The Australian Genetic Heart Disease Registry: Protocol for a Data Linkage Study

Alexandra Butters; Bianca Blanch; Anna Kemp-Casey; Judy Do; Laura Yeates; Felicity Leslie; Christopher Semsarian; Lee Nedkoff; Tom Briffa; Jodie Ingles; Joanna Sweeting

doi:10.2196/48636

The Australian Genetic Heart Disease Registry: Protocol for a Data Linkage Study

JMIR Res Protoc. 2023 Sep 20:12:e48636. doi: 10.2196/48636.

Authors

Alexandra Butters^{1

2

3}, Bianca Blanch⁴, Anna Kemp-Casey⁵, Judy Do^{1

2}, Laura Yeates^{1

2

3

4

6}, Felicity Leslie^{1

2}, Christopher Semsarian^{3

4

6}, Lee Nedkoff^{7

8}, Tom Briffa⁷, Jodie Ingles^{1

2

6}, Joanna Sweeting^{1

2}

Affiliations

¹ Clinical Genomics Laboratory, Centre for Population Genomics, Garvan Institute of Medical Research, Darlinghurst, Australia.
² Clinical Genomics Laboratory, Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Australia.
³ Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
⁴ Agnes Ginges Centre for Molecular Cardiology, Centenary Institute, The University of Sydney, Sydney, Australia.
⁵ Clinical and Health Sciences, University of South Australia, Adelaide, Australia.
⁶ Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia.
⁷ School of Population and Global Health, The University of Western Australia, Perth, Australia.
⁸ Victor Change Cardiac Research Institute, Sydney, Australia.

PMID: 37728963
PMCID: PMC10551791
DOI: 10.2196/48636

Abstract

Background: Genetic heart diseases such as hypertrophic cardiomyopathy can cause significant morbidity and mortality, ranging from syncope, chest pain, and palpitations to heart failure and sudden cardiac death. These diseases are inherited in an autosomal dominant fashion, meaning family members of affected individuals have a 1 in 2 chance of also inheriting the disease ("at-risk relatives"). The health care use patterns of individuals with a genetic heart disease, including emergency department presentations and hospital admissions, are poorly understood. By linking genetic heart disease registry data to routinely collected health data, we aim to provide a more comprehensive clinical data set to examine the burden of disease on individuals, families, and health care systems.

Objective: The objective of this study is to link the Australian Genetic Heart Disease (AGHD) Registry with routinely collected whole-population health data sets to investigate the health care use of individuals with a genetic heart disease and their at-risk relatives. This linked data set will allow for the investigation of differences in outcomes and health care use due to disease, sex, socioeconomic status, and other factors.

Methods: The AGHD Registry is a nationwide data set that began in 2007 and aims to recruit individuals with a genetic heart disease and their family members. In this study, demographic, clinical, and genetic data (available from 2007 to 2019) for AGHD Registry participants and at-risk relatives residing in New South Wales (NSW), Australia, were linked to routinely collected health data. These data included NSW-based data sets covering hospitalizations (2001-2019), emergency department presentations (2005-2019), and both state-wide and national mortality registries (2007-2019). The linkage was performed by the Centre for Health Record Linkage. Investigations stratifying by diagnosis, age, sex, socioeconomic status, and gene status will be undertaken and reported using descriptive statistics.

Results: NSW AGHD Registry participants were linked to routinely collected health data sets using probabilistic matching (November 2019). Of 1720 AGHD Registry participants, 1384 had linkages with 11,610 hospital records, 7032 emergency department records, and 60 death records. Data assessment and harmonization were performed, and descriptive data analysis is underway.

Conclusions: We intend to provide insights into the health care use patterns of individuals with a genetic heart disease and their at-risk relatives, including frequency of hospital admissions and differences due to factors such as disease, sex, and socioeconomic status. Identifying disparities and potential barriers to care may highlight specific health care needs (eg, between sexes) and factors impacting health care access and use.

International registered report identifier (irrid): DERR1-10.2196/48636.

Keywords: arrhythmia; big data; cardiology; cardiomyopathies; data linkage; genetic heart diseases; genetics; harmonization; health care use; heart; mortality; national; probabilistic matching; registries; registry; risk.

©Alexandra Butters, Bianca Blanch, Anna Kemp-Casey, Judy Do, Laura Yeates, Felicity Leslie, Christopher Semsarian, Lee Nedkoff, Tom Briffa, Jodie Ingles, Joanna Sweeting. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 20.09.2023.