Evidence map›Paper›PMID 35734292›Full record

ArticleHeart rhythm O22022

Longitudinal electrocardiographic assessment in Brugada syndrome.

Jonathan M Daw, C Anwar A Chahal, Jeffrey S Arkles, David J Callans, Sanjay Dixit, Andrew E Epstein, David S Frankel, Fermin C Garcia, Matthew C Hyman, Ramanan Kumareswaran and 9 more

Open access · goldAbstract read
In one paragraph

Article in Heart rhythm O2, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
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

19 authors at 1 institution in 1 country.

Jonathan M DawElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
C Anwar A ChahalElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Jeffrey S ArklesElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
David J CallansElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Sanjay DixitElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Andrew E EpsteinElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
David S FrankelElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Fermin C GarciaElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Matthew C HymanElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Ramanan KumareswaranElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
David LinElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Saman NazarianElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Michael P RileyElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Pasquale SantangeliElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Robert D SchallerElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Gregory E SuppleElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Cory TschabrunnElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Francis E MarchlinskiElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
Rajat DeoElectrophysiology Section, Division of Cardiovascular Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania.
University of Pennsylvania · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The type 1 electrocardiographic (ECG) pattern diagnostic of Brugada syndrome (BrS) can be dynamic. Limited studies have rigorously evaluated the temporal stability of the Brugada ECG pattern. Objective: We sought to evaluate fluctuations of the Brugada pattern in serial resting ECGs from BrS patients managed within a large health care system. Methods: In our cohort of BrS patients with at least 2 standard, resting ECGs recorded on separate clinical encounters, we evaluated serial changes in the Brugada pattern and categorized patients into 1 of 3 groups: dynamic was defined as the presence of both type 1 and non-type 1 patterns in available ECGs; the provoked-only group was defined as having a non-type 1 Brugada pattern across resting ECGs; and the persistent group was defined as having a type 1 pattern on all ECGs. We also evaluated the clinical risk in this cohort according to the Shanghai risk score. Results: In 72 patients with BrS (mean age 46 ± 15 years, 69% male), 828 standard, resting ECGs were recorded over a median duration of 30.2 (interquartile range 6.3-68.1) months. The dynamic group comprised 50 (69% of the cohort) patients, the provoked-only group consisted of 17 patients (24% of the cohort), and the persistent group included 5 patients. No significant differences were detected in the total number of ECGs evaluated during the follow-up period between any of the groups. Only sinus node dysfunction and a prior cardiac arrest were associated with the persistent type 1 group. The majority of patients had a low annualized risk of lethal arrhythmic events. Conclusion: Most BrS patients have a dynamic Brugada pattern noted on longitudinal, resting ECGs. Expert consensus statements should provide clarity on the frequency of obtaining resting ECGs in patients suspected of having BrS during follow-up.

Indexed as

Brugada syndromeChannelopathiesElectrocardiographyRisk stratificationSudden cardiac death

Identifiers

PMID35734292
PMCPMC9207730
OpenAlexW4210943091

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.