Year: 2022
Type: article
Study design: Observational Study
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.
Source: PubMed Central
Authors: Kaitlin C Williamson, Daniel Fabbri
Institution: Vanderbilt University Medical Center
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4367669396