Added value of a resting ECG neural network that predicts cardiovascular mortality

The resting 12-lead electrocardiogram (ECG) remains the most commonly used test in evaluating patients with suspected cardiovascular disease. Prognostic values of individual findings on the ECG have been reported but may be of limited use.

The characteristics of 45,855 ECGs ordered by physician's discretion were first recorded and analyzed using a computerized system. Ninety percent of these ECGs were used to train an artifical neural network (ANN) to predict cardiovascular mortality (CVM) based on 132 ECG and four demographic characteristics. The ANN generated a Resting ECG Neural Network (RENN) score that was then tested in the remaining ECGs. The RENN score was finally assessed in a cohort of 2189 patients who underwent exercise treadmill testing and were followed for CVM.

The RENN score was able to better predict CVM compared to individual ECG markers or a traditional Cox regression model in the testing cohort. Over a mean of 8.6 years, there were 156 cardiovascular deaths in the treadmill cohort. Among the patients who were classified as intermediate risk by Duke Treadmill Scoring (DTS), the third tertile of the RENN score demonstrated an adjusted Cox hazard ratio of 5.4 (95% CI 2.0-15.2) compared to the first RENN tertile. The 10-year CVM was 2.8%, 8.6% and 22% in the first, second and third RENN tertiles, respectively.

An ANN that uses the resting ECG and demographic variables to predict CVM was created. The RENN score can further risk stratify patients deemed at moderate risk on exercise treadmill testing.