Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 8 de 8
Filter
1.
Heart Rhythm ; 2021 Dec 31.
Article in English | MEDLINE | ID: covidwho-1587705

ABSTRACT

BACKGROUND: During the early stages of the coronavirus disease 2019 (COVID-19) pandemic, a marked increase in sudden cardiac death (SCD) was observed. The p.S1103Y-SCN5A common variant, which is present in ∼8% of individuals of African descent, may be a circumstance-dependent, SCD-predisposing, proarrhythmic polymorphism in the setting of hypoxia-induced acidosis or QT-prolonging drug use. OBJECTIVE: The purpose of this study was to ascertain the effects of acidosis and hydroxychloroquine (HCQ) on the action potential duration (APD) in a patient-specific induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model of p.S1103Y-SCN5A. METHODS: iPSC-CMs were generated from a 14-year-old p.S1103Y-SCN5A-positive African American male. The patient's variant-corrected iPSC-CMs (isogenic control [IC]) were generated using CRISPR/Cas9 technology. FluoVolt voltage-sensitive dye was used to assess APD90 values in p.S1103Y-SCN5A iPSC-CMs compared to IC before and after an acidotic state (pH 6.9) or 24 hours of treatment with 10 µM HCQ. RESULTS: Under baseline conditions (pH 7.4), there was no difference in APD90 values of p.S1103Y-SCN5A vs IC iPSC-CMs (P = NS). In the setting of acidosis (pH 6.9), there was a significant increase in fold-change of APD90 in p.S1103Y-SCN5A iPSC-CMs compared to IC iPSC-CMs (P <.0001). Similarly, with 24-hour 10 µM HCQ treatment, the fold-change of APD90 was significantly higher in p.S1103Y-SCN5A iPSC-CMs compared to IC iPSC-CMs (P <.0001). CONCLUSION: Although the African-specific p.S1103Y-SCN5A common variant had no effect on APD90 under baseline conditions, the physiological stress of either acidosis or HCQ treatment significantly prolonged APD90 in patient-specific, re-engineered heart cells.

2.
Mayo Clin Proc ; 95(6): 1213-1221, 2020 06.
Article in English | MEDLINE | ID: covidwho-1450185

ABSTRACT

As the coronavirus disease 19 (COVID-19) global pandemic rages across the globe, the race to prevent and treat this deadly disease has led to the "off-label" repurposing of drugs such as hydroxychloroquine and lopinavir/ritonavir, which have the potential for unwanted QT-interval prolongation and a risk of drug-induced sudden cardiac death. With the possibility that a considerable proportion of the world's population soon could receive COVID-19 pharmacotherapies with torsadogenic potential for therapy or postexposure prophylaxis, this document serves to help health care professionals mitigate the risk of drug-induced ventricular arrhythmias while minimizing risk of COVID-19 exposure to personnel and conserving the limited supply of personal protective equipment.


Subject(s)
Death, Sudden, Cardiac , Hydroxychloroquine , Long QT Syndrome , Lopinavir , Risk Adjustment/methods , Ritonavir , Torsades de Pointes , Anti-Infective Agents/administration & dosage , Anti-Infective Agents/adverse effects , Betacoronavirus/drug effects , Betacoronavirus/isolation & purification , COVID-19 , Coronavirus Infections/drug therapy , Coronavirus Infections/epidemiology , Death, Sudden, Cardiac/etiology , Death, Sudden, Cardiac/prevention & control , Drug Combinations , Drug Monitoring/methods , Drug Repositioning/ethics , Drug Repositioning/methods , Electrocardiography/methods , Humans , Hydroxychloroquine/administration & dosage , Hydroxychloroquine/adverse effects , Long QT Syndrome/chemically induced , Long QT Syndrome/mortality , Long QT Syndrome/therapy , Lopinavir/administration & dosage , Lopinavir/adverse effects , Pandemics , Pneumonia, Viral/drug therapy , Pneumonia, Viral/epidemiology , Ritonavir/administration & dosage , Ritonavir/adverse effects , SARS-CoV-2 , Torsades de Pointes/chemically induced , Torsades de Pointes/mortality , Torsades de Pointes/therapy
4.
Circulation ; 143(13): 1274-1286, 2021 03 30.
Article in English | MEDLINE | ID: covidwho-1180993

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)
Artificial Intelligence , Electrocardiography/methods , Heart Diseases/diagnosis , Heart Rate/physiology , Adult , Aged , Area Under Curve , COVID-19/physiopathology , COVID-19/virology , Electrocardiography/instrumentation , Female , Heart Diseases/physiopathology , 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
5.
Circulation ; 143(13): 1274-1286, 2021 03 30.
Article in English | MEDLINE | ID: covidwho-1058120

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)
Artificial Intelligence , Electrocardiography/methods , Heart Diseases/diagnosis , Heart Rate/physiology , Adult , Aged , Area Under Curve , COVID-19/physiopathology , COVID-19/virology , Electrocardiography/instrumentation , Female , Heart Diseases/physiopathology , 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
7.
Mayo Clin Proc Innov Qual Outcomes ; 5(1): 137-150, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-899296

ABSTRACT

OBJECTIVE: To systematically review the literature and to estimate the risk of chloroquine (CQ) and hydroxychloroquine (HCQ) cardiac toxicity in patients with coronavirus disease 2019 (COVID-19). METHODS: We searched multiple data sources including PubMed/MEDLINE, Ovid Embase, Ovid EBM Reviews, Scopus, and Web of Science and medrxiv.org from November 2019 through May 27, 2020. We included studies that enrolled patients with COVID-19 treated with CQ or HCQ, with or without azithromycin, and reported on cardiac toxic effects. We performed a meta-analysis using the arcsine transformation of the different incidences. RESULTS: A total of 19 studies with a total of 5652 patients were included. The pooled incidence of torsades de pointes arrhythmia, ventricular tachycardia, or cardiac arrest was 3 per 1000 (95% CI, 0-21; I 2 =96%) in 18 studies with 3725 patients. Among 13 studies of 4334 patients, the pooled incidence of discontinuation of CQ or HCQ due to prolonged QTc or arrhythmias was 5% (95% CI, 1-11; I 2 =98%). The pooled incidence of change in QTc from baseline of 60 milliseconds or more or QTc of 500 milliseconds or more was 9% (95% CI, 3-17; I 2 =97%). Mean or median age, coronary artery disease, hypertension, diabetes, concomitant QT-prolonging medications, intensive care unit admission, and severity of illness in the study populations explained between-studies heterogeneity. CONCLUSION: Treatment of patients with COVID-19 with CQ or HCQ is associated with an important risk of drug-induced QT prolongation and relatively higher incidence of torsades de pointes, ventricular tachycardia, or cardiac arrest. Therefore, these agents should not be used routinely in the management of COVID-19 disease. Patients with COVID-19 who are treated with antimalarials for other indications should be adequately monitored.

SELECTION OF CITATIONS
SEARCH DETAIL