Development and Validation of a Risk Prediction Model for Surgical Site Infections Following Appendectomy: A Retrospective Cohort Study

Surgical site infections (SSIs) are a common and serious complication following appendectomy, yet practical tools for individualized risk stratification are lacking. In resource-limited settings, where SSI rates are often high, the absence of reliable prediction models hinders effective prevention strategies and antimicrobial stewardship. Our goal was to develop and validate an evidence-based risk scoring system to help clinicians identify patients at increased risk of SSI using routine clinical parameters.
Methods: We conducted a retrospective cohort study of 245 consecutive appendectomy cases at a tertiary referral center between March 2024 and April 2025. CDC-defined SSIs were identified through 30-day postoperative surveillance. We used multivariable logistic regression to assess demographic, clinical, and operative predictors of SSI development. Model discrimination was evaluated with receiver operating characteristic (ROC) curve analysis, while calibration was tested using the Hosmer-Lemeshow goodness-of-fit test. The scoring system was derived from standardized β-coefficients, and internal validation was conducted with 1,000 bootstrap iterations to ensure robustness.