Among 26 included studies, 28 models exhibited good predictive performance for surgical site infection with AUC values above 0.7, but all studies had high risks of bias.
Systematic Review (n=315,858)
Yes
While risk prediction models for surgical site infection after abdominal surgery show good discriminatory ability, their clinical utility is currently limited by high risk of bias, lack of external validation, and methodological shortcomings.
Objective To systematically review risk prediction models for surgical site infection (SSI) after abdominal surgery and to provide a reference for clinical risk management. Methods 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. Results 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. Conclusion 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. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD42024576543 , Identifier: CRD42024576543.
Xu et al. (Tue,) conducted a systematic review in surgical site infection after abdominal surgery (n=315,858). risk prediction models for surgical site infection vs. none was evaluated on occurrence of surgical site infection (SSI). Among 26 included studies, 28 models exhibited good predictive performance for surgical site infection with AUC values above 0.7, but all studies had high risks of bias.