Evidence map›Paper›PMID 28355432›Full record

SynthesisJAMA cardiology2017

Primary Prevention Implantable Cardioverter Defibrillators in Patients With Nonischemic Cardiomyopathy: A Meta-analysis.

Sana M Al-Khatib, Gregg C Fonarow, Jose A Joglar, Lurdes Y T Inoue, Daniel B Mark, Kerry L Lee, Alan Kadish, Gust Bardy, Gillian D Sanders

Abstract readMeta-Analysis
In one paragraph

Synthesis in JAMA cardiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 4 pooled it
–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

32 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. A Contemporary Review of Subcutaneous and Extravascular Implantable Cardiac Defibrillators.The Journal of innovations in cardiac rhythm management · 2026
    Review
  7. Review
  8. Article
  9. Article
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  11. Enhancing patient acceptance of ICD implantation through structured shared decision making: conversation is key.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2024
    Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Review
  17. Review
  18. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Sana M Al-KhatibDivision of Cardiology, Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina.
Gregg C FonarowDivision of Cardiology, UCLA (University of California, Los Angeles).
Jose A JoglarDivision of Cardiology, University of Texas Southwestern, Dallas.
Lurdes Y T InoueDepartment of Biostatistics, University of Washington, Seattle.
Daniel B MarkDivision of Cardiology, Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina.
Kerry L LeeDepartment of Biostatistics and Bioinformatics, Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina.
Alan KadishDivision of Cardiology, Feinberg School of Medicine, Northwestern Memorial Hospital, Chicago, Illinois.
Gust BardyDivision of Cardiology, University of Washington, Seattle.
Gillian D SandersDepartment of Clinical Pharmacology, Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Conflicting data have emerged on the efficacy of implantable cardioverter defibrillators (ICDs) for primary prevention of sudden cardiac death (primary prevention ICDs) in patients with nonischemic cardiomyopathy. Objective: To investigate the association of primary prevention ICDs with all-cause mortality in patients with nonischemic cardiomyopathy. Data Sources: PubMed was searched from January 1, 2000, through October 31, 2016, for the terms implantable defibrillator OR implantable cardioverter defibrillator AND non-ischemic cardiomyopathy. Additional references were identified from bibliographies of pertinent articles and queries to experts in this field. Study Selection: Inclusion criteria consisted of a randomized clinical trial design and comparison of the ICD with medical therapy (control) in at least 100 patients with nonischemic cardiomyopathy. In addition, studies had to report on all-cause mortality during a follow-up period of at least 12 months and be published in English. The search yielded 10 studies, of which only 1 met the inclusion criteria. A search of bibliographies of pertinent articles and queries of experts in this field led to 3 additional studies. Data Extraction and Synthesis: The PRISMA guidelines were used to abstract data and assess data quality and validity. Data were pooled using fixed- and random-effects models. Main Outcomes and Measures: The primary end point was all-cause mortality. Before data collection started, primary prevention ICDs were hypothesized to reduce all-cause mortality among patients with nonischemic cardiomyopathy. Results: Four randomized clinical trials met the selection criteria and included 1874 unique patients; 937 were in the ICD group and 937 in the control group. Pooling data from these trials showed a significant reduction in all-cause mortality with an ICD (hazard ratio, 0.75; 95% CI, 0.61-0.93; P = .008; P = .87 for heterogeneity). Conclusions and Relevance: Primary prevention ICDs are efficacious at reducing all-cause mortality among patients with nonischemic cardiomyopathy. These findings support professional guidelines that recommend the use of ICDs in such patients.

Indexed as

Defibrillators, ImplantableMortalityPrimary PreventionCardiomyopathiesCause of DeathDeath, Sudden, CardiacHeart FailureHumansProportional Hazards ModelsRandomized Controlled Trials as Topic

Identifiers

PMID28355432
PMCPMC5815025

What OpenQuestion holds

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Read underepoch 390

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.