Article ; Online: Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.
2021 Volume 143, Issue 13, Page(s) 1274–1286
Abstract: Background: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs ... and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device ... with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device ...
| 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. |
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| MeSH term(s) | 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 | |||||
| Language | English | |||||
| Publishing date | 2021-02-01 | |||||
| Publishing country | United States | |||||
| Document type | Journal Article ; Research Support, Non-U.S. Gov't | |||||
| ZDB-ID | 80099-5 | |||||
| ISSN | 1524-4539 ; 0009-7322 ; 0069-4193 ; 0065-8499 | |||||
| ISSN (online) | 1524-4539 | |||||
| ISSN | 0009-7322 ; 0069-4193 ; 0065-8499 | |||||
| DOI | 10.1161/CIRCULATIONAHA.120.050231 | |||||
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| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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