In Journal of cardiac surgery ; h5-index 21.0
INTRODUCTION : In patients undergoing high-risk cardiac surgery, the uncertainty of outcome may complicate the decision process to intervene. To augment decision-making, a machine learning approach was used to determine weighted personalized factors contributing to mortality.
METHODS : American College of Surgeons National Surgical Quality Improvement Program was queried for cardiac surgery patients with predicted mortality ≥10% between 2012 and 2019. Multiple machine learning models were investigated, with significant predictors ultimately used in gradient boosting machine (GBM) modeling. GBM-trained data were then used for local interpretable model-agnostic explanations (LIME) modeling to provide individual patient-specific mortality prediction.
RESULTS : A total of 194 patient deaths among 1291 high-risk cardiac surgeries were included. GBM performance was superior to other model approaches. The top five factors contributing to mortality in LIME modeling were preoperative dialysis, emergent cases, Hispanic ethnicity, steroid use, and ventilator dependence. LIME results individualized patient factors with model probability and explanation of fit.
CONCLUSIONS : The application of machine learning techniques provides individualized predicted mortality and identifies contributing factors in high-risk cardiac surgery. Employment of this modeling to the Society of Thoracic Surgeons database may provide individualized risk factors contributing to mortality.
Rogers Michael P, Janjua Haroon, Fishberger Gregory, Harish Abhinav, Sujka Joseph, Toloza Eric M, DeSantis Anthony J, Hooker Robert L, Pietrobon Ricardo, Lozonschi Lucian, Kuo Paul C
2022-Nov-08
adult cardiac surgery, machine learning, model interpretability, risk scoring
