Characteristics and Quality of National Cardiac Registries: A Systematic Review

Abstract

Background: National cardiac registries are increasingly used for informing health policy, improving the quality and cost-effectiveness of patient care, clinical research, and monitoring the safety of novel treatments. However, the quality of registries is variable. We aimed to assess the characteristics and quality of national cardiac registries across all subspecialties of cardiac care.

Methods: Publications relating to national cardiac registries across six cardiac subspecialty domains were identified by searching MEDLINE and the Google advanced search function with 26 438 citations and 4812 full-text articles reviewed.

Results: A total of 155 registries, representing 49 countries, were included in the study. Of these, 45 related to coronary disease or percutaneous coronary intervention, 28 related to devices, arrhythmia, and electrophysiology, 24 related to heart failure, transplant, and mechanical support, 21 related to structural heart disease, 21 related to congenital heart disease, and 16 related to cardiac surgery. Enrollment was procedure-based in 60% and disease-based in 40%. A total of 73.10 million patients were estimated to have been enrolled in cardiac registries. Quality scoring was performed using a validated registry grading system, with registries performing best in the use of explicit variable definitions and worst in assessment of data reliability. Higher quality scores were associated with government funding, mandated enrollment, linkage to other registries, and outcome risk adjustment. Quality scores and number of registries within a country were positively correlated with each other and with measures of national economic output, health expenditure, and urbanization.

Conclusions: There has been remarkable growth in the uptake of national cardiac registries across the last few decades. However, the quality of processes used to ensure data completeness and accuracy remain variable and few countries have integrated registries covering multiple subspecialty domains. Clinicians, funders, and health policymakers should be encouraged to focus on the range, quality, and integration of these registries. Registration: URL: https://www.crd.york.ac.uk/prospero; Unique identifier: CRD42020204224.

Keywords: atrial fibrillation; coronary artery disease; percutaneous coronary intervention; quality of care; registries.

Figure 1.

Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) diagram. PRISMA diagram summarizing the search strategy used to identify national cardiac registries meeting inclusion criteria.

Figure 2.

National cardiac registries world map. World map illustrating number of subspecialty domains covered by country in red, median quality score by country in blue, and estimated number of patients entered into registries by country in green.

Figure 3.

National cardiac registry growth 1980–2020. Change in number of national cardiac registries and estimated total patients entered into national cardiac registries separated by subspecialty domain from 1980 to 2020. CAD indicates coronary artery disease; EP, electrophysiology; and HF, heart failure.

Figure 4.

Individual quality score components. Jitter plot demonstrating individual components of the quality score separated by subspecialty domain, including enrollment of at-risk population, interobserver and intraobserver coding reliability, use of explicit variable definitions, independence of observers from outcomes, data completeness for each variable collected, and methods of data validation (see Table). CAD indicates coronary artery disease and percutaneous coronary intervention; and EP, electrophysiology.

Figure 5.

Correlates with registry number, size, and data quality. Correlation matrix demonstrating the correlation coefficients between various variables. A correlation coefficient of >0.7 denotes a strong correlation, 0.3–0.7 a moderate correlation, and <0.3 a low correlation. Positive correlations are shown in blue while negative correlations are shown in orange. For example, a country’s total number of registries demonstrates a strong positive correlation (0.8) with gross domestic product (GDP), health expenditure (HE), and health expenditure as a percentage of gross domestic product. Registry variables are highlighted in bold, whereas World Bank country variables are not. Exp. indicates expenditure; NS, nonsignificant correlation (P>0.05); Year Est., year established; Corr, correlation coefficient; and Pop., population.