Classifying Infection Risk Following Pediatric Cardiac Surgery.

ABSTRACT

Postoperative infections frequently complicate pediatric cardiac surgery, increasing morbidity and cost. If high risk patients could be identified early, preventive measures could mitigate infection risk. In this study, we used structured health data to generate a cohort of pediatric cardiac surgery cases from a single center and used billing codes to assign outcomes for postoperative sepsis, bacteremia, necrotizing enterocolitis, and a composite outcome. We subsequently validated these outcomes manually using clinical notes and culture data. Using this cohort of 2080 surgeries, we trained models to classify the risk of postoperative infections using logistic regression and several machine learning methods. We compared the performance of the models trained on the validated outcomes to those trained on unvalidated outcomes. Manual validation revealed low accuracy of diagnosis codes as classifiers of postoperative infections. Despite significant differences in outcome assignments, similar model performance was achieved using unvalidated and validated outcomes.