Evidence map›Paper›PMID 39247435›Full record

ArticleInternational journal of cardiology. Heart & vasculature2024

Predictive value of cardiac magnetic resonance imaging for fatal arrhythmias in structural and nonstructural heart diseases.

Xing Xing, Xiaoqiang Liu, Yi Zhang, Lei Zhang, Gu Shen, Yulong Ge, Fang Wang

Abstract read
In one paragraph

Article in International journal of cardiology. Heart & vasculature, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Xing XingDepartment of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaoqiang LiuDepartment of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yi ZhangDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lei ZhangDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Gu ShenDepartment of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yulong GeDepartment of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Fang WangDepartment of Cardiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The risk stratification for fatal arrhythmias remains inadequate. Cardiac magnetic resonance (CMR) imaging provides a detailed evaluation of arrhythmogenic substrates. This study investigated the predictive capacity of multiparametric CMR for fatal ventricular arrhythmias (VAs) in a heterogeneous disease cohort. Methods: The study included 396 consecutive patients with structural heart disease (SHD, n = 248) and non-apparent SHD (n = 148) who underwent CMR scans between 2018 and 2022. The primary endpoint was fatal composite arrhythmias. Results: Thirty-three patients (8.3 %) experienced fatal arrhythmias (25 with SHD, 8 with non-apparent SHD) over a median follow-up of 24 months. The independent risk factors for patients with SHD included syncope (hazard ratio [HR] = 5.347; Conclusion: CMR provides effective prognostic information for patients with and without apparent SHD. The presence of LGE, CMR myocardial abnormalities, and right ventricular dysfunction are strong risk markers for fatal arrhythmias.

Indexed as

Cardiac magnetic resonanceFatal arrhythmiaSudden cardiac death

Identifiers

PMID39247435
PMCPMC11379979

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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.