# Systematic review of risk prediction models for surgical site infection after abdominal surgery in adults

## TL;DR

This systematic review evaluates risk prediction models for surgical site infections after abdominal surgery, finding many with good performance but high bias.

## Contribution

The study provides a comprehensive overview of existing models and highlights the need for improved validation and generalizability.

## Key findings

- 25 out of 28 models showed good predictive performance (AUC > 0.7).
- All studies had a high risk of bias.
- Common predictors included surgical duration, diabetes, BMI, and ASA score.

## Abstract

To systematically review risk prediction models for surgical site infection (SSI) after abdominal surgery and to provide a reference for clinical risk management.

A comprehensive search was conducted in Web of Science, Cochrane Library, PubMed, Sinomed, Chinese Medical Journal Full-text Database, CNKI, VIP, and Wanfang Data for studies published from January 1, 1980, to August 12, 2024. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability of the models.

A total of 25 studies were included, involving 28 SSI risk prediction models after abdominal surgery. Among them, 25 models showed good predictive performance (AUC > 0.7), but all studies exhibited a high risk of bias. The most frequently included predictors were surgical duration, diabetes, BMI (body mass index), serum albumin levels, ASA (American Society of Anesthesiologists) physical status score, age, intraoperative blood loss, wound classification, and open surgery.

Risk prediction models for SSI after abdominal surgery are still in the developmental stage. Future studies should emphasize model construction and validation to improve their clinical utility and generalizability.

https://www.crd.york.ac.uk/PROSPERO/view/CRD42024576543, Identifier: CRD42024576543.