Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study

Abstract

ObjectiveTo predict extubation readiness in preterm infants using machine learning analysis of bedside pulse oximeter and ventilator data. Study designThis is an observational study with prospective recordings of oxygen saturation (SpO2) and ventilator data from infants < 30 weeks’ gestation age (GA). Research pulse oximeters collected SpO2 (1 Hz sampling rate) to quantify intermittent hypoxemia (IH). Continuous ventilator metrics were collected (4-5 min sampling) from bedside ventilators. Data modeling was completed using unbiased machine learning algorithms. Three model sets were created using the following data source combinations: [1] IH and ventilator (IH + SIMV), [2] IH, and [3] ventilator (SIMV). Infants were also analyzed separated by postnatal age (infants < 2 or ≥ 2 weeks of age). Models were compared by area under the ROC curve (AUC). ResultsA total of 110 extubation events from 110 preterm infants were analyzed. Infants had a median GA and birth weight of 26 weeks and 825 grams, respectively. Of the three models presented, the IH + SIMV model achieved the highest AUC of 0.77 for all infants. Separating infants by postnatal age increased accuracy further achieving AUC of 0.94 for < 2 weeks of age group and AUC of 0.83 for ≥ 2 weeks group. ConclusionsMachine learning analysis has the potential to enhance prediction accuracy of extubation readiness in preterm infants while utilizing readily available data streams from bedside pulse oximeters and ventilators.