Development and validation of the C-reactive protein–triglyceride-glucose index for predicting short- and long-term mortality in critically ill patients with coronary artery disease: a multicenter cohort study

Background: The C-reactive protein-triglyceride glucose index (CTI) has been proposed as a novel biomarker of insulin resistance and inflammation, but its association with mortality in critically ill patients with coronary artery disease (CAD) remains unclear. This study aimed to evaluate the associations between the CTI and both short- and long-term all-cause mortality and to assess the predictive value of this index. Methods: Patients with CAD were identified from the MIMIC-IV database and divided into internal training and testing cohorts, while an external validation cohort was derived from the eICU-CRD and Shenzhen Regional Health Information Platform (SRHIP) database. Based on the optimal CTI cut-off values, patients were grouped into three categories. The primary outcomes were short-term (30-day) and long-term (365-day) all-cause mortality. Associations between the CTI and mortality were examined using Kaplan-Meier curves, restricted cubic spline regression, and Cox proportional hazards models. Subgroup, mediation and sensitivity analyses tested result robustness. The CTI was further compared with other single predictors, and six machine learning (ML) models were built to assess its predictive performance. Finally, the SHapley Additive exPlanations (SHAP) analysis identified feature contributions, and a user-friendly web application was developed. Results: The primary cohort included 1,561 patients, and two external validation cohorts included 242 and 105 patients from the eICU-CRD and SRHIP databases. High CTI values were significantly associated with increased short- and long-term mortality, demonstrating a nonlinear dose-response relationship. The CTI exhibited particularly high predictive value for short-term outcomes. The incorporation of the CTI into ML models notably improved the predictive performance, and this improvement was confirmed in the external validation cohort. Conclusions: The CTI was identified as an independent predictor of short- and long-term mortality in critically ill patients with CAD, with a particularly high predictive value for short-term risk stratification. Integrating the CTI into predictive models significantly increased the prognostic accuracy.