Key result
The FAMCAT algorithm demonstrated high discrimination for identifying familial hypercholesterolaemia in primary care (AUC 0.844), performing significantly better than Simon Broome criteria (AUC 0.730) and DLCN Score (AUC 0.766).
Why the study?
Familial hypercholesterolaemia causes premature heart disease and is severely underdiagnosed, and improving identification in primary care could reduce avoidable heart attacks and early deaths.
Does the FAMCAT algorithm improve the identification of familial hypercholesterolaemia in primary care patients compared to standard clinical criteria?
Cohort (n=1,030,183)
Yes
Does the FAMCAT algorithm improve the identification of familial hypercholesterolaemia in primary care patients compared to standard clinical criteria?
Effect estimate: AUC 0.844 (95% CI 0.834 to 0.854)
Absolute Event Rate: 0.844% vs 0.73%
The FAMCAT algorithm demonstrates superior predictive accuracy for identifying familial hypercholesterolaemia in primary care compared to currently recommended clinical criteria.
Should not yet change FH screening practice; supports prospective validation of FAMCAT in primary care.
BACKGROUND: Familial 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. AIM: To evaluate the accuracy of the familial hypercholesterolaemia case ascertainment identifcation tool (FAMCAT) for identifying FH in primary care. DESIGN & SETTING: A 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. METHOD: centile). Discrimination and calibration were assessed by area under the receiver operating curve (AUC) and by comparing observed versus predicted cases. RESULTS: A 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). CONCLUSION: Better 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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Akyea et al. (2020) conducted a cohort in Familial hypercholesterolaemia (n=1,030,183). FAMCAT algorithm vs. Simon Broome criteria, DLCN Score, and cholesterol >99th centile was evaluated on Incident diagnosis of familial hypercholesterolaemia (discrimination assessed by AUC) (AUC 0.844, 95% CI 0.834 to 0.854). The FAMCAT algorithm demonstrated high discrimination for identifying familial hypercholesterolaemia in primary care (AUC 0.844), performing significantly better than Simon Broome criteria (AUC 0.730) and DLCN Score (AUC 0.766).
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