Electronic health records to facilitate continuous detection of familial hypercholesterolemia

Shari Pepplinkhuizen 1, Shirin Ibrahim 2, Rutger Vink 3, Bas Groot 3, Erik S G Stroes 2, Willem A Bax 4, Jan H Cornel 5

Affiliations

PMID: 32927124
DOI: 10.1016/j.atherosclerosis.2020.07.022

Abstract

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.

Keywords: Algorithm; Electronic health records (EHRs); Familial hypercholesterolaemia; LDL-Cholesterol.