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Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.
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.
  • Giudicessi JR; Clinician-Investigator Training Program (J.R.G.), Mayo Clinic, Rochester, MN.
  • Schram M; AliveCor Inc., Mountain View, CA. (M.S., C.D.G., J.B.S., D.E.A.).
  • Bos JM; 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.
  • Galloway CD; AliveCor Inc., Mountain View, CA. (M.S., C.D.G., J.B.S., D.E.A.).
  • Shreibati JB; AliveCor Inc., Mountain View, CA. (M.S., C.D.G., J.B.S., D.E.A.).
  • Johnson PW; Department of Health Sciences Research (Biomedical Statistics and Informatics), Mayo Clinic, Jacksonville, FL (P.W.J., R.E.C.).
  • Carter RE; Department of Health Sciences Research (Biomedical Statistics and Informatics), Mayo Clinic, Jacksonville, FL (P.W.J., R.E.C.).
  • Disrud LW; 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.
  • Kleiman R; eResearch Technology Inc, Philadelphia, PA (R.K.).
  • Attia ZI; 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.
  • Noseworthy PA; 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.
  • Friedman PA; 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.
  • Albert DE; AliveCor Inc., Mountain View, CA. (M.S., C.D.G., J.B.S., D.E.A.).
  • Ackerman MJ; 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.
Circulation ; 143(13): 1274-1286, 2021 03 30.
Article in English | MEDLINE | ID: covidwho-1058120
Semantic information from SemMedBD (by NLM)
1. 2019 novel coronavirus INTERACTS_WITH COVID-19
Subject
2019 novel coronavirus
Predicate
INTERACTS_WITH
Object
COVID-19
2. Systemic disease PREDISPOSES Sudden Cardiac Death
Subject
Systemic disease
Predicate
PREDISPOSES
Object
Sudden Cardiac Death
3. Systemic disease PREDISPOSES Ventricular arrhythmia
Subject
Systemic disease
Predicate
PREDISPOSES
Object
Ventricular arrhythmia
4. Hereditary Diseases PROCESS_OF Patients
Subject
Hereditary Diseases
Predicate
PROCESS_OF
Object
Patients
5. Patients LOCATION_OF MTR wt Allele
Subject
Patients
Predicate
LOCATION_OF
Object
MTR wt Allele
6. 2019 novel coronavirus INTERACTS_WITH COVID-19
Subject
2019 novel coronavirus
Predicate
INTERACTS_WITH
Object
COVID-19
7. Systemic disease PREDISPOSES Sudden Cardiac Death
Subject
Systemic disease
Predicate
PREDISPOSES
Object
Sudden Cardiac Death
8. Systemic disease PREDISPOSES Ventricular arrhythmia
Subject
Systemic disease
Predicate
PREDISPOSES
Object
Ventricular arrhythmia
9. Hereditary Diseases PROCESS_OF Patients
Subject
Hereditary Diseases
Predicate
PROCESS_OF
Object
Patients
10. Patients LOCATION_OF MTR wt Allele
Subject
Patients
Predicate
LOCATION_OF
Object
MTR wt Allele
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.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Electrocardiography / Heart Diseases / Heart Rate Type of study: Diagnostic study / Observational study / Prognostic study / Risk factors Topics: Long Covid Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: Circulation Year: 2021 Document Type: Article Affiliation country: CIRCULATIONAHA.120.050231

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Electrocardiography / Heart Diseases / Heart Rate Type of study: Diagnostic study / Observational study / Prognostic study / Risk factors Topics: Long Covid Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: Circulation Year: 2021 Document Type: Article Affiliation country: CIRCULATIONAHA.120.050231