Differential impact of TyG and TyG-BMI indices on short- and long-term mortality in critically ill ischemic stroke patients.

Abstract

Background: Ischemic stroke is a major contributor to global morbidity and mortality, particularly in critically ill patients in intensive care units (ICUs). While advances in stroke management have improved outcomes, predicting mortality remains challenging due to the involvement of complex metabolic and cardiovascular factors. The triglyceride-glucose (TyG) index, a marker for insulin resistance, has gained attention for its potential to predict adverse outcomes in stroke patients. Furthermore, the TyG-BMI index, which combines TyG with body mass index (BMI), may offer a more comprehensive measure by accounting for obesity-related metabolic burden. However, the comparative impact of these indices on short- and long-term mortality among critically ill ischemic stroke patients remains unclear.

Methods: This retrospective cohort study analyzed data from the Medical Information Mart for Intensive Care IV (MIMIC-IV 3.0) database, including 1,334 critically ill ischemic stroke patients. The patients were divided into four groups based on TyG and TyG-BMI quartiles, respectively. Cox proportional hazards models were employed to assess the association of these indices with 30-day, 90-day, 180-day, and 1-year all-cause mortality (ACM). Kaplan-Meier survival analysis was used to compare survival rates across different index levels. We utilized restricted cubic splines (RCS) to examine the association between the TyG, TyG-BMI index and the specified outcomes. Furthermore, TyG and TyG-BMI index were utilized to establish logistic regression models for mortality across different time periods, and corresponding Receiver Operating Characteristic (ROC) curves were generated.

Results: Kaplan-Meier survival analysis show that Higher TyG levels were associated with significantly increased mortality risk at all time points, with patients in the highest TyG quartile exhibiting the greatest risk. Conversely, patients having a lower TyG-BMI level faced a heightened risk of long-term ACM. The RCS analysis results demonstrated that the TyG index did not exhibit a statistically significant nonlinear relationship with mortality across all time points. However, a significant nonlinear relationship was observed between the TyG index and long-term mortality. From the ROC curve, it can be observed that TyG performs better in predicting short-term mortality. Conversely, TyG-BMI demonstrates superior performance in predicting long-term mortality. The analysis revealed that while the TyG index alone is a strong predictor of mortality, the TyG-BMI index enhances the ability to predict long-term outcomes.

Conclusion: This finding suggests both the TyG and TyG-BMI indices serve as valuable predictors of mortality in critically ill ischemic stroke patients. However, significant differences were observed across the various follow-up periods. Based on the distinct characteristics of these two indicators, future research should focus on the selective integration of TyG and TyG-BMI indices into clinical risk assessment models, tailored to the metabolic profiles of ischemic stroke patients in the ICU. This approach could enhance the precision of mortality risk stratification and optimize patient management strategies.