Evidence map›Paper›PMID 35036973›Full record

ArticleAmerican heart journal plus : cardiology research and practice2022

Arrhythmia patterns during and after hospitalization for COVID-19 infection detected via patch-based mobile cardiac telemetry.

Olga Reynbakh, Eric D Braunstein, Mike Hsu, Jeff Ellis, Lori Crosson, Judith Lenane, Andrew Krumerman, Luigi Di Biase, Kevin J Ferrick

Open access · goldAbstract read
In one paragraph

Article in American heart journal plus : cardiology research and practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.7field-weighted citation impact, top 36% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 2 countries.

Olga ReynbakhDivision of Cardiology, Department of Medicine, Albert Einstein College of Medicine/Montefiore Medical Center, 111 E 210th st, Bronx, NY 10467, United States of America.
Eric D BraunsteinDivision of Cardiology, Department of Medicine, Albert Einstein College of Medicine/Montefiore Medical Center, 111 E 210th st, Bronx, NY 10467, United States of America.
Mike HsuiRhythm Technologies, Inc., 699 8th St., Suite 600, San Francisco, CA 94103, United States of America.
Jeff EllisiRhythm Technologies, Inc., 2 Marriott Drive, Lincolnshire, IL 60069, United States of America.
Lori CrossoniRhythm Technologies, Inc., 2 Marriott Drive, Lincolnshire, IL 60069, United States of America.
Judith LenaneiRhythm Technologies, Inc., 2 Marriott Drive, Lincolnshire, IL 60069, United States of America.
Andrew KrumermanDivision of Cardiology, Department of Medicine, Albert Einstein College of Medicine/Montefiore Medical Center, 111 E 210th st, Bronx, NY 10467, United States of America.
Luigi Di BiaseDivision of Cardiology, Department of Medicine, Albert Einstein College of Medicine/Montefiore Medical Center, 111 E 210th st, Bronx, NY 10467, United States of America.
Kevin J FerrickDivision of Cardiology, Department of Medicine, Albert Einstein College of Medicine/Montefiore Medical Center, 111 E 210th st, Bronx, NY 10467, United States of America.
Albert Einstein College of Medicine · USiRhythm (United States) · USMontefiore Medical Center · US

Funding

CTSA Administrative Supplement for Informatics Core: A novel AI/ML system to predict respiratory failure and ARDS in Covid-19 patientsUL1TR002556 · NCATS · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI KELLER, MARLA J, KIM, MIMI Y · 2018 to 2022
$26.7M
NCATS NIH HHS UL1 TR002556
6 · The paper itself

Abstract

backgroundCoronavirus infection is the cause of the current world-wide pandemic. Cardiovascular complications occur in 20-30% of patients with COVID-19 infection including myocardial injury and arrhythmias. Current understanding of specific arrhythmia type and frequency is limited.

objectiveWe aimed to analyze arrhythmia type and frequency in patients with COVID infection, identifying arrhythmia patterns over time during hospitalization and post discharge utilizing a patch based mobile cardiac telemetry system.

methodsA prospective cohort study during the COVID-19 pandemic was performed. We included in our study patients hospitalized with COVID-19 infection who had a patch-based mobile telemetry device placed for cardiac monitoring.

resultsQuantitative reports for 59 patients were available for analysis. Arrhythmias were detected in 72.9% of patients and at a consistent frequency throughout the monitoring period in 52.9%-89.5% of patients daily. The majority of arrhythmias were SVT (59.3% of patients) and AF (22.0%). New onset AF was noted in 15.0% of all patients and was significantly associated with older age (OR 1.4 for 5 yrs. difference; 95% CI 1.03-2.13). Of 9 patients who were discharged with continued patch monitoring, 7 (78%) had arrhythmic events during their outpatient monitoring period.

conclusionIn COVID-19 patients arrhythmias were observed throughout hospitalization with a consistent daily frequency. Patients continued to exhibit cardiac arrhythmias after hospital discharge of a type and frequency similar to that seen during hospitalization. These findings suggest that the risk of arrhythmia associated with COVID infection remains elevated throughout the hospital course as well as following hospital discharge.

Indexed as

ArrhythmiaCardiac monitoringCOVID-19Mobile telemetry

Identifiers

PMID35036973
PMCPMC8744398
OpenAlexW4205254075

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.