The Australian Genetic Heart Disease Registry: Protocol for a Data Linkage Study
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".