{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:55:49Z","timestamp":1782842149638,"version":"3.54.5"},"reference-count":44,"publisher":"JMIR Publications Inc.","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Dermatol"],"abstract":"\n Background<\/jats:title>\n Hidradenitis suppurativa (HS) is a potentially debilitating, chronic, recurring inflammatory disease. Observational databases provide opportunities to study the epidemiology of HS.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study\u2019s objective was to develop phenotype algorithms for HS suitable for epidemiological studies based on a network of observational databases.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/38783","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T12:17:26Z","timestamp":1668082646000},"page":"e38783","source":"Crossref","is-referenced-by-count":6,"title":["Phenotype Algorithms to Identify Hidradenitis Suppurativa Using Real-World Data: Development and Validation 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