E43: Development, Validation, and Comparison of Machine Learning Models for Predicting Pediatric Surgical Site Infection Using the NSQIP-P Database

In this episode, Thomas K Varghese, Jr, MD, FACS, is joined by Carrie Chan, MSN, MPH, from the University of California, San Francisco, and Karthik Balakrishnan, MD, FACS, from Stanford Medicine Children’s Health. They discuss their recent article,“Development, Validation, and Comparison of Machine Learning Models for Predicting Pediatric Surgical Site Infections Using the NSQIP-P Database,” which represents the largest study to date on predicting pediatric surgical site infection. The authors developed machine-learning models and ultimately recommend a regularized logistic regression model for clinical integration, balancing performance and feasibility for implementation. Findings support using routine preoperative data for personalized infection prevention and preoperative planning.

Disclosure Information: Ms Chan and Drs Varghese and Balakrishnan have nothing to disclose.

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Chan, Carrie T MSN, MPH; Pletcher, Mark J MD, MPH; Balakrishnan, Karthik MD, MPH, FACS; Hswen, Yulin ScD, MPH; Scheffler, Aaron PhD, MS. Development, Validation, and Comparison of Machine Learning Models for Predicting Pediatric Surgical Site Infections Using the NSQIP-P Database. Journal of the American College of Surgeons 242(3):p 712-722, March 2026. | DOI: 10.1097/XCS.0000000000001683

Learn more about the Journal of the American College of Surgeons, a monthly peer-reviewed journal publishing original contributions on all aspects of surgery, including scientific articles, collective reviews, experimental investigations, and more.

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