Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.

Background: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome (LQTS), and/or systemic diseases including SARS-CoV-2-mediated COVID19, can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)-enabled 12-lead electrocardiogram (ECG) algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. Methods: Using over 1.6 million 12-lead ECGs from 538,200 patients, a deep neural network (DNN) was derived (n = 250,767 patients for training and n = 107,920 patients for testing) and validated (n = 179,513 patients) to predict the QTc using cardiologist over-read QTc values as the gold standard. The ability of this DNN to detect clinically-relevant QTc prolongation (e.g. QTc ≥ 500 ms) was then tested prospectively on 686 genetic heart disease (GHD) patients (50% with LQTS) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. Results: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (-1.76 ± 23.14 ms). Similarly, within the prospective, GHD-enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (-0.45 ± 24.73 ms) and a commercial core ECG laboratory [+10.52 ms ± 25.64 ms] was nominal. When applied to mECG tracings, the DNN's ability to detect a QTc value ≥ 500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. Conclusions: Using smartphone-enabled electrodes, an AI-DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital LQTS in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.