Machine Learning Superior to Logistic Risk Scores for Predicting Mortality Risk After TAVI - The Cardiology Advisor

Machine learning was found to be superior to logistic risk scores in predicting intrahospital all-cause mortality after transcatheter aortic valve implantation (TAVI), according to study results published in Clinical Research in Cardiology. Current strategies for identifying patients eligible for TAVI rely on risk assessment tools such as the Society of Thoracic Surgeon's Risk Score (STS score). The predictive power of these tools is poor, and improved options for risk stratification of TAVI patients are needed. In this retrospective analysis of data from 451 patients, investigators aimed to evaluate whether machine learning models could be used to predict clinical outcomes for patients after TAVI. A total of 83 features, including patient demographics, comorbidities, laboratory data, electro- and echocardiogram findings, and computed tomography (CT) results, were used to train and test the predictive models.