Validation of diagnostic codes for Charcot-Marie-Tooth disease in the Danish National Patient Registry

Signe Vaeth,1 Uffe Birk Jensen,1 Rikke Christensen,1 Henning Andersen2 1Department of Clinical Genetics, Aarhus University Hospital, Aarhus N, 2Department of Neurology, Aarhus University Hospital, Aarhus C, Denmark Purpose: To validate the diagnostic codes for Charcot–Marie–Tooth...

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Bibliographic Details
Main Authors: Vaeth S, Jensen UB, Christensen R, Andersen H
Format: Article
Language:English
Published: Dove Medical Press 2016-11-01
Series:Clinical Epidemiology
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Online Access:https://www.dovepress.com/validation-of-diagnostic-codes-for-charcot-marie-tooth-disease-in-the--peer-reviewed-article-CLEP
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Summary:Signe Vaeth,1 Uffe Birk Jensen,1 Rikke Christensen,1 Henning Andersen2 1Department of Clinical Genetics, Aarhus University Hospital, Aarhus N, 2Department of Neurology, Aarhus University Hospital, Aarhus C, Denmark Purpose: To validate the diagnostic codes for Charcot–Marie–Tooth disease (CMT) in the Danish National Patient Registry (DNPR) using positive predictive value (PPV) as a measure of validity. Patients and methods: We used the DNPR to identify all patients diagnosed with at least one primary CMT diagnosis at a specialized department in the Central Denmark Region during the period 1977–2012. From this population, we randomly selected 123 patients for the validation study. Medical files were reviewed and used as reference standard. We estimated the PPV of the CMT diagnoses and stratified the analysis according to age at diagnosis, gender, and calendar time. Results: In the DNPR, 275 patients were identified. We were able to retrieve 96 medical files from the random sample of 123 patients, and 85 CMT diagnoses were confirmed. The average age at diagnosis was 42.5 years, and 34% were female. The PPV was 88.5% (95% confidence interval: 80.4–94.1). Conclusion: The CMT diagnoses in the DNPR have high validity. The DNPR can be used as a data source for epidemiologic research on CMT. Keywords: registries, positive predictive value, International Classification of Disease codes, epidemiology
ISSN:1179-1349