Machine learning risk stratification for high-risk infant follow-up of term and late preterm infants. Pediatr Res 2024 Jun 26
Date
06/27/2024Pubmed ID
38926547Pubmed Central ID
PMC11669732DOI
10.1038/s41390-024-03338-6Scopus ID
2-s2.0-85197295581 (requires institutional sign-in at Scopus site) 1 CitationAbstract
BACKGROUND: Term and late preterm infants are not routinely referred to high-risk infant follow-up programs at neonatal intensive care unit (NICU) discharge. We aimed to identify NICU factors associated with abnormal developmental screening and develop a risk-stratification model using machine learning for high-risk infant follow-up enrollment.
METHODS: We performed a retrospective cohort study identifying abnormal developmental screening prior to 6 years of age in infants born ≥34 weeks gestation admitted to a level IV NICU. Five machine learning models using NICU predictors were developed by classification and regression tree (CART), random forest, gradient boosting TreeNet, multivariate adaptive regression splines (MARS), and regularized logistic regression analysis. Performance metrics included sensitivity, specificity, accuracy, precision, and area under the receiver operating curve (AUC).
RESULTS: Within this cohort, 87% (1183/1355) received developmental screening, and 47% had abnormal results. Common NICU predictors across all models were oral (PO) feeding, follow-up appointments, and medications prescribed at NICU discharge. Each model resulted in an AUC > 0.7, specificity >70%, and sensitivity >60%.
CONCLUSION: Stratification of developmental risk in term and late preterm infants is possible utilizing machine learning. Applying machine learning algorithms allows for targeted expansion of high-risk infant follow-up criteria.
IMPACT: This study addresses the gap in knowledge of developmental outcomes of infants ≥34 weeks gestation requiring neonatal intensive care. Machine learning methodology can be used to stratify early childhood developmental risk for these term and late preterm infants. Applying the classification and regression tree (CART) algorithm described in the study allows for targeted expansion of high-risk infant follow-up enrollment to include those term and late preterm infants who may benefit most.
Author List
Carlton K, Zhang J, Cabacungan E, Herrera S, Koop J, Yan K, Cohen SAuthors
Erwin Cabacungan MD Associate Professor in the Pediatrics department at Medical College of WisconsinSusan Cohen MD Professor in the Pediatrics department at Medical College of Wisconsin
Jennifer I. Koop Olsta PhD Professor in the Neurology department at Medical College of Wisconsin
Ke Yan PhD Associate Professor in the Pediatrics department at Medical College of Wisconsin
