BMJ Open, 2016. 19(7): p. A578.
Jaffe DH, Flaks-Manov N, Benis A, Gabay H, DiBonaventura M, Rosenbaum H, Joseph A, Leventer-Roberts M.
Objectives: To characterize a population-based cohort of patients with Gaucher disease (GD) relative to the general population and assesses socio-demographic and clinical differences by disease severity as defined by use of enzyme replacement therapy (ERT).
Methods: A cross-sectional study was conducted using the Clalit Health Services’ (Clalit) electronic health record (EHR) database to identify the prevalence of GD cases as of 30/6/2014. Cases were identified using a combination of International Classification of Diseases codes, 9th revision and associated free text, internally and externally validated registries, laboratory tests, and medication use. Demographics and clinical characteristics are presented for all GD patients by ERT initiation (+/-) and for the general Clalit population. Differences by ERT initiation were tested using chi-square tests.
Results: We identified 500 GD patients in the Clalit database as of the index date. The prevalence of GD ranged from 5.3 per 100,000 for members aged ≤34 years old to 20.2 per 100,000 in ≥35-year-olds. The majority of GD patients were ≥18 years old (91.6%), female (54.0%), and of higher socioeconomic status relative to the general Clalit population (GD=34.8% vs. Clalit=19.0%). The prevalence of overweight/obesity was 51.0% among GD patients and 46.5% among all Clalit members. Only 35.0% of GD patients had a Charlson Comorbidity Index (CCI)<1 compared with 68.2% in the general population. Among GD patients, no differences were observed by ERT initiation status for socio-demographic or clinical characteristics with the exception of CCI<1 (ERT+=19.9% vs. ERT-=45.6%; p<0.001). Prior to ERT treatment, anemia, thrombocytopenia and hepatomegaly were more common in ERT+ than ERT- patients (p<0.02).
Conclusions: This is the first study to have detailed socio-economic and clinical data on a large GD cohort using a comprehensive real-time EHR database. Establishing such a cohort is critical to understanding disease burden and outcomes in a real-world population.