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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| Vydané v: | Circulation (New York, N.Y.) Ročník 143; číslo 13; s. 1274 |
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| Hlavní autori: | , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
30.03.2021
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| ISSN: | 1524-4539, 1524-4539 |
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| 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. |
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| AbstractList | 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.BACKGROUNDHeart 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.METHODSUsing >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.RESULTSIn 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.CONCLUSIONSUsing 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. 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. |
| Author | Disrud, Levi W Galloway, Conner D Shreibati, Jacqueline B Attia, Zachi I Ackerman, Michael J Giudicessi, John R Friedman, Paul A Noseworthy, Peter A Carter, Rickey E Johnson, Patrick W Kleiman, Robert Albert, David E Schram, Matthew Bos, J Martijn |
| Author_xml | – sequence: 1 givenname: John R surname: Giudicessi fullname: Giudicessi, John R organization: Clinician-Investigator Training Program (J.R.G.), Mayo Clinic, Rochester, MN – sequence: 2 givenname: Matthew surname: Schram fullname: Schram, Matthew organization: AliveCor Inc., Mountain View, CA. (M.S., C.D.G., J.B.S., D.E.A.) – sequence: 3 givenname: J Martijn surname: Bos fullname: Bos, J Martijn organization: Department of Cardiovascular Medicine; Windland Smith Rice Sudden Death Genomics Laboratory, Department of Molecular Pharmacology and Experimental Therapeutics (J.M.B., M.J.A.), Mayo Clinic, Rochester, MN – sequence: 4 givenname: Conner D surname: Galloway fullname: Galloway, Conner D organization: AliveCor Inc., Mountain View, CA. (M.S., C.D.G., J.B.S., D.E.A.) – sequence: 5 givenname: Jacqueline B surname: Shreibati fullname: Shreibati, Jacqueline B organization: AliveCor Inc., Mountain View, CA. (M.S., C.D.G., J.B.S., D.E.A.) – sequence: 6 givenname: Patrick W surname: Johnson fullname: Johnson, Patrick W organization: Department of Health Sciences Research (Biomedical Statistics and Informatics), Mayo Clinic, Jacksonville, FL (P.W.J., R.E.C.) – sequence: 7 givenname: Rickey E surname: Carter fullname: Carter, Rickey E organization: Department of Health Sciences Research (Biomedical Statistics and Informatics), Mayo Clinic, Jacksonville, FL (P.W.J., R.E.C.) – sequence: 8 givenname: Levi W surname: Disrud fullname: Disrud, Levi W organization: Division of Heart Rhythm Services, Windland Smith Rice Genetic Heart Rhythm Clinic (L.W.D., Z.I.A., P.A.N., P.A.F., M.J.A.), Mayo Clinic, Rochester, MN – sequence: 9 givenname: Robert surname: Kleiman fullname: Kleiman, Robert organization: eResearch Technology Inc, Philadelphia, PA (R.K.) – sequence: 10 givenname: Zachi I surname: Attia fullname: Attia, Zachi I organization: Division of Heart Rhythm Services, Windland Smith Rice Genetic Heart Rhythm Clinic (L.W.D., Z.I.A., P.A.N., P.A.F., M.J.A.), Mayo Clinic, Rochester, MN – sequence: 11 givenname: Peter A surname: Noseworthy fullname: Noseworthy, Peter A organization: Division of Heart Rhythm Services, Windland Smith Rice Genetic Heart Rhythm Clinic (L.W.D., Z.I.A., P.A.N., P.A.F., M.J.A.), Mayo Clinic, Rochester, MN – sequence: 12 givenname: Paul A surname: Friedman fullname: Friedman, Paul A organization: Division of Heart Rhythm Services, Windland Smith Rice Genetic Heart Rhythm Clinic (L.W.D., Z.I.A., P.A.N., P.A.F., M.J.A.), Mayo Clinic, Rochester, MN – sequence: 13 givenname: David E surname: Albert fullname: Albert, David E organization: AliveCor Inc., Mountain View, CA. (M.S., C.D.G., J.B.S., D.E.A.) – sequence: 14 givenname: Michael J surname: Ackerman fullname: Ackerman, Michael J organization: Division of Pediatric Cardiology, Department of Pediatric and Adolescent Medicine (M.J.A.), Mayo Clinic, Rochester, MN |
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| References | 33779269 - Circulation. 2021 Mar 30;143(13):1299-1301 |
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| SubjectTerms | 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 |
| Title | Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device |
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