Evidence map›Paper›PMID 29209507›Full record

ArticleOpen heart

Routinely collected health data to study inherited heart disease: a systematic review (2000-2016).

Bianca Blanch, Joanna Sweeting, Christopher Semsarian, Jodie Ingles

Abstract read
In one paragraph

Article in Open heart. 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
–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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Bianca BlanchAgnes Ginges Centre for Molecular Cardiology, Centenary Institute, Sydney, New South Wales, Australia.
Joanna SweetingAgnes Ginges Centre for Molecular Cardiology, Centenary Institute, Sydney, New South Wales, Australia.
Christopher SemsarianAgnes Ginges Centre for Molecular Cardiology, Centenary Institute, Sydney, New South Wales, Australia.
Jodie InglesAgnes Ginges Centre for Molecular Cardiology, Centenary Institute, Sydney, New South Wales, Australia.ORCID 0000-0002-4846-7676

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Our understanding of inherited heart disease is predominantly based on retrospective specialised clinic cohorts, which have inherent selection bias. Population-based routinely collected data can provide insight into unbiased, large-scale patterns of treatment and care but may be limited by the granularity of clinical information available. We sought to synthesise the global literature to determine whether we can identify patients with inherited heart diseases using routinely collected health data. Methods: Medline, Embase, CINAHL, PreMEDLINE and Google Scholar citation databases were searched for relevant articles published between 1 January 2000 and 31 October 2016. Results: A total of 5641 titles/abstracts were screened and 46 full-text articles were retrieved. Twelve peer-reviewed, English-language manuscripts met our inclusion criteria. Studies predominantly focused on Marfan syndrome (41%) or hypertrophic cardiomyopathy (29%). All studies used International Classification of Disease diagnosis codes to define inherited heart disease populations; three studies also used procedure codes. Nine of the 17 definitions for inherited heart disease were repeated across studies. Conclusions: Inherited heart disease populations can be identified using routinely collected health data, though challenges relate to existing diagnosis codes. This is an underutilised resource with the potential to inform patterns of care, patient outcomes and overall disease burden.

Indexed as

arrhythmogenic right ventricular dyplasiacardiomyopathy hypertrophicmarfans

Identifiers

PMID29209507
PMCPMC5652561

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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.