Triglyceride-glucose index and mortality in critically ill patients with cardiovascular-kidney-metabolic syndrome stage 4

Figures

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  Fig. 1. Flowchart of patient selection. From the MIMIC-IV database (version 3.1), adult patients (≥ 18 years) with CKM stage 4 who were admitted to the ICU were initially identified (n = 12,272). Patients were excluded if they had multiple ICU admissions (only the first ICU stay was retained), an ICU stay of less than 24 h, or missing triglyceride, glucose, or BMI measurements within the first 24 h after ICU admission (n = 9,147). The final analytic cohort consisted of 3,125 patients, who were categorized into three groups according to TyG index tertiles: Q1 (n = 1,042), Q2 (n = 1,041), and Q3 (n = 1,042)

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  Fig. 2. Kaplan-Meier survival curves for 30-day, 90-day, and 365-day all-cause mortality, stratified by TyG index tertiles. Panel A shows the 30-day all-cause mortality, Panel B shows the 90-day all-cause mortality, and Panel C shows the 365-day all-cause mortality. The mortality rate in the high TyG group (Q3) was consistently higher than in the low TyG group (Q1), with statistically significant differences observed in all comparisons (log-rank P < 0.0001 for all)

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  Fig. 3. Subgroup analysis of TyG index and all-cause mortality risk. Panel A presents the 30-day mortality risk stratified by key variables, and Panel B presents the 365-day mortality risk. Hazard ratios (HR) and 95% confidence intervals (CI) are shown for each subgroup. The P_for_interaction values highlight significant interactions (P_for_interaction < 0.05), indicating that the TyG index has differential predictive value in certain subgroups. Notably, a significant interaction was observed in patients with diabetes mellitus (DM) and hypertension (HTN), suggesting that the TyG index may be more predictive in these populations (P < 0.05)

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  Fig. 4. RCS regression analysis of the TyG index and all-cause mortality. Panel A presents the association between the TyG index and 30-day all-cause mortality, and Panel B shows the association with 365-day all-cause mortality. Both curves were generated using the fully adjusted Cox proportional hazards model. The fully adjusted model incorporated demographic characteristics (age, sex, race, BMI), vital signs (heart rate, systolic and diastolic blood pressure, body temperature), laboratory variables (creatinine, blood urea nitrogen, sodium, potassium, calcium, chloride, anion gap, hemoglobin, white blood cell count, platelet count), comorbidities (diabetes mellitus, hypertension, atrial fibrillation, chronic kidney disease, heart failure, myocardial infarction, ischemic heart disease, cerebrovascular accident, peripheral vascular disease), severity scores (SOFA, APS III), and ICU interventions (renal replacement therapy, mechanical ventilation). The RCS models demonstrated a linear association between the TyG index and mortality, with no evidence of nonlinearity (P for nonlinearity = 0.289 for 30-day mortality; 0.145 for 365-day mortality)