  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.
