Abstract
BACKGROUND: Gaucher disease (GD) is a rare inherited multiorgan disorder, yet a diagnosis can be significantly delayed due to a broad spectrum of symptoms and lack of disease awareness. Recently, the prototype of a GD point-scoring system (PSS) was established by the Gaucher Earlier Diagnosis Consensus (GED-C) initiative, and more recently, validated in Gaucher patients in UK. In our study, the original GED-C PSS was tested in Finnish GD patients. Furthermore, the feasibility of point scoring large electronic health record (EHR) data set by data mining to identify potential undiagnosed GD cases was evaluated. METHODS: This biobank study was conducted in collaboration with two Finnish biobanks. Five previously diagnosed Finnish GD patients and ~ 170,000 adult biobank subjects were included in the study. The original PSS was locally adjusted due to data availability issues and applied to the Finnish EHR data representing special health care recordings. RESULTS: All GD patients had high levels of the biomarker lyso-Gb1 and deleterious GBA mutations. One patient was a compound heterozygote with a novel variant, potentially pathogenic mutation. Finnish EHR data allowed the retrospective assessment of 27-30 of the 32 original GED-C signs/co-variables. Total point scores of GD patients were high but variable, 6-18.5 points per patient (based on the available data on 28-29 signs/co-variables per patient). All GD patients had been recorded with anaemia while only three patients had a record of splenomegaly. 0.72% of biobank subjects were assigned at least 6 points but none of these potential "GD suspects" had a point score as high as 18.5. Splenomegaly had been recorded for 0.25% of biobank subjects and was associated with variable point score distribution and co-occurring ICD-10 diagnoses. DISCUSSION: This study provides an indicative GED-C PSS score range for confirmed GD patients, also representing potential mild cases, and demonstrates the feasibility of scoring Finnish EHR data by data mining in order to screen for undiagnosed GD patients. Further prioritisation of the "GD suspects" with more developed algorithms and data-mining approaches is needed. FUNDING: This study was funded by Shire (now part of Takeda).