Development and validation of a prediction model for evaluating extubation readiness in preterm infants

Abstract

Successful early extubation has advantages not only in terms of short-term respiratory morbidities and survival but also in terms of long-term neurodevelopmental outcomes in preterm infants. However, no consensus exists regarding the optimal protocol or guidelines for extubation readiness in preterm infants. Therefore, the decision to extubate preterm infants was almost entirely at the attending physician's discretion. We identified robust and quantitative predictors of success or failure of the first planned extubation attempt before 36 weeks of post-menstrual age in preterm infants (<32 weeks gestational age) and developed a prediction model for evaluating extubation readiness using these predictors. Extubation success was defined as the absence of reintubation within 72 h after extubation. This observational cohort study used data from preterm infants admitted to the neonatal intensive care unit of Seoul National University Bundang Hospital in South Korea between July 2003 and June 2019 to identify predictors and develop and test a predictive model for extubation readiness. Data from preterm infants included in the Medical Informative Medicine for Intensive Care (MIMIC-III) database between 2001 and 2008 were used for external validation. From a machine learning model using predictors such as demographics, periodic vital signs, ventilator settings, and respiratory indices, the area under the receiver operating characteristic curve and average precision of our model were 0.805 (95% confidence interval [CI], 0.802–0.809) and 0.917, respectively in the internal validation and 0.715 (95% CI, 0.713–0.717) and 0.838, respectively in the external validation. Our prediction model (NExt-Predictor) demonstrated high performance in assessing extubation readiness in both internal and external validations.