RESUMEN
BACKGROUND: Current Society of Thoracic Surgeons (STS) risk models for predicting outcomes of mitral valve surgery (MVS) assume a linear and cumulative impact of variables. We evaluated postoperative MVS outcomes and designed mortality and morbidity risk calculators to supplement the STS risk score. METHODS: Data from the STS Adult Cardiac Surgery Database for MVS was used from 2008 to 2017. The data included 383,550 procedures and 89 variables. Machine learning (ML) algorithms were employed to train models to predict postoperative outcomes for MVS patients. Each model's discrimination and calibration performance were validated using unseen data against the STS risk score. RESULTS: Comprehensive mortality and morbidity risk assessment scores were derived from a training set of 287,662 observations. The area under the curve (AUC) for mortality ranged from 0.77 to 0.83, leading to a 3% increase in predictive accuracy compared to the STS score. Logistic Regression and eXtreme Gradient Boosting achieved the highest AUC for prolonged ventilation (0.82) and deep sternal wound infection (0.78 and 0.77) respectively. EXtreme Gradient Boosting performed the best with an AUC of 0.815 for renal failure. For permanent stroke prediction all models performed similarly with an AUC around 0.67. The ML models led to improved calibration performance for mortality, prolonged ventilation, and renal failure, especially in cases of reconstruction/repair and replacement surgery. CONCLUSIONS: The proposed risk models complement existing STS models in predicting mortality, prolonged ventilation, and renal failure, allowing healthcare providers to more accurately assess a patient's risk of morbidity and mortality when undergoing MVS.
