MOY 01 Prediction Models for Surgical Site Infections in Gastrointestinal Surgery: A Systematic Review of Regression and Artificial Intelligence Approaches

Abstract

Surgical site infections (SSIs) are a common complication in gastrointestinal surgery, leading to major morbidity, mortality, and economic cost. There is a paucity of prediction models available for SSIs to improve the identification of patients at risk of an SSI. This review aims to evaluate the performance, validation, and methodological quality of prediction models for SSI in gastrointestinal surgery. A systematic review was conducted of MEDLINE, Embase, and Web of Science databases from January 1, 2015, to July 3, 2025. The primary outcome was discriminative performance (area under the receiver operating characteristic curve [AUROC]). Secondary outcomes included calibration, clinical utility assessment, and validation. From 7,692 records, 40 studies met the inclusion criteria, describing 129 distinct prediction models (86 regression-based and 37 machine learning/artificial intelligence-based). SSI incidence varied from 0.7% to 54.8%. AUROC for regression models ranged from 0.49 to 0.997 (median 0.76), and for ML/AI models from 0.50 to 0.991 (median 0.67). 27 models (20.93%) reported any form of calibration, and only 13 models (10.07%) showed a decision curve analysis.9 studies (47.50%) performed some form of external validation either of their new score and/or of a previous score, and 7 studies (17.5%) performed no validation of their newly developed score. Contemporary SSI prediction models for gastrointestinal surgery remain characterised by inadequate validation, poor calibration reporting, insufficient assessment of clinical utility, and limited integration into electronic health records. These significant barriers must be addressed in future model development and validation to affect clinical practice.