Machine learning time-to-event algorithms for predicting the duration of ventilation after cardiac surgery.

Objective: Prolonged mechanical ventilation after cardiac surgery contributes significantly to morbidity, mortality, and excessive hospital resource use. Accurate prediction of prolonged mechanical ventilation duration can improve decision-making and patient outcomes. We aimed to develop and validate time-to-event models to predict the duration of ventilation and prolonged mechanical ventilation. Methods: From the Medical Information Mart for Intensive Care III and IV databases, we extracted postoperative data from all cardiac surgery patients. We benchmarked 3 machine learning time-to-event algorithms (random survival forest, gradient boosted survival model, and survival support vector machine) against traditional Elastic-Net Cox regression. We evaluated model performance using weighted mean area under the curve ( Results: Models were trained on data from 10,430 cardiac surgery patients ventilated for a median of 7.0 hours (interquartile range, 4.4-16.0). Random survival forest had the highest Conclusions: Machine learning time-to-event models for prolonged mechanical ventilation and the duration of ventilation, particularly random survival forest and gradient boosted survival model, have significantly improved performance compared with current state-of-the-art tools and may be valuable decision supports in the postoperative management of cardiac surgery patients.