New prediction model identifies more young individuals with FH for DNA screeningLiterature - Besseling J et al., Eur Heart J Lipids 2016
Selection of individuals for genetic testing for familial hypercholesterolaemia: development and external validation of a prediction model for the presence of a mutation causing familial hypercholesterolaemia
Besseling J, Reitsma JB, Gaudet D, et al.
Eur Heart J Lipids 2016; published online ahead of print
BackgroundFH is a monogenic disorder of lipid metabolism, seen as either the rare homozygous, or the more common heterozygous form (HeFH) . Patients with HeFH have markedly elevated LDL-C levels and without treatment, they are exposed to a 3-4-fold increase of CV risk compared with the general population [2,3]. Hence, early detection of HeFH is very important and the definite diagnosis is possible by identifying a molecular defect in one of three different genes : the LDL receptor (LDLR), the apolipoprotein B (APOB), or proprotein convertase subtilisin/kexin type 9 (PCSK9).
Although the identification of HeFH patients with genetic cascade screening and their treatment with statins is recommended in guidelines and was shown to be cost-effective, the selection of patients for genetic testing in clinical practice remains challenging [5,6]. The available algorithms [7-9] fail to identify young individuals, leading to under-diagnosis and under-treatment of young HeFH patients because [10,11]:
- the presence of tendon xanthomas is an important criterion, but it is a rare finding in young individuals
- fixed, non-age-adjusted cut-off values for LDL-C are used to classify patients
- the required information on family history of lipid disorders and premature CV is often absent
Main resultsPrediction model:
The final prediction model included age, sex, levels of LDL-C, HDL-C, and triglycerides, history and age of CVD, use of statins, smoking, alcohol, and presence of hypertension. The regression coefficients of the predictors were used to construct an interactive web-based calculator that can be used to calculate the probability of the presence of an FH mutation in individual subjects
Model performance in the development cohort:
The AUC of the final model was 85.4% (95% CI: 85.0–85.9), and the slope of the calibration line was 1.02 (optimal slope is 1.00).
Prediction example: out of 29,331 (45.8%) persons who had a predicted probability of 0.30 or lower:
- 25,473 (86.8%) did not carry an FH mutation
- 3,857 were found to have an FH mutation (14.7% of all FH patients)
The AUC of the final model was 95.4% (95% CI: 94.7–96.1) in the external validation cohort, and the slope of the calibration line was 1.06.
ConclusionA new prediction model developed to identify the presence of a deleterious FH mutation showed good discrimination and calibration. This model might add value for the selection of individuals eligible for DNA analysis for an FH mutation, particularly at young age.
Find this article online at Eur Heart J Lipids
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