Unveiling the untreated: development of a database algorithm to identify potential Fabry disease patients in Germany - Hilz, Max-Josef; Lyn, Nicole; Marczykowski, Felix; Werner, Barbara; Pignot, Marc; Ponce, Elvira; Bender, Joseph; Edigkaufer, Michael; DasMahapatra, Pronabesh (Orphanet Journal of Rare Diseases, 2024)
Background: Fabry disease (FD), an X-linked lysosomal storage disorder, is caused by mutations in the gene encoding α-galactosidase A, resulting in lysosomal accumulation of globotriaosylceramide and other glycosphingolipids. Early detection of FD is challenging, accounting for delayed diagnosis and treatment initiation. This study aimed to develop an algorithm using a logistic regression model to facilitate early identification of patients based on ICD-10-GM coding using a German Sickness Fund Database. Methods: The logistic regression model was fitted on a binary outcome variable based on either a treated FD cohort or a control cohort (without FD). Comorbidities specific to the involved organs were used as covariates to identify potential FD patients with ICD-10-GM E75.2 diagnosis but without any FD-specific medication. Specificity and sensitivity of the model were optimized to determine a likely threshold. The cut-point with the largest values for the Youden index and concordance probability method and the lowest value for closest to (0,1) was identified as 0.08 for each respective value. The sensitivity and specificity for this cut-point were 80.4% and 79.8%, respectively. Additionally, a sensitivity analysis of the potential FD patients with at least two codes of E75.2 diagnoses was performed. Results: A total of 284 patients were identified in the potential FD cohort using the logistic regression model. Most potential FD patients were < 30 years old and female. The identification and incidence rates of FD in the potential FD cohort were markedly higher than those of the treated FD cohort. Conclusions: This model serves as a tool to identify potential FD patients using German insurance claims data.
CRIS-Team Scopus Importer:2024-07-19
Keywords: Algorithm; BKK German Sickness Fund Database; Early diagnosis; Fabry disease; Logistic regression modelling