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

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,...

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Published in:Circulation (New York, N.Y.) Vol. 143; no. 13; p. 1274
Main 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
Format: Journal Article
Language:English
Published: United States 30.03.2021
Subjects:
Adult
Aged
Area Under Curve
Artificial Intelligence
COVID-19 - physiopathology
COVID-19 - virology
Electrocardiography - instrumentation
Electrocardiography - methods
Female
Heart Diseases - diagnosis
Heart Diseases - physiopathology
Heart Rate - physiology
Humans
Long QT Syndrome - diagnosis
Long QT Syndrome - physiopathology
Male
Middle Aged
Prospective Studies
ROC Curve
SARS-CoV-2 - isolation & purification
Sensitivity and Specificity
Smartphone
long QT syndrome
machine learning
artificial intelligence
electrocardiography
ISSN:1524-4539, 1524-4539
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Summary: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.
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ISSN:1524-4539
1524-4539
DOI:10.1161/CIRCULATIONAHA.120.050231