Developing an extubation prediction model utilizing continuous physiologic data for neonates <28 weeks gestational age

Abstract Objective To incorporate continuous vital sign and ventilator data to improve neonatal extubation failure prediction in high-risk neonates. Study Design: In a single-center retrospective cohort, neonates < 28 weeks' gestation with an extubation event and physiologic data were included. Extubation failure was defined as re-intubation within 7 days. The cohort was divided 75/25 into train-test sets with K-fold cross-validation, evaluating multiple machine learning algorithms. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) values. Results Of 157 infants, 31 experienced extubation failure (mean time to failure, 58 hours). Using physiologic data and clinician-engineered features, logistic regression performed best (AUC 0.88). Top predictors included post-menstrual age at extubation, median FiO2, median SpO2, maximum heart rate, and minimum SpO2. Conclusion Incorporating physiologic data significantly improved extubation prediction over historical models. We propose optimizing negative predictive value to objectively guide earlier ventilator liberation, with future multicenter studies required to enhance performance and transferability.