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Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart

Medicine and Health

Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart

D. M. Popescu, J. K. Shade, et al.

This groundbreaking research, conducted by an expert team including Dan M. Popescu and Julie K. Shade from Johns Hopkins University, introduces a novel deep learning approach to predict patient-specific survival curves for ischemic heart disease. Utilizing advanced imaging and clinical data, this study demonstrates impressive accuracy in forecasting survival, potentially revolutionizing decision-making in arrhythmic death probabilities.

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Playback language: English
Abstract
This study developed a novel deep learning (DL) approach combining neural networks and survival analysis to predict patient-specific survival curves for patients with ischemic heart disease, using contrast-enhanced cardiac magnetic resonance images and clinical covariates. The DL-predicted survival curves accurately predicted survival up to 10 years, including uncertainty estimates. The model achieved concordance indexes of 0.83 and 0.74 and 10-year integrated Brier scores of 0.12 and 0.14 in internal validation and independent test sets, respectively, outperforming standard survival models using only clinical covariates. This technology could improve clinical decision-making by providing accurate, generalizable predictions of arrhythmic death probabilities.
Publisher
Nature Cardiovascular Research
Published On
Apr 07, 2022
Authors
Dan M. Popescu, Julie K. Shade, Changxin Lai, Konstantinos N. Aronis, David Ouyang, M. Vinayaga Moorthy, Nancy R. Cook, Daniel C. Lee, Alan Kadish, Christine M. Albert, Katherine C. Wu, Mauro Maggioni, Natalia A. Trayanova
Tags
deep learning
survival analysis
ischemic heart disease
cardiac imaging
prediction models
clinical decision-making

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