Abstract
Misdiagnoses are very common in Fabry disease (FD). The clinical manifestation of the disease is highly variable leading in a broad range of possible differential diagnoses. According to the literature 25% of FD patients were firstly misdiagnosed with a mean time of over 13 years between the onset of symptoms and diagnosis. Frequently misdiagnoses of Fabry disease include fibromyalgia syndrome, rheumatic fever and arthritis, hypertrophic cardiomyopathy, chronic kidney disease with unknown aetiology and others. Recognition, diagnosis and treatment availabilities of Fabry disease can prevent patients from life threating complications such as sudden cardiac death, end-stage chronic renal disease, malignant arrhythmias or stroke. The aim of this study was to develop and validate case-finding algorithms for Fabry disease, covered by diagnosis of rheumatologic (RMD), renal (RD), cardiologic (CD) and neurological (ND) diseases. One hundred patients per disease group (RMD, RD, CD and ND) were randomly selected from one healthcare system using the International Classification of Diseases version 10 (ICD10) codes for RMD including unexplained arthritis (M13.9, M13, M09), rheumatoid arthritis (M06), fibromyalgia syndrome (M79.7); RD including proteinuria (N06, N04.1), chronic kidney disease (N18); CD including hypertrophic cardiomyopathy (I42.2) and ND with unknown aetiology stroke (I64). Fifty-four case-finding algorithms were constructed using a combination of ICD10 codes and clinical FD phenotype algorithm (cardiac, neurological, renal criteria). Algorithms with the highest average positive predictive value (PPV) were validated and the patients were referred to genetic analysis of GLA mutations. From five hundred analysed patients 14 new Fabry disease cases were diagnosed. The highest PPV for Fabry disease had algorithm with ICD10 code of renal involvement in a combination of three different FD phenotype variations (42.8 % of validated cases). Algorithms included ICD10 codes of unknown aetiology hypertrophic cardiomyopathy, fibromyalgia syndrome, unknown aetiology stroke and unexplained arthritis confirmed diagnosis of FD in 21.4 %, 7.2 %, 21.4 % and 7.2 % patients of all validated cases, respectively. The risk of having Fabry disease depending on ICD10 code between the RMD, RD, CD and ND groups consisted 2.0 %, 6.0 %, 3.0 %, 3.0 %, respectively. Case-finding algorithms accurately identify patients with Fabry disease in administrative databases. These algorithms can be used to collect population-based cohorts, find new FD cases and facilitate future research in epidemiology. Fabry disease should be added in the list of differential diagnoses for unclear pathologies and those taking an atypical disease course.
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