ArticleBJGP open2020
Evaluating a clinical tool (FAMCAT) for identifying familial hypercholesterolaemia in primary care: a retrospective cohort study.
Article in BJGP open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Improving the clinical diagnosis of familial hypercholesterolemia among patients attending diabetes and cardiology clinics.American journal of preventive cardiology · 2026Article
- Electronic health records-based algorithms to screen for U.S. Centers for Disease Control and Prevention tier 1 genetic diseases: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Risk factors for long-term cardiovascular post-acute sequelae of COVID-19 infection: A nested case-control study in Hong Kong.NPJ cardiovascular health · 2024Article
- Improving the Detection of Potential Cases of Familial Hypercholesterolemia: Could Machine Learning Be Part of the Solution?Journal of the American Heart Association · 2024Article
- Review
- Electronic health record-based facilitation of familial hypercholesterolaemia detection sensitivity of different algorithms in genetically confirmed patients.European heart journal. Digital health · 2022Article
- A mixed methods study of the awareness and management of familial hypercholesterolaemia in Irish general practice.Frontiers in medicine · 2022Article
- Comparing the performance of the novel FAMCAT algorithms and established case-finding criteria for familial hypercholesterolaemia in primary care.Open heart · 2021Article
- Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care.NPJ digital medicine · 2020Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFamilial hypercholesterolaemia (FH) is an inherited lipid disorder causing premature heart disease, which is severely underdiagnosed. Improving the identification of people with FH in primary care settings would help to reduce avoidable heart attacks and early deaths.
aimTo evaluate the accuracy of the familial hypercholesterolaemia case ascertainment identifcation tool (FAMCAT) for identifying FH in primary care. DESIGN &
settingA retrospective cohort study of 1 030 183 patients was undertaken. Data were extracted from the UK Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) database. Patient were aged >16 years.
methodThe FAMCAT algorithm was compared with methods of FH detection recommended by national guidelines (Simon Broome diagnostic criteria, Dutch Lipid Clinic Network [DLCN] Score, and cholesterol levels >99
resultsA total of 1707 patients had a diagnosis of FH. FAMCAT showed a high level of discrimination (AUC = 0.844, 95% confidence interval [CI] = 0.834 to 0.854), performing significantly better than Simon Broome criteria (AUC = 0.730, 95% CI = 0.719 to 0.741), DLCN Score (AUC = 0.766, 95% CI = 0.755 to 0.778), and screening cholesterols >99 th centile (AUC = 0.579, 95% CI = 0.571 to 0.588). Inclusion of premature myocardial infarction (MI) and fitting cholesterol as a continuous variable improved the accuracy of FAMCAT (AUC = 0.894, 95% CI = 0.885 to 0.903).
conclusionBetter performance of the FAMCAT algorithm, compared with other approaches for case finding of FH in primary care, such as Simon Broome criteria, DLCN criteria or very high cholesterol levels, has been confirmed in a large population cohort.
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