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Unveiling the untreated: development of a database algorithm to identify potential Fabry disease patients in Germany.
Hilz, Max J; Lyn, Nicole; Marczykowski, Felix; Werner, Barbara; Pignot, Marc; Ponce, Elvira; Bender, Joseph; Edigkaufer, Michael; DasMahapatra, Pronabesh.
Afiliación
  • Hilz MJ; University of Erlangen-Nuremberg, Erlangen, Germany.
  • Lyn N; Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Marczykowski F; Sanofi, Cambridge, MA, USA. Nicole.Lyn@sanofi.com.
  • Werner B; Oracle Life Sciences, Munich, Germany.
  • Pignot M; Team Gesundheit GmbH, Essen, Germany.
  • Ponce E; ZEG Berlin - Center for Epidemiology and Health Research, Berlin, Germany.
  • Bender J; Sanofi, Cambridge, MA, USA.
  • Edigkaufer M; Sanofi, Cambridge, MA, USA.
  • DasMahapatra P; Sanofi, Frankfurt, Germany.
Orphanet J Rare Dis ; 19(1): 259, 2024 Jul 09.
Article en En | MEDLINE | ID: mdl-38982319
ABSTRACT

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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Enfermedad de Fabry Límite: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged País/Región como asunto: Europa Idioma: En Revista: Orphanet J Rare Dis Asunto de la revista: MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Enfermedad de Fabry Límite: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged País/Región como asunto: Europa Idioma: En Revista: Orphanet J Rare Dis Asunto de la revista: MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Reino Unido