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.
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| http://dx.doi.org/10.1136/openhrt-2017-000686 | DOI Listing |
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