Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality
Abstract
Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59+- 19 years, 55 percent women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57-13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for post-operative mortality for RCRI greater than 2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77-0.92), non-cardiac surgery with an AUC of 0.83 (0.79-0.88), and catherization or endoscopy suite procedures with an AUC of 0.76 (0.72-0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to pre-operative ECGs, can improve discrimination of post-operative mortality.
Cite
@article{arxiv.2205.03242,
title = {Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality},
author = {David Ouyang and John Theurer and Nathan R. Stein and J. Weston Hughes and Pierre Elias and Bryan He and Neal Yuan and Grant Duffy and Roopinder K. Sandhu and Joseph Ebinger and Patrick Botting and Melvin Jujjavarapu and Brian Claggett and James E. Tooley and Tim Poterucha and Jonathan H. Chen and Michael Nurok and Marco Perez and Adler Perotte and James Y. Zou and Nancy R. Cook and Sumeet S. Chugh and Susan Cheng and Christine M. Albert},
journal= {arXiv preprint arXiv:2205.03242},
year = {2022}
}