Abstract
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 ( AUC¯wC,D ), cumulative/dynamic area under the receiver operating characteristic curve (AUC(C,D)(t)), Concordance Index, and integrated Brier score. Permutation feature importance was reported for the best models. We conducted a sensitivity analysis to evaluate model fairness across different races and sexes. 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 AUC¯wC,D (0.834, 95% CI, 0.832-0.836) and integrated Brier score (0.041), whereas gradient boosted survival model had the highest Concordance Index (0.721, 95% CI, 0.717-0.724). All machine learning models significantly outperformed Elastic-Net Cox Regression. Ventilatory settings, laboratory results, and Sequential Organ Failure Assessment score within 4 hours of intubation were identified as the most important features. Sensitivity analysis showed equal or improved performance for minority female and non-White cohorts. 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.