Familial hypercholesterolemia (FH) is an inherited disorder characterized by high levels of blood cholesterol from birth and premature coronary heart disease (CVD). Thus, the identification of FH patients is crucial to prevent the onset of cardiovascular events. Our aim was to evaluate the performance of the Dutch Lipid Clinic Network (DLCN) score, using this evidence to develop a national-based algorithm for FH diagnosis in the Italian setting.
The DLCN score was applied on a sample of adults with genetically-confirmed FH, evaluating the impact of its criteria (personal/family history of CVD, presence of xanthomata/arcus cornealis and patients untreated lipid levels), and of missing data on the DLCN score performance. These criteria and additional factors, such as triglycerides or Lp(a) levels, will be considered to create a system of weights based on covariates predictive relevance, and calculate the probability for each subject of having FH. Stepwise logistic regression analyses will be carried out.
The DLCN score was applied on 1377 adults with genetically-confirmed FH, resulting in 28.5% of the sample classified as probable FH and 37.9% as definite FH. Among the sample, 10.0% had ≥4 missing data. We found that using estimated pre-treatment LDL-C levels may significantly modify the DLCN score, causing higher percentages of patients classified as probable or definite FH.
The DLCN score failed to identify a third of subjects with genetically-confirmed FH. An update of this tool and its validation in Italian national context might help to improve diagnosis and management of FH patients.