Joint effect of triglyceride-glucose index and low-density lipoprotein cholesterol on mortality and days alive out of hospital in acute stroke: a multi-center machine learning-based cohort study.

OBJECTIVE: The lipid paradox suggests low LDL-C is paradoxically associated with poor outcomes, yet its joint effect with insulin resistance in acute stroke remains unclear. This study aimed to evaluate the combined association of triglyceride-glucose (TyG) index and LDL-C with mortality and days alive out of hospital (DAOH) METHODS: This multi-center cohort study used MIMIC as the derivation cohort and eICU-CRD as external validation. Multivariable regression, survival analysis, and RCS were used to assess the associations of TyG and LDL-C with mortality and DAOH90. Mediation analysis explored potential mechanisms. Boruta and LASSO selected features for ten machine learning models, with SHAP for interpretation. RESULTS: Among 3,028 MIMIC and 2,385 eICU patients, TyG showed a positive linear association with mortality, while LDL-C exhibited an L-shaped association. The low LDL-C + high TyG group had the highest in-hospital mortality (OR = 3.36, 95%CI: 2.37-4.76) and shortest DAOH90. Inpatient statin use was associated with lower mortality. WBC statistically accounted for 15.46% of the observed association between TyG and in-hospital mortality. Among multivariable machine-learning models incorporating nine selected predictors, GBM achieved the best performance (AUC = 0.802). CONCLUSIONS: High TyG combined with low LDL-C identifies a high-risk phenotype in acute stroke, significantly associated with adverse outcomes. TyG and LDL-C may serve as accessible biomarkers for risk stratification in acute stroke.