Phenotype Algorithms to Identify Hidradenitis Suppurativa Using Real-World Data: Development and Validation Study

Background: Hidradenitis suppurativa (HS) is a potentially debilitating, chronic, recurring inflammatory disease. Observational databases provide opportunities to study the epidemiology of HS. Objective: This study’s objective was to develop phenotype algorithms for HS suitable for epidemiological s...

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Veröffentlicht in:JMIR dermatology Jg. 5; H. 4; S. e38783
Hauptverfasser: Hardin, Jill, Murray, Gayle, Swerdel, Joel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Toronto JMIR Publications 30.11.2022
Schlagworte:
Algorithms
Autoimmune diseases
Codes
Computer centers
Disease
Electronic health records
Genotype & phenotype
Insurance claims
Medicaid
Original Paper
Patients
United Kingdom > UK
United States > US
Japan
England
Australia
France
Germany
ISSN:2562-0959, 2562-0959
Online-Zugang:Volltext
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Zusammenfassung:Background: Hidradenitis suppurativa (HS) is a potentially debilitating, chronic, recurring inflammatory disease. Observational databases provide opportunities to study the epidemiology of HS. Objective: This study’s objective was to develop phenotype algorithms for HS suitable for epidemiological studies based on a network of observational databases. Methods: A data-driven approach was used to develop 4 HS algorithms. A literature search identified prior HS algorithms. Standardized databases from the Observational Medical Outcomes Partnership (n=9) were used to develop 2 incident and 2 prevalent HS phenotype algorithms. Two open-source diagnostic tools, CohortDiagnostics and PheValuator, were used to evaluate and generate phenotype performance metric estimates, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value. Results: We developed 2 prevalent and 2 incident HS algorithms. Validation showed that PPV estimates were highest (mean 86%) for the prevalent HS algorithm requiring at least two HS diagnosis codes. Sensitivity estimates were highest (mean 58%) for the prevalent HS algorithm requiring at least one HS code. Conclusions: This study illustrates the evaluation process and provides performance metrics for 2 incident and 2 prevalent HS algorithms across 9 observational databases. The use of a rigorous data-driven approach applied to a large number of databases provides confidence that the HS algorithms can correctly identify HS subjects.
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ISSN:2562-0959
2562-0959
DOI:10.2196/38783