ArticleJournal of the American College of Surgeons2026
Development, Validation, and Comparison of Machine Learning Models for Predicting Pediatric Surgical Site Infections Using the NSQIP-P Database.
Article in Journal of the American College of Surgeons, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Predicting pediatric surgical site infections using machine learning: clinical implications and limitations.Annals of medicine and surgery (2012) · 2026Article
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5 authors.
Funding
Abstract
backgroundSurgical site infections (SSIs) cause substantial postoperative morbidity in children. Despite being largely preventable, SSI rates have continued to rise. Existing SSI predictive models are predominantly designed for adults. This study aimed to develop, validate, and compare machine learning models for predicting pediatric SSI risk and recommend a model for clinical workflow integration. STUDY
designWe conducted a retrospective cohort analysis of 1,152,034 cases from the NSQIP-Pediatrics (NSQIP-P) database (2012 to 2022), including 27,562 SSIs. Data were split into training (1,101,950) and test (50,084) sets. We developed 5 models: elastic net-regularized logistic regression, random forest, gradient boosted trees, k-nearest neighbors, and neural networks. Performance was evaluated using Brier scores, c-statistics, and calibration metrics, with bootstrap resampling to estimate confidence intervals.
resultsAll models performed similarly (Brier scores 0.023 to 0.024; c-statistics 0.72 to 0.77). Regularized logistic regression showed strong performance (Brier score 0.023; c-statistic 0.77) and was selected for its balance of predictive accuracy, computational efficiency, and feasibility for clinical use. Key predictors included procedural codes, perioperative diagnoses, comorbidities, acuity markers, labs, and patient demographics. In the test set, mean predicted SSI risk was 2.4%; 62% of patients had risk <2%, whereas 3% had risk ≥10%.
conclusionsMachine learning models can predict pediatric SSI risk using preoperative data. Our regularized logistic regression model is a promising candidate for integration into electronic health record systems to enable individualized risk estimation and targeted infection prevention. Next steps include external validation and user-centered implementation studies.
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