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

TLDR

QTc prolongation, whether drug‑induced, genetic, or disease‑related such as COVID‑19, predisposes to ventricular arrhythmias and sudden death, yet its assessment relies mainly on 12‑lead electrocardiography. The study aimed to train and validate an AI‑enabled 12‑lead ECG algorithm to predict QTc and then prospectively evaluate it on mobile ECG tracings in a cohort enriched for repolarization abnormalities. A deep neural network was trained on over 1.6 million 12‑lead ECGs from 538 k patients, validated against cardiologist‑over‑read QTc values, and then tested prospectively on 686 patients with genetic heart disease using a prototype mECG device comparable to the AliveCor KardiaMobile 6L. In both retrospective validation and prospective testing, the AI‑derived DNN produced QTc estimates within ±25 ms of expert over‑reads and achieved an AUC of 0.97, 80 % sensitivity, and 94 % specificity for detecting QTc ≥ 500 ms on mobile ECG tracings, demonstrating accurate, cost‑effective screening for long QT syndrome.

Abstract

Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2-mediated coronavirus disease 2019 (COVID-19), 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 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.Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the "gold standard". The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L.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, genetic heart disease-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±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.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 long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.