J Pediatr. 2024 Mar 30:114043. doi: 10.1016/j.jpeds.2024.114043. Online ahead of print.
ABSTRACT
OBJECTIVE: To predict extubation readiness in preterm infants using machine learning analysis of bedside pulse oximeter and ventilator data.
STUDY DESIGN: This 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).
RESULTS: A 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.
CONCLUSIONS: Machine 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.
PMID:38561049 | DOI:10.1016/j.jpeds.2024.114043
