%0 Journal Article
%@ 2562-0959
%I JMIR Publications
%V 5
%N 4
%P e38783
%T Phenotype Algorithms to Identify Hidradenitis Suppurativa Using Real-World Data: Development and Validation Study
%A Hardin,Jill
%A Murray,Gayle
%A Swerdel,Joel
%+ Janssen Research and Development, 1125 Trenton-Harbourton Road, Titusville, NJ, 08560, United States, 1 650 619 8599, jhardi10@its.jnj.com
%K dermatology
%K hidradenitis suppurativa
%K medical dermatology
%K observational data
%K phenotype
%K inflammation
%K skin disease
%K epidemiology
%K algorithm
%D 2022
%7 30.11.2022
%9 Original Paper
%J JMIR Dermatol
%G English
%X 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. 
%M 37632892
%R 10.2196/38783
%U https://derma.jmir.org/2022/4/e38783
%U https://doi.org/10.2196/38783
%U http://www.ncbi.nlm.nih.gov/pubmed/37632892