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Systematic review of risk prediction models for surgical site infection after abdominal surgery in adults
Yating Xu1, Juecen Liu1, Yao Chen1
1School of Nursing, Southwest Medical University, Luzhou, Sichuan, China.
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.
