Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Circulation ; h5-index 165.0

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.

Giudicessi John R, Schram Matthew, Bos J Martijn, Galloway Connor D, Shreibati Jacqueline B, Johnson Patrick W, Carter Rickey E, Disrud Levi W, Kleiman Robert, Attia Zachi I, Noseworthy Peter A, Friedman Paul A, Albert David E, Ackerman Michael J