Exploring the prognostic impact of triglyceride-glucose index in critically ill patients with first-ever stroke: insights from traditional methods and machine learning-based mortality prediction.

ABSTRACT:

Background

The incidence and mortality of first-ever strokes have risen sharply, especially in the intensive care unit (ICU). Emerging surrogate for insulin resistance, triglyceride-glucose index (TyG), has been linked to stroke prognosis. We aims to explore the relationships between TyG with ICU all-cause mortality and other prognosis, and to develop machine learning (ML) models in predicting ICU all-cause mortality in the first-ever strokes.

Methods

We included first-ever stroke patients from the eICU Collaborative Research Database in 2014-2015 as the primary analysis cohort (then divided into training and internal validation cohorts) and from local hospital's ICUs as the external validation cohort. Multivariate Cox proportional hazards models and restricted cubic spline

SUBMITTER: Chen Y 

PROVIDER: S-EPMC11658255 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature