Evaluation of the Trauma and Injury Severity Score (TRISS) Performance in Intensive Care Trauma Patients: A...

The Trauma and Injury Severity Score (TRISS) is a widely implemented tool for predicting outcomes in trauma patients. However, the application of the TRISS in intensive care units (ICUs) is limited, and data from international sources are scarce, as it requires adaptation to the demands of the specialized ICU environment. This article aimed to evaluate the predictive performance of the TRISS in critically ill adult patients with polytrauma admitted to ICU and to compare its prognostic accuracy against established ICU scoring systems. A comprehensive literature search was conducted across PubMed, Scopus, ScienceDirect, and CINAHL, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The review included studies evaluating the predictive performance of TRISS in adult polytrauma patients admitted to the ICU. Two reviewers independently conducted study selection, data extraction, and risk of bias assessment. Cohen's d values were synthesized via random-effects meta-analysis. Heterogeneity and publication bias were assessed using I² statistics and Egger's test, respectively. The combined searches yielded 4,179 records, of which 24 studies were included in the systematic review and 21 in the meta-analysis. TRISS demonstrated a strong ability to discriminate between survivors and non-survivors, with a pooled effect size of Cohen's d of -1.54 (95% CI: -1.73 to -1.35, p<0.001). In subgroup analyses based on equivalent study sets, Cohen's d values were -1.55 for TRISS and 1.53 for Acute Physiology And Chronic Health Evaluation II (APACHE II), -1.20 for TRISS and 1.53 for APACHE III, and -1.71 for TRISS and 1.40 for Sequential Organ Failure Assessment (SOFA). Despite considerable heterogeneity among studies (I²>99%), no significant publication bias was detected. These findings indicate that TRISS has comparable prognostic accuracy to established ICU scoring systems. Additional clinical variables may further enhance predictive performance in critical care.