Accepted for/Published in: JMIR Dermatology
Date Submitted: Apr 15, 2022
Date Accepted: Nov 10, 2022
Date Submitted to PubMed: Aug 26, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Development of Phenotype Algorithms to Identify Hidradenitis Suppurativa Using Real World Data
ABSTRACT
Background:
Hidradenitis Suppurativa (HS) is a chronic, potentially debilitating, 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 using a network of observational databases.
Methods:
A data driven approach was used to develop four HS algorithms. A literature search identified prior HS algorithms. Nine Observational Medical Outcomes Partnership (OMOP) standardized databases were used to develop two incident and two prevalent HS phenotype algorithms. Two open-source diagnostic tools, CohortDiagnostics and PheValuator, were used to evaluate and generate phenotype performance metric estimates (sensitivity, specificity, positive predictive value (PPV), and negative predictive value).
Results:
Two prevalent and two incident HS algorithms were developed. Validation showed PPV estimates were highest in the prevalent algorithm requiring at least two HS diagnostic codes (mean 86%). Sensitivity estimates were highest in the prevalent algorithm requiring at least one HS code (mean 58%).
Conclusions:
This study illustrates the evaluation process and provides performance metrics for two incident and two prevalent HS algorithms across nine observational databases. The use of the rigorous data driven approach applied to a large number of databases provides confidence that the HS algorithms are correctly identifying HS subjects. Clinical Trial: NA
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