Prediction of extubation failure among low birthweight neonates using machine learning

Abstract

Objective: To develop machine learning models predicting extubation failure in low birthweight neonates using large amounts of clinical data.

Study design: Retrospective cohort study using MIMIC-III, a large single-center, open-source clinical dataset. Logistic regression and boosted-tree (XGBoost) models using demographics, medications, and vital sign and ventilatory data were developed to predict extubation failure, defined as reintubation within 7 days.

Results: 1348 low birthweight (≤2500 g) neonates who received mechanical ventilation within the first 7 days were included, of which 350 (26%) failed a trial of extubation. The best-performing model was a boosted-tree model incorporating demographics, vital signs, ventilator parameters, and medications (AUROC 0.82). The most important features were birthweight, last FiO₂, average mean airway pressure, caffeine use, and gestational age.

Conclusions: Machine learning models identified low birthweight ventilated neonates at risk for extubation failure. These models will need to be validated across multiple centers to determine generalizability of this tool.