ArticleJMIR dermatology2022
Phenotype Algorithms to Identify Hidradenitis Suppurativa Using Real-World Data: Development and Validation Study.
Article in JMIR dermatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Risk of Serious Infection With Adalimumab in Hidradenitis Suppurativa Compared With Psoriasis.JAMA dermatology · 2025Article
- Machine Learning for Early Detection of Hidradenitis Suppurativa: A Feasibility Study Using Medical Insurance Claims Data.JID innovations : skin science from molecules to population health · 2025Article
- Cross Biobank Comparison of Phenomic Profiles.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Characteristics and treatment pathways in pediatric and adult hidradenitis suppurativa: An examination using real world data.JAAD international · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHidradenitis suppurativa (HS) is a potentially debilitating, chronic, recurring inflammatory disease. Observational databases provide opportunities to study the epidemiology of HS.
objectiveThis study's objective was to develop phenotype algorithms for HS suitable for epidemiological studies based on a network of observational databases.
methodsA 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.
resultsWe 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.
conclusionsThis 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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