Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement: The role of machine learning
Pradyumna Agasthi, Hasan Ashraf, Sai Harika Pujari, Marlene Girardo, Andrew Tseng, Farouk Mookadam, Nithin R. Venepally, Matthew Buras, Bishoy Abraham, Banveet Kaur Khetarpal, Mohamed Allam, Siva K. Mulpuru, Mackram F. Eleid, Kevin L. Greason, Nirat Beohar, John Sweeney, David Fortuin, David R. Holmes, Reza Arsanjani
Abstract
BACKGROUND: Atrioventricular block requiring permanent pacemaker (PPM) implantation is an important complication of transcatheter aortic valve replacement (TAVR). Application of machine learning could potentially be used to predict pre-procedural risk for PPM. AIM: To apply machine learning to be used to predict pre-procedural risk for PPM. METHODS: A retrospective study of 1200 patients who underwent TAVR (January 2014-December 2017) was performed. 964 patients without prior PPM were included for a 30-d analysis and 657 patients without PPM requirement through 30 d were included for a 1-year analysis. After the exclusion of variables with near-zero variance or ≥ 50% missing data, 167 variables were included in the random forest gradient boosting algorithm (GBM) optimized using 5-fold cross-validations repeated 10 times. The receiver operator curve (ROC) for the GBM model and PPM risk score models were calculated to predict the risk of PPM at 30 d and 1 year. RESULTS: value < 0.001. CONCLUSION: The GBM model has good discrimination and calibration in identifying patients at high risk of PPM post-TAVR.