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

• Published in: International journal of medical informatics (Shannon, Ireland) Ireland), 2023-10, Vol.178, p.105192-105192, Article 105192
• Main Authors: Song, Wongeun, Hwa Jung, Young, Cho, Jihoon, Baek, Hyunyoung, Won Choi, Chang, Yoo, Sooyoung
• Format: Article
• Language: English
• Subjects: Extubation readiness; Prediction model; Preterm infants
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/j.ijmedinf.2023.105192

[Display omitted] •Given the lack of consensus on extubation readiness in preterm infants, we developed the NExt-Predictor, a model that uses robust predictors for evaluation.•Our prediction model incorporates time series features such as periodic vital signs, ventilator settings, and respiratory indices, enhancing its predictive capabilities.•NExt-Predictor's high precision and area under the receiver operating characteristic curve in both internal and external validations indicate its potential as a tool for extubation decisions. 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 (