Automated electronic health record-based screening for Fabry disease in unexplained left ventricular hypertrophy (FAPREV-HCM).

Abstract

Background and aims: Hypertrophic cardiomyopathy (HCM) has various aetiologies, including genetic conditions like Fabry disease (FD), a lysosomal storage disorder. FD prevalence in high-risk HCM populations ranges from 0.3% to 11.8%. Early diagnosis of FD is crucial due to available treatments, but its rarity and diverse symptoms complicate identification. Heart-specific FD variants often lead to late diagnoses due to the absence of typical FD symptoms. This prospective study (NCT04943991) was conducted to identify patients with undiagnosed FD using electronic health records (EHR) at a German tertiary-care hospital.

Methods: Over 20 years (2000-2020), 2824 patients with 'left ventricular hypertrophy (LVH)' or 'hypertrophic cardiomyopathy (HCM)' were identified by full-text search. Exclusion criteria were age over 85, other diagnosed cardiomyopathies, significant valvular heart disease, death, active malignancy and prior FD testing. The remaining patients received an invitation for FD genetic testing.

Results: Of the 2824 identified patients, 2626 (93%) fulfilled the exclusion criteria. Among the 198 included patients, 96 responded, and 55 underwent genetic testing, yielding a response rate of 48% and a testing rate of 28%. In one patient (1.8% of tested), FD was diagnosed with the p.N215S variant. Subsequent family screening revealed six additional FD cases, with four initiating FD-specific therapies. Comprehensive clinical evaluations were conducted in five of the seven identified patients.

Conclusions: Genetic testing of patients with unexplained LVH/HCM using EHR is effective for identifying FD. Subsequent family screening further identified at-risk individuals, promoting regular follow-ups and if needed FD-specific therapies. This approach highlights the potential for broader application in high-risk populations to uncover treatable genetic conditions. The next phase should focus on automating the executed search process.

Trial registration number: NCT04943991.