Predicting pulmonary hypertension in infants with bronchopulmonary dysplasia.

OBJECTIVE: Develop and validate predictive models for pulmonary hypertension (PH) in high-risk infants. STUDY DESIGN: We trained logistic regression (LR) and long short-term memory (LSTM) models using a multicenter cohort study of infants 22-28 weeks gestational age discharged from neonatal intensive care units from 2008 to 2020, at two timepoints: 33 weeks post-menstrual age (PMA) for infants receiving mechanical ventilation at that time, and 36 weeks PMA for infants receiving any respiratory support at that time. RESULTS: At 33 weeks PMA (N = 2849), top LR model predictors were current fraction of inspired oxygen and birth weight. At 36 weeks (N = 20,173), top LR model predictors were current respiratory support and birth weight. Both LR and LSTM models had strong performance in the temporal validation cohort (infants discharged 2021-2022) for both timepoints. CONCLUSION: Using available clinical variables, we developed and validated predictive models that may identify infants most at risk for PH at two timepoints.

Duke Scholars

Author Christoph Paul Vincent Hornik Pediatrics, Critical Care Medicine

Author Rachel Gottron Greenberg Pediatrics, Neonatology

Author Kevin Dennis Hill Pediatrics, Cardiology

Author Rishi Kamaleswaran Trauma, Acute, and Critical Care Surgery

Author Kanecia Obie Zimmerman Pediatrics, Critical Care Medicine

Author Benjamin Alan Goldstein Biostatistics & Bioinformatics, Division of Translational Bi ...