Association between triglyceride–glucose index and mortality in critically ill patients with atrial fibrillation: a retrospective cohort study

Abstract

BACKGROUND: The triglyceride-glucose (TyG) index, an emerging surrogate marker of insulin resistance, has been implicated in adverse cardiovascular outcomes. However, its prognostic value in critically ill patients with atrial fibrillation (AF) remains unclear. This study aimed to investigate the association between the TyG index and all-cause mortality in this high-risk population. METHODS: We identified critically ill patients with AF from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and categorized them into tertiles based on their TyG index levels. The primary outcome was 30-day mortality, with 90-day and 365-day all-cause mortality as secondary outcomes. Cox proportional hazards regression analysis and restricted cubic splines were used to elucidate the relationship between the TyG index and all-cause mortality. Kaplan-Meier survival analysis was performed to visualize survival differences among the tertiles. RESULTS: A total of 1473 patients were included; the 30-day, 90-day, and 365-day all-cause mortality rates were 26.8%, 33.3%, and 41.1%, respectively. Multivariate Cox proportional hazards analysis revealed that the TyG index was independently associated with mortality at 30 days [hazard ratio (HR) (95% confidence interval (CI)) 1.26 (1.09-1.45), P = 0.002], 90 days [HR (95% CI) 1.27 (1.11-1.45), P < 0.001], and 365 days [HR (95% CI) 1.24 (1.10-1.40), P < 0.001]. Restricted cubic splines regression showed a positive linear association between the TyG index and mortality risk. Kaplan-Meier survival curves further confirmed the significant survival disparities across TyG index tertiles. CONCLUSIONS: A significant linear association was observed between higher TyG index and increased all-cause mortality at 30, 90, and 365 days in critically ill patients with AF. This underscores the role of the TyG index as a key prognostic indicator for risk stratification and management in intensive care.