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

ABSTRACT:

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

SUBMITTER: Brasher M 

PROVIDER: S-EPMC12055302 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature