CTI Index for Predicting Mortality in Critically Ill Coronary Artery Disease Patients

A multicenter cohort study developed and validated the C-reactive protein–triglyceride-glucose index (CTI) for mortality prediction. The primary cohort includes 1,561 patients from the MIMIC-IV database, with external validation using 242 and 105 patients from the eICU-CRD and SRHIP databases. The study, authored by Jian Yang and last updated in May 2026, found high CTI values significantly associated with increased short- and long-term mortality.

Use Cases

• Predicting 30-day mortality risk in critically ill CAD patients based on the CTI biomarker.
• Assessing long-term (365-day) survival prognosis using a combined inflammation and insulin resistance index.
• Comparing the predictive performance of the CTI against other single biomarkers for risk stratification.
• Building machine learning models for mortality prediction incorporating the CTI as a feature.
• Conducting sensitivity and subgroup analyses to test the robustness of biomarker associations with mortality.

Strengths

• Multicenter validation using three distinct databases (MIMIC-IV, eICU-CRD, SRHIP).
• Study evaluates both short-term (30-day) and long-term (365-day) mortality outcomes.
• Analysis includes six machine learning models and external validation of predictive performance.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the primary dataset is unknown, which may limit suitability assessment.
• Data may reflect geographic and institutional bias inherent to the specific ICU databases used.

Provenance

Source

MIMIC-IV, eICU-CRD, and Shenzhen Regional Health Information Platform (SRHIP) databases.

Collection Method

Retrospective multicenter cohort study identifying patients with coronary artery disease.

Freshness

Last updated 2026-05-13 04:45:22; freshness should be verified.