Evidence map›Paper›PMID 35495864›Full record

ReviewCJC open2022

Arrhythmia Monitoring for Risk Stratification in Hypertrophic Cardiomyopathy.

Darson Du, Christopher O Y Li, Kevin Ong, Ashkan Parsa, Adaya Weissler-Snir, Jeffrey B Geske, Zachary Laksman

Abstract readReview
In one paragraph

Review in CJC open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
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

7 authors.

Darson DuDivision of Cardiology, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Christopher O Y LiDivision of Cardiology, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Kevin OngDivision of Cardiology, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Ashkan ParsaDivision of Cardiology, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
Adaya Weissler-SnirDivision of Cardiology, Hartford Hospital, University of Connecticut, Hartford, Connecticut, USA.
Jeffrey B GeskeDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Zachary LaksmanDivision of Cardiology, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy, presenting significant clinical heterogeneity. Arrhythmia risk stratification and detection are critical components in the evaluation and management of all cases of HCM. The 2020 American Heart Association/American College of Cardiology HCM guidelines provide new recommendations for periodic 24-48-hour ambulatory electrocardiogram monitoring to screen for atrial and ventricular arrhythmias. A strategy of more frequent or prolonged monitoring would lead to earlier arrhythmia recognition and the potential for appropriate treatment. However, whether such a strategy in patients with HCM results in improved outcomes is not yet established. The available evidence, knowledge gaps, and potential merits of such an approach are reviewed. Cardiac implantable electronic devices provide an opportunity for early arrhythmia detection, with the potential to enable early management strategies in order to improve outcomes.

Identifiers

PMID35495864
PMCPMC9039556

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.