Machine learning for prediction of all-cause mortality after transcatheter aortic valve implantation
Jacek Kwieciński, Maciej Dąbrowski, Luis Nombela‐Franco, Kajetan Grodecki, Konrad Pieszko, Zbigniew Chmielak, Anna Pyłko, Breda Hennessey, Łukasz Kalińczuk, Gabriela Tirado‐Conte, Bartosz Rymuza, Janusz Kochman, Maksymilian P. Opolski, Zenon Huczek, Marc R. Dweck, Damini Dey, Pilar Jiménez‐Quevedo, Piotr J. Slomka, Adam Witkowski
Abstract
AIMS: Prediction of adverse events in mid-term follow-up after transcatheter aortic valve implantation (TAVI) is challenging. We sought to develop and validate a machine learning model for prediction of 1-year all-cause mortality in patients who underwent TAVI and were discharged following the index procedure. METHODS AND RESULTS: The model was developed on data of patients who underwent TAVI at a high-volume centre between January 2013 and March 2019. Machine learning by extreme gradient boosting was trained and tested with repeated 10-fold hold-out testing using 34 pre- and 25 peri-procedural clinical variables. External validation was performed on unseen data from two other independent high-volume TAVI centres. Six hundred four patients (43% men, 81 ± 5 years old, EuroSCORE II 4.8 [3.0-6.3]%) in the derivation and 823 patients (46% men, 82 ± 5 years old, EuroSCORE II 4.7 [2.9-6.0]%) in the validation cohort underwent TAVI and were discharged home following the index procedure. Over the 12 months of follow-up, 68 (11%) and 95 (12%) subjects died in the derivation and validation cohorts, respectively. In external validation, the machine learning model had an area under the receiver-operator curve of 0.82 (0.78-0.87) for prediction of 1-year all-cause mortality following hospital discharge after TAVI, which was superior to pre- and peri-procedural clinical variables including age 0.52 (0.46-0.59) and the EuroSCORE II 0.57 (0.51-0.64), P < 0.001 for a difference. CONCLUSION: Machine learning based on readily available clinical data allows accurate prediction of 1-year all-cause mortality following a successful TAVI.