Background: Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, direct-to-patient, self-applied ECG patch use has substantially increased. There are limited data comparing clinic with self-applied electrocardiogram (ECG) patches.
Objective: The purpose of this study was to compare rates of ECG patch return, percentages of time patches yielded analyzable data (analyzable time), and percentages of prescribed time ECG patches were worn between clinic and self-applied ECG patches before and during COVID-19.
Methods: A retrospective analysis of patients prescribed an ECG patch during "pre-COVID" (March 1, 2019, through March 1, 2020) and "COVID" (April 4, 2020, through April 1, 2021) years was performed. ECG patch return rates, mean percentages of analyzable time, and mean percentages of prescribed wear time were compared between clinic and self-applied groups.
Results: Among the 29,093 ECG patch prescriptions (19% COVID self-applied), the COVID self-applied group had a lower return rate (90.8%) than did both clinic-applied groups (COVID: 97.1%; pre-COVID: 98.1%; P < .001). Among the 28,048 ECG patches (17.5% self-applied) returned for analysis, the COVID self-applied group demonstrated a lower mean percentage of analyzable time (95.9% ± 8.2%) than did both clinic-applied groups (COVID: 96.6% ± 6.6%; pre-COVID 96.6% ± 7.4%; P < .001). There were no differences in the mean percentage of prescribed wear time between groups (pre-COVID clinic-applied: 96.7% ± 34.3%; COVID clinic-applied: 97.4% ± 39.8%; COVID self-applied: 98.1% ± 52.1%; P = .09).
Conclusion: Self-applied ECG patches were returned at a lower rate and had a statistically lower percentage of analyzable time than clinic-applied patches. However, there were no differences between groups in mean percentages of prescribed wear time, and mean percentages of analyzable time were >95% in all groups.
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http://dx.doi.org/10.1016/j.hrthm.2022.11.020 | DOI Listing |
Commun Med (Lond)
January 2025
Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
Background: The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures.
Methods: We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated.
BMJ Open
January 2025
Department of Medical Research Bærum Hospital, Vestre Viken Hospital Trust, Gjettum, Norway
Objectives: The objective of this study was to assess the quality of ECG recordings and the concordance between the automatic detection of cardiac arrhythmia episodes by a patch ECG and an insertable cardiac monitor.
Design: Prospective cohort study.
Setting And Participants: Endurance athletes diagnosed with paroxysmal atrial fibrillation (AF) and no other relevant comorbidities participating in a randomised controlled trial on the effects of training adaption.
J Mol Cell Cardiol
January 2025
Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1QW, UK. Electronic address:
Introduction: Brugada Syndrome (BrS) is an inherited arrhythmia syndrome characterised by ST-segment elevation in the right precordial ECG leads and is associated with an increased risk of sudden cardiac death. We identify and characterise a novel SCN3B variant encoding the regulatory β3-subunit of the cardiac voltage-gated sodium channel, Na1.5.
View Article and Find Full Text PDFJ Electrocardiol
December 2024
VPG Medical, Inc., Rochester, NY, USA.
Passive cardiac monitoring has become synonymous with wearable technologies, necessitating patients to incorporate new devices into their daily routines. While this requirement may not be a burden for many, it is a constraint for individuals with chronic diseases who already have their daily routine. In this study, we introduce an innovative technology that harnesses the front-facing camera of smartphones to capture pulsatile signals discreetly when users engage in other activities on their device.
View Article and Find Full Text PDFAm J Crit Care
January 2025
Salah S. Al Zaiti is a professor, School of Nursing, University of Rochester, Rochester, New York.
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