Artificial Intelligence–Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device. Issue 13 (30th March 2021)

• Record Type: Journal Article
• Authors: Giudicessi, John R.; Schram, Matthew; Bos, J. Martijn; Galloway, Conner 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.
• Is Part Of: Circulation. Volume 143:Issue 13(2021)
• Publication Date: 2021-03-30
• Subjects: artificial intelligence -- electrocardiography -- long QT syndrome -- machine learning; Blood -- Circulation -- Periodicals; Cardiovascular system -- Periodicals; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; Blood Circulation; Cardiovascular System; Vascular Diseases; 616.1
• DOI: 10.1161/CIRCULATIONAHA.120.050231
• Languages: English
• ISSNs: 0009-7322

Abstract

Abstract : Background: 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. Methods: 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. 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, 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. 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 long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective. Abstract : Supplemental Digital Content is available in the text.