Prediction of extubation failure in the paediatric cardiac ICU using machine learning and high-frequency physiologic data

• Published in: Cardiology in the young Vol. 32; no. 10; pp. 1649 - 1656
• Main Authors: Rooney, Sydney R, Reynolds, Evan L, Banerjee, Mousumi, Pasquali, Sara K, Charpie, John R, Gaies, Michael G, Owens, Gabe E
• Format: Journal Article
• Language: English
• Published: England 01.10.2022
• Subjects: Airway Extubation; Child; Humans; Intensive Care Units, Pediatric; Machine Learning; Retrospective Studies; Ventilator Weaning - methods; artificial respiration; clinical decision-making; machine learning; quality of healthcare; Airway extubation
• ISSN: 1047-9511; 1467-1107
• DOI: 10.1017/S1047951121004959

Cardiac intensivists frequently assess patient readiness to wean off mechanical ventilation with an extubation readiness trial despite it being no more effective than clinician judgement alone. We evaluated the utility of high-frequency physiologic data and machine learning for improving the prediction of extubation failure in children with cardiovascular disease. This was a retrospective analysis of clinical registry data and streamed physiologic extubation readiness trial data from one paediatric cardiac ICU (12/2016-3/2018). We analysed patients' final extubation readiness trial. Machine learning methods (classification and regression tree, Boosting, Random Forest) were performed using clinical/demographic data, physiologic data, and both datasets. Extubation failure was defined as reintubation within 48 hrs. Classifier performance was assessed on prediction accuracy and area under the receiver operating characteristic curve. Of 178 episodes, 11.2% (N = 20) failed extubation. Using clinical/demographic data, our machine learning methods identified variables such as age, weight, height, and ventilation duration as being important in predicting extubation failure. Best classifier performance with this data was Boosting (prediction accuracy: 0.88; area under the receiver operating characteristic curve: 0.74). Using physiologic data, our machine learning methods found oxygen saturation extremes and descriptors of dynamic compliance, central venous pressure, and heart/respiratory rate to be of importance. The best classifier in this setting was Random Forest (prediction accuracy: 0.89; area under the receiver operating characteristic curve: 0.75). Combining both datasets produced classifiers highlighting the importance of physiologic variables in determining extubation failure, though predictive performance was not improved. Physiologic variables not routinely scrutinised during extubation readiness trials were identified as potential extubation failure predictors. Larger analyses are necessary to investigate whether these markers can improve clinical decision-making.