Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Pieszko, Konrad; Shanbhag, Aakash D; Singh, Ananya; Hauser, M Timothy; Miller, Robert J H; Liang, Joanna X; Motwani, Manish; Kwieciński, Jacek; Sharir, Tali; Einstein, Andrew J; Fish, Mathews B; Ruddy, Terrence D; Kaufmann, Philipp A; Sinusas, Albert J; Miller, Edward J; Bateman, Timothy M; Dorbala, Sharmila; Di Carli, Marcelo; Berman, Daniel S; Dey, Damini

doi:10.5167/uzh-234632

Citations

Citation copied

Pieszko, K., Shanbhag, A. D., Singh, A., Hauser, M. T., Miller, R. J. H., Liang, J. X., Motwani, M., Kwieciński, J., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., Di Carli, M., Berman, D. S., … Slomka, P. J. (2023). Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging. Npj Digital Medicine, 6(1), 78. https://doi.org/10.1038/s41746-023-00806-x

Abstract

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross