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Related Experiment Video

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Author Spotlight: Exploring ShiDuGao's Multi-Target Approach in Anus Eczema Treatment
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Phenotype Algorithms to Identify Hidradenitis Suppurativa Using Real-World Data: Development and Validation Study.

Jill Hardin1,2, Gayle Murray1, Joel Swerdel1,2

  • 1Janssen Research and Development, Titusville, NJ, United States.

JMIR Dermatology
|August 26, 2023
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Summary

New algorithms accurately identify hidradenitis suppurativa (HS) in large observational databases. This research provides validated tools for studying HS epidemiology, improving disease research and patient identification.

Keywords:
algorithmdermatologyepidemiologyhidradenitis suppurativainflammationmedical dermatologyobservational dataphenotypeskin disease

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Area of Science:

  • Medical informatics
  • Epidemiology
  • Health outcomes research

Background:

  • Hidradenitis suppurativa (HS) is a chronic, inflammatory skin condition with significant impact.
  • Observational databases are crucial for understanding HS epidemiology.

Purpose of the Study:

  • To develop and validate phenotype algorithms for identifying HS in observational databases.
  • To support large-scale epidemiological studies of hidradenitis suppurativa.

Main Methods:

  • A data-driven approach was used to create four HS algorithms (2 incident, 2 prevalent).
  • Algorithms were developed using 9 Observational Medical Outcomes Partnership databases.
  • CohortDiagnostics and PheValuator tools assessed algorithm performance metrics.

Main Results:

  • Two prevalent and two incident HS algorithms were successfully developed.
  • The prevalent algorithm requiring two HS diagnosis codes showed the highest positive predictive value (mean 86%).
  • The prevalent algorithm using at least one HS code demonstrated the highest sensitivity (mean 58%).

Conclusions:

  • The study provides validated HS phenotype algorithms for epidemiological research.
  • Performance metrics for incident and prevalent HS algorithms are available across multiple databases.
  • A rigorous, data-driven methodology ensures reliable identification of HS patients.