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Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

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Identification of Adult Patients With Dermatomyositis Using Real-World Data Sources.

Benjamin Martin¹, Will Kelly¹, Hannah Morgan-Cooper²

¹Johns Hopkins University School of Medicine, Baltimore, Maryland.

Arthritis Care & Research

|August 12, 2025

Summary

This summary is machine-generated.

Developing accurate algorithms for identifying dermatomyositis (DM) cases across multiple health databases improves rare disease research. These validated methods enhance data generalizability and reproducibility for future studies.

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Published on: October 3, 2019

Area of Science:

Rheumatology
Epidemiology
Health Informatics

Background:

Rare diseases like dermatomyositis (DM) present research challenges due to small, single-center cohorts.
Limited generalizability hinders understanding and treatment development for DM.

Purpose of the Study:

To develop and evaluate case identification algorithms for DM.
To enable coordinated analysis of DM across diverse electronic health record (EHR) and claims databases.

Main Methods:

Eight algorithms were developed to identify adult DM patients in eleven independent databases (over 800 million patients) using the OMOP Common Data Model.
Performance was assessed via manual chart review and OHDSI tools (CohortDiagnostics, PheValuator) for sensitivity and positive predictive value (PPV).

Main Results:

Significant performance variability (sensitivity, PPV >30%) was observed across databases for the eight algorithms.
One incidence algorithm (42% sensitivity, 83% PPV) and one prevalence algorithm (49% sensitivity, 84% PPV) showed good performance.
CohortDiagnostics provided incidence rates by age and sex, consistent with prior data.

Conclusions:

Validated DM case identification algorithms were developed, demonstrating accuracy across diverse real-world data sources.
This approach facilitates reproducible and generalizable research for rare rheumatic diseases.
The methodology can be adapted for identifying other rheumatic conditions.