Predicting cardiovascular death 1-year after transcatheter aortic valve implantation: an artificial-intelligence based risk model

Abstract Background Prediction of cardiovascular death after transcatheter aortic valve implantation (TAVI) remains difficult. Currently, more lower risk patients are considered for TAVI and predicting not only immediate survival, but more longer-term outcomes seems important. Most developed risk tools use standard statistical approaches and focus mainly on short-term (30 days post-TAVI) outcomes. Purpose The aim of this project was to identify important predictors for worse outcomes after aortic valve replacement using machine learning features. Furthermore, we want to develop and artificial intelligence (AI) -model to predict cardiovascular death 1 year after TAVI. Methods For our analysis we used 725 patients with severe AS that underwent TAVI between 2008 and 2022 and were included in a prospective single centre observational study. Leveraging an automated machine learning framework, we systematically evaluated various machine learning algorithms including Random Forest, AdaBoost, Neural Networks, Randomized Decision Trees and and Gradient Boosting. Results We constructed prediction models utilizing three feature subsets: the top 10 most important features, the top 15 most important features (Figure 1), and all available features (n=66). Our analyses yielded area under curve (AUC) values of 0.71, 0.83, and 0.92, respectively (Figure 2). The best model was obtained by Randomized Decision Trees. NYHA classification, left ventricular ejection fraction, Charlson comorbidity index, posterior wall thickness and myocardial infarction 90-days before TAVI appeared as the most influential predictors for cardiovascular death within one year after TAVI. Conclusions Our model was able to identify the most important features indicating high risk for patients undergoing TAVI. Using the 15-feature model, we were able to accurately predict cardiovascular death 1 year after TAVI. AI-models to predict risk can be used to better inform/select patients undergoing TAVI.