ArticleJournal of cutaneous medicine and surgery
Development and Validation of Algorithms to Identify Individuals With Cutaneous Lupus From Healthcare Databases.
Article in Journal of cutaneous medicine and surgery. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Prevalence of Systemic Lupus Erythematosus in Australia, 2010-2022: A Population-Based Study Using Linked National Administrative Health Data.Arthritis care & research · 2026Article
- Autoimmune Disease Risk With GLP-1RA, DPP-4i, and SGLT2i Treatment in Patients With Diabetes.ACR open rheumatology · 2026Article
- What's New in Cutaneous Lupus Erythematosus: Guidelines, Biologics, and Beyond.The Journal of clinical and aesthetic dermatology · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundThere are no validated methods to identify individuals with cutaneous lupus erythematosus (CLE) from large databases including claims data and electronic health records, severely limiting the study of the epidemiology of this disease.
objectivesTo develop and validate accurate algorithms to identify individuals with CLE from healthcare records.
methodsTwelve case-finding algorithms were developed based on the International Classification of Diseases (ICD)-10 diagnosis codes, provider specialty, and medication prescription data. To validate performance, algorithms were applied to a test cohort of 300 individuals drawn from a clinical data repository of a multi-institutional healthcare network in Boston, MA. Documentation of a CLE diagnosis by a dermatologist or rheumatologist determined from chart review or supportive biopsy findings was used as the case definition standard. Performance was evaluated based on calculated positive predictive values (PPVs), specificities, and sensitivities of each algorithm.
resultsPPVs ranged from 58.0% to 92.9%. The use of a single diagnosis code for CLE from any provider had poor PPV. The algorithm with the highest PPV (89.0%) while maintaining sensitivity required at least 1 ICD-10 CLE diagnosis code recorded by a dermatologist.
conclusionsUtilizing CLE diagnosis codes and dermatology as the coding provider specialty is a valid method for identifying CLE patients from electronic health records.
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