Electronic health records to facilitate continuous detection of familial hypercholesterolemia. (October 2020)

• Record Type: Journal Article
• Authors: Pepplinkhuizen, Shari; Ibrahim, Shirin; Vink, Rutger; Groot, Bas; Stroes, Erik S.G.; Bax, Willem A.; Cornel, Jan H.
• Is Part Of: Atherosclerosis. Volume 310(2020)
• Page Start: 83
• Page End: 87
• Publication Date: 2020-10
• Subjects: Familial hypercholesterolaemia -- Electronic health records (EHRs) -- LDL-Cholesterol -- Algorithm; Arteriosclerosis -- Periodicals; Electronic journals; 616.136
• DOI: 10.1016/j.atherosclerosis.2020.07.022
• Languages: English
• ISSNs: 0021-9150

Abstract

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. Graphical abstract: Image 1 Highlights: Familial hypercholesterolemia (FH) results in high risk of coronary heart disease. FH is underdiagnosed, possibly due to lack of recognition by physicians. An automated algorithm in electronic health records (EHR) may facilitate detection of FH. Correction for lipid lowering therapy and patient characteristics are taken into account. This continuous EHR-based algorithm can facilitate FH detection.