Machine learning for prediction of bronchopulmonary dysplasia-free survival among very preterm infants
Rebekah Leigh, Andrew Pham, Srinandini S. Rao, Farha Vora, Gina Hou, Chelsea Kent, Abigail Rodriguez, Arvind Narang, John Tan, Fu‐Sheng Chou
Abstract
BACKGROUND: Bronchopulmonary dysplasia (BPD) is one of the most common and serious sequelae of prematurity. Prompt diagnosis using prediction tools is crucial for early intervention and prevention of further adverse effects. This study aims to develop a BPD-free survival prediction tool based on the concept of the developmental origin of BPD with machine learning. METHODS: Datasets comprising perinatal factors and early postnatal respiratory support were used for initial model development, followed by combining the two models into a final ensemble model using logistic regression. Simulation of clinical scenarios was performed. RESULTS: Data from 689 infants were included in the study. We randomly selected data from 80% of infants for model development and used the remaining 20% for validation. The performance of the final model was assessed by receiver operating characteristics which showed 0.921 (95% CI: 0.899-0.943) and 0.899 (95% CI: 0.848-0.949) for the training and the validation datasets, respectively. Simulation data suggests that extubating to CPAP is superior to NIPPV in BPD-free survival. Additionally, successful extubation may be defined as no reintubation for 9 days following initial extubation. CONCLUSIONS: Machine learning-based BPD prediction based on perinatal features and respiratory data may have clinical applicability to promote early targeted intervention in high-risk infants.