Use of a Smartwatch for Assessment of the QT Interval in Outpatients with Coronavirus Disease 2019

Chinitz, Jason S.; Goyal, Rajat; Morales, Donna Chelle; Harding, Melissa; Selim, Samy; Epstein, Laurence M.

doi:10.19102/icrm.2020.1100904

Cited by 12 publications

(10 citation statements)

References 13 publications

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“…This number of adequate measurements increased to 94% when the SW was moved to alternative positions. Chinitz et al ( 42 ) published a case report about a physician in home isolation due to a COVID-19 infection. She was prescribed hydroxychloroquine and considered at moderate risk for drug-associated QT prolongation.…”

Section: Resultsmentioning

confidence: 99%

Smart devices to measure and monitor QT intervals

Hoek,

Brouwer,

Voors

et al. 2023

Front. Cardiovasc. Med.

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Careful observation of the QT interval is important to monitor patients with long QT syndrome and during treatment with potentially QT-prolonging medication. It is also crucial in the development of novel drugs, in particular in case of a potential side effect of QT prolongation and in patients with increased risk of QT prolongation. The 12-lead electrocardiogram (ECG) is the gold standard to evaluate cardiac conduction and repolarization times. Smartwatches and smart devices offer possibilities for ambulatory ECG recording and therefore measuring and monitoring the QT interval. We performed a systematic review of studies on smartwatches and smart devices for QTc analysis. We reviewed PubMed for smartwatches and smart devices that can measure and monitor the QT interval. A total of 31 studies were included. The most frequent devices were (1) KardiaMobile 6L, a Food and Drug Administration-approved device for QTc analyses that provides a 6-lead ECG, (2) an Apple Watch, a smartwatch with an integrated ECG tool that allows recording of a single-lead ECG, and (3) the Withings Move ECG ScanWatch, an analog watch with a built-in single-lead ECG. The KardiaMobile 6L device and the Apple Watch provide accurate measurements of the QT interval, although the Apple Watch is studied in standard and non-standard positions, and the accuracy of QT measurements increased when the smartwatch was moved to alternative positions. Most studies were performed on patients, and limited results were available from healthy volunteers.

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Section: Resultsmentioning

confidence: 99%

Smart devices to measure and monitor QT intervals

Hoek,

Brouwer,

Voors

et al. 2023

Front. Cardiovasc. Med.

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“…Prior published studies of COVID-19 have used portable EKG monitoring devices solely in the inpatient setting [ 27 ] or as a one-time validation of the QT intervals as measured by Apple SmartWatches® compared with standard 12-lead EKGs in a variety of treatment settings [ 28 ]. There is one case report of a COVID-19 patient monitored at home via an Apple Smartwatch® [ 29 ]. The 6-lead device proved to be advantageous over its single-lead counterparts by providing additional leads for EKG analysis: Single-lead devices are only capable of providing information from lead I, but nearly 73% (296/407) of the EKGs in our study were felt to have more reliable and accurate interval measurements in leads other than lead I.…”

Section: Discussionmentioning

confidence: 99%

Testing the feasibility of operationalizing a prospective, randomized trial with remote cardiac safety EKG monitoring during a pandemic

Liu

Ezekowitz

Columbo

et al. 2021

J Interv Card Electrophysiol

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Background The coronavirus SARS-CoV-2 is highly contagious. Hydroxychloroquine (HCQ) has in vitro activity against SARS-CoV-2. The FDA authorized emergency use of HCQ against COVID-19. HCQ may have dose-related cardiotoxicity. This clinical trial received ethical approval on May 15, 2020, operationalized in June to evaluate a low prophylaxis dose of HCQ (200mg BID) in household contacts of COVID-19-positive patients without physical contact between investigators and participants. It represents the first report of the FDA approved 6-lead EKGs with a smartphone KardiaMobile® 6L application. Methods To reach a sample size of 170, household members were contacted by telephone, emailed consent forms with electronic signature capability, and randomized 2:1 to HCQ or observation for 10 days with follow-up of 14 days. Home saliva PCR tests recorded COVID status on days 1 and 14. Symptoms and 6-lead EKGs were obtained daily. Results Fifty-one participants were randomized with 42 evaluable at day 14. Remote monitoring of 407 EKGs revealed no QTc prolongation or other ECG changes in either group. At time of consent, no participants were symptomatic or COVID+. On days 1 and 14, COVID tests were positive in 4 and 2 in the HCQ group and 4 and 0 in the observation group. No tests converted to positive. There were no deaths or hospitalizations. Conclusions A clinical trial without personal contact, rapidly initiated and operationalized to exclude cardiac toxicity using daily remote 6-lead EKG monitoring, is feasible. Of 407 EKGs from 42 participants, there was no evidence of cardiac toxicity. Clinical trial registration Clinicaltrials.gov : NCT04652648 registration date: December 3, 2020

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“…A defining characteristic of QTNet is that it estimates the QTc only using data from Lead-I; i.e., the ECG lead most often acquired by pocket and wearable ECG monitoring devices (11)(12)(13)(14).…”

Section: Estimating Qt Intervals With Qtnetmentioning

confidence: 99%

Detecting QT prolongation From a Single-lead ECG With Deep Learning

Alam,

Aguirre,

Stultz

2023

Preprint

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Background and AimsFor a number of antiarrhythmics, drug loading requires a 3-day hospitalization with monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would facilitate out-of-hospital care. We aim to develop a deep learning model that infers QT intervals from ECG lead-I – the lead most often acquired from ambulatory ECG monitors – and we use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading.MethodsUsing 4.22 million 12-lead ECG recordings from 903.6 thousand patients at the Massachusetts General Hospital, we develop a deep learning model, QTNet, that infers QT intervals from lead-I. Over 3 million ECGs from 653 thousand patients are used to train the model and an internal-test set containing 633 thousand ECGs from 135 thousand patients was used for testing. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another institution. QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs.ResultsQTNet achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). For the ECGRDVQ-dataset, QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%.ConclusionsDrug-induced QT prolongation risk can be tracked from ECG lead-I using deep learning. This research leads the path toward out-of-hospital care using wearable ECG devices for antiarrhythmic therapies.What’s New?Using only Lead-I ECG, a novel deep neural network, QTNet, can estimate the QT intervals that are similar to those generated from the 12-lead ECG by the clinical ECG machines, with a mean absolute error of 12ms and a Pearson correlation coefficient of 0.91.The same QTNet, when applied without any fine-tuning on an external population undergoing Dofetilide loading, can identify whether and when clinically critical QT prolongation occurs after the drug loading. In comparison to manual annotations of QT intervals by clinical experts, QTNet achieves 87% sensitivity and 77% specificity.QTNet is a novel regression model that can be used on Lead-I ECG streams, potentially from wearable devices at out-of-hospital settings, for health critical applications such as drug-induced QT prolongation tracking.

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Use of a Smartwatch for Assessment of the QT Interval in Outpatients with Coronavirus Disease 2019

Cited by 12 publications

References 13 publications

Smart devices to measure and monitor QT intervals

Smart devices to measure and monitor QT intervals

Testing the feasibility of operationalizing a prospective, randomized trial with remote cardiac safety EKG monitoring during a pandemic

Detecting QT prolongation From a Single-lead ECG With Deep Learning

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