Interpretable Machine Learning-Based Predictive Modeling of Patient Outcomes Following Cardiac Surgery

Objective The clinical applicability of machine learning predictions of patient outcomes following cardiac surgery remains unclear. We applied machine learning to predict patient outcomes associated with high morbidity and mortality after cardiac surgery and identified the importance of variables to the derived model’s performance. Methods We applied machine learning to the Society of Thoracic Surgeons Adult Cardiac Surgery Database to predict post-operative hemorrhage requiring re-operation, venous thromboembolism and stroke. We used permutation feature importance to identify variables important to model performance and a misclassification analysis to study the limitations of the model. Results The study dataset included 662,772 subjects who had cardiac surgery between 2015 and 2017 and 240 variables. Hemorrhage requiring re-operation, venous thromboembolism and stroke occurred in 2.9%, 1.2% and 2.0% of subjects respectively. The model performed remarkably well at predicting all three complications (AUC 0.92-0.97). Pre- and intra-operative variables were not important to model performance. Instead, performance for the prediction of all three outcomes was driven primarily by several post-operative variables, including known risk factors for the complications such as mechanical ventilation and new-onset of post-operative arrhythmias. Many of the post-operative variables important to model performance also increased the risk of subject misclassification, indicating internal validity. Conclusions A machine learning model accurately and reliably predicts patient outcomes following cardiac surgery. Post-operative, as opposed to pre- or intra-operative variables, are important to model performance. Interventions targeting this period including minimizing the duration of mechanical ventilation and early treatment of new-onset post-operative arrhythmias may help lower the risk of these complications.