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

Mandy Brasher, Alexandr Virodov, Thomas M. Raffay, Henrietta S. Bada, M. Douglas Cunningham, Cody Bumgardner, Elie G. Abu Jawdeh

Research output: Contribution to journalArticlepeer-review


Objective: The objective of this study was 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 of gestation age. Research pulse oximeters collected SpO2 (1 Hz sampling rate) to quantify intermittent hypoxemia (IH). Continuous ventilator metrics were collected (4-5-minute 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 receiver operating characteristic curve (AUC). Results: A total of 110 extubation events from 110 preterm infants were analyzed. Infants had a median gestation age and birth weight of 26 weeks and 825 g, respectively. Of the 3 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.

Original languageEnglish
Article number114043
JournalJournal of Pediatrics
StatePublished - Aug 2024

Bibliographical note

Publisher Copyright:
© 2024


  • bedside monitoring
  • extubation attempt
  • extubation failure
  • extubation success
  • mechanical ventilation
  • neonatal intensive care
  • prediction tool
  • preterm infants

ASJC Scopus subject areas

  • Pediatrics, Perinatology, and Child Health


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