study. Cardiovasc Diabetol 2022, 21:105.\u003c/li\u003e\n \u003cli\u003eShao Y, Gan Z, Wang T, et al: Correlation of the triglyceride-glucose index and heart rate with 28-day all-cause mortality in severely ill patients: analysis of the MIMIC-IV database. Lipids Health Dis 2024, 23:387.\u003c/li\u003e\n \u003cli\u003eMelis MJ, Miller M, Peters VBM, et al: The role of hormones in sepsis: an integrated overview with a focus on mitochondrial and immune cell dysfunction. Clin Sci (Lond) 2023, 137:707-725.\u003c/li\u003e\n \u003cli\u003eCheng L, Zhang F, Xue W, et al: Association of dynamic change of triglyceride-glucose index during hospital stay with all-cause mortality in critically ill patients: a retrospective cohort study from MIMIC IV2.0. Cardiovasc Diabetol 2023, 22:142.\u003c/li\u003e\n \u003cli\u003eWang W-Q, Chen M-Z, Yang Y-H: Triglyceride-glucose index and 28-day all-cause mortality in critically ill obese patients: A MIMIC-IV database analysis. J Clin Lipidol 2025, 19:960-968.\u003c/li\u003e\n \u003cli\u003ePan W, Ji T-F, Hu B-T, et al: Association between triglyceride glucose body mass index and 1 year all-cause mortality in stage 4 CKM syndrome patients. Sci Rep 2025, 15:17019.\u003c/li\u003e\n \u003cli\u003eLiao Y, Zhang R, Shi S, et al: Triglyceride-glucose index linked to all-cause mortality in critically ill patients: a cohort of 3026 patients. Cardiovasc Diabetol 2022, 21:128.\u003c/li\u003e\n \u003cli\u003eCui H, Liu Q, Wu Y, et al: Cumulative triglyceride-glucose index is a risk for CVD: a prospective cohort study. Cardiovasc Diabetol 2022, 21:22.\u003c/li\u003e\n \u003cli\u003eWang X, Feng B, Huang Z, et al: Relationship of cumulative exposure to the triglyceride-glucose index with ischemic stroke: a 9-year prospective study in the Kailuan cohort. Cardiovasc Diabetol 2022, 21:66.\u003c/li\u003e\n \u003cli\u003eSchuster NA, Rijnhart JJM, Twisk JWR, et al: Modeling non-linear relationships in epidemiological data: The application and interpretation of spline models. Front Epidemiol 2022, 2:975380.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"TyG index, insulin resistance, ICU mortality, metabolic variability, restricted cubic splines, MIMIC-IV, SWH","lastPublishedDoi":"10.21203/rs.3.rs-7838255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7838255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eThis study aims to evaluate the relationship between TyG variability and in-hospital mortality across two ICU databases (MIMIC-IV and SWH) and to determine if TyG variability provides more prognostic value than baseline TyG.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective observational study utilized data from the MIMIC-IV (2008-2019) and SWH (2016-2023) ICU cohorts. TyG metrics, including baseline (TyG-BL), median (TyG-Median), mean (TyG-Average), standard deviation (TyG-STD), range (TyG-Range), and coefficient of variation (TyG-CV), were calculated. The association between TyG metrics and all-cause in-hospital mortality was evaluated using multivariable logistic regression. Nonlinear relationships were explored using restricted cubic splines (RCS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 2,208 ICU patients were included (MIMIC: n = 1,707; SWH: n = 501). In MIMIC, TyG variability metrics (TyG-STD, TyG-Range) were independently associated with mortality, with TyG-STD showing an OR of 1.66 (95% CI 1.06–2.61, P = 0.027) and TyG-Range an OR of 1.24 (95% CI 1.05–1.46, P = 0.011). TyG variability metrics in SWH showed similar trends, but associations attenuated after full adjustment. Kaplan-Meier analysis demonstrated clear survival curve separation for TyG variability metrics in MIMIC, while the SWH cohort showed weaker separation. RCS analysis revealed a nonlinear relationship between TyG metrics and mortality risk in MIMIC, with a steeper increase in risk at higher TyG values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eTyG variability, rather than baseline TyG values, is independently associated with in-hospital mortality in critically ill patients, with a stronger association observed in the MIMIC cohort. These findings suggest that TyG variability reflects metabolic instability and may serve as a better predictor of ICU mortality. Future prospective studies are needed to validate TyG variability as a predictive tool in ICU risk assessments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Association of triglyceride-glucose index fluctuation with in-hospital all-cause mortality in critically ill patients: A Multidatabase Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 16:48:05","doi":"10.21203/rs.3.rs-7838255/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-12T08:07:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T22:04:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T16:43:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41849393639242420910710521714099475131","date":"2025-11-10T21:59:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201924772075953089172725193911087983341","date":"2025-10-27T14:26:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T14:15:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-15T13:05:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-13T06:23:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-13T06:22:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-12T03:53:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"94fc2675-6b98-4487-b8ab-bd531cfb37b6","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57217733,"name":"Health sciences/Biomarkers"},{"id":57217734,"name":"Health sciences/Diseases"},{"id":57217735,"name":"Health sciences/Health care"},{"id":57217736,"name":"Health sciences/Medical research"},{"id":57217737,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-03-23T16:06:21+00:00","versionOfRecord":{"articleIdentity":"rs-7838255","link":"https://doi.org/10.1038/s41598-026-42020-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-18 15:59:26","publishedOnDateReadable":"March 18th, 2026"},"versionCreatedAt":"2025-11-06 16:48:05","video":"","vorDoi":"10.1038/s41598-026-42020-1","vorDoiUrl":"https://doi.org/10.1038/s41598-026-42020-1","workflowStages":[]},"version":"v1","identity":"rs-7838255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7838255","identity":"rs-7838255","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}