Purpose of review Studies reaffirm that familial hypercholesterolemia is more prevalent than initially considered, with a population frequency of approximately one in 300. The majority of patients remains unidentified. This warrants critical evaluation of existing screening methods and exploration of novel methods of detection. Recent findings New public policy recommendations on the detection of familial hypercholesterolemia have been made by a global community of experts and advocates. Phenotypic tools for diagnosing index cases remain inaccurate. Genetic testing is the gold standard for familial hypercholesterolemia and a new international position statement has been published. Correction of LDL cholesterol (LDL-C) for the cholesterol content of lipoprotein(a) [Lp(a)] may increase the precision of the phenotypic diagnosis of familial hypercholesterolemia. Cascade cotesting for familial hypercholesterolemia and elevated Lp(a) levels provides a new opportunity to stratify risk in families. Digital technology and machine learning methods, coupled with clinical alert and decision support systems, lead the way in more efficient approaches for detecting and managing index cases. Universal screening of children, combined with child-parent cascade testing, appears to be the most effective method for underpinning a population strategy for maximizing the detection of familial hypercholesterolemia. Summary Detection of familial hypercholesterolemia can be enhanced by optimizing current diagnostic algorithms, probing electronic health records with novel information technologies and integrating universal screening of children with cascade testing of parents and other relatives.
Aims To validate the reported increased atherosclerotic cardiovascular disease (ASCVD) risk associated with very high lipoprotein(a) [Lp(a)] and to investigate the impact of routine Lp(a) assessment on risk reclassification. Methods and results We performed a cross-sectional case-control study in the Amsterdam UMC, a tertiary hospital in The Netherlands. All patients in whom a lipid blood test was ordered between October 2018 and October 2019 were included. Individuals with Lp(a) >99th percentile were age and sex matched to individuals with Lp(a) ≤20th percentile. We computed odds ratios (ORs) for myocardial infarction (MI) and ASCVD using multivariable logistic regression adjusted for age, sex, and systolic blood pressure. Furthermore, we assessed the additive value of Lp(a) to established ASCVD risk algorithms. Lipoprotein(a) levels were determined in 12 437 individuals, out of whom 119 cases [Lp(a) >99th percentile; >387.8 nmol/L] and 119 matched controls [Lp(a) ≤20th percentile; ≤7 nmol/L] were included. Mean age was 58 ± 15 years, 56.7% were female, and 30.7% had a history of ASCVD. Individuals with Lp(a) levels >99th percentile had an OR of 2.64 for ASCVD [95% confidence interval (CI) 1.45–4.89] and 3.39 for MI (95% CI 1.56–7.94). Addition of Lp(a) to ASCVD risk algorithms led to 31% and 63% being reclassified into a higher risk category for Systematic Coronary Risk Evaluation (SCORE) and Second Manifestations of ARTerial disease (SMART), respectively. Conclusion The prevalence of ASCVD is nearly three-fold higher in adults with Lp(a) >99th percentile compared with matched subjects with Lp(a) ≤20th percentile. In individuals with very high Lp(a), addition of Lp(a) resulted in one-third of patients being reclassified in primary prevention, and over half being reclassified in secondary prevention.
Background and aims: Familial hypercholesterolemia (FH) is an inherited disorder associated with increased risk of coronary heart disease as a result of high LDL-cholesterol (LDL-C). The clinical diagnosis can be made with the Dutch Lipid Clinic Network criteria (DLCN criteria). FH is an underdiagnosed disorder, possibly due to false negative LDL-C interpretation during lipid lowering therapy (LLT). We hypothesized that automated health record-based integration of data can provide a signal to facilitate identification of FH patients. Methods: We included patients with LDL-C ≥6.5 mmol/l after correction for LLT in all patients testing LDL-C in Northwest Clinics, The Netherlands. Patients previously diagnosed with FH were excluded. The primary endpoint was the additional number of patients with DLCN criteria ≥6 points after correction for LLT. Secondary endpoints were the additional number of patients with DLCN criteria ≥6 points after also adding data on patient-and family history, and LDL-C before and after correction for LLT. Analysis was performed in a daily automated routine (HiX ChipSoft). Results: In a total of 41,937 individual LDL-C measurements during 26 weeks, we found 351 patients with LDL-C ≥6.5 mmol/l after automated correction for LLT. FH had previously been diagnosed in 42 patients. In the remaining 309 patients (58.3% female; age: 66 ± 11 yrs (mean ± SD); 85.8% on LLT), the number of patients with DLCN criteria ≥6 points increased from 9 to 95 after correction for LLT, and to 127 after also adding patient and family history. The mean LDL-C before and after correction for LLT was 4.69 ± 1.42 mmol/l and 8.16 ± 1.68 mmol/l, respectively (mean ± SD; p < 0.001). Conclusions: We conclude that automated medical record-based integration of LDL-C, LLT and patient-and family history can provide a crucial signal to facilitate identification of FH. Whether this signal results in subsequent genetic identification of FH patients and their relatives requires further study.
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