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The bm12 Inducible Model of Systemic Lupus Erythematosus SLE in C57BL/6 Mice
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Using a data-driven approach for the development and evaluation of phenotype algorithms for systemic lupus
Joel N Swerdel1,2, Darmendra Ramcharran1, Jill Hardin1,2
1Janssen Research and Development Epidemiology, Titusville, New Jersey, United States of America.
Plos One
|February 16, 2023
Summary
Researchers developed four phenotype algorithms for Systemic Lupus Erythematosus (SLE) using a data-driven approach. These validated algorithms improve accurate subject identification in observational studies for better epidemiological research.
Area of Science:
- Health Informatics
- Epidemiology
- Autoimmune Diseases
Background:
- Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease with unknown etiology.
- Accurate identification of SLE patients in observational databases is crucial for epidemiological research.
Purpose of the Study:
- To develop and validate phenotype algorithms for identifying Systemic Lupus Erythematosus (SLE) in observational databases.
- To create algorithms suitable for both prevalent and incident SLE cases.
Main Methods:
- Utilized a literature search to identify existing SLE algorithms.
- Employed Observational Health Data Sciences and Informatics (OHDSI) tools for algorithm refinement and validation.
- Developed algorithms to correct for potential index date misclassification and improve code discovery.
Main Results:
- Four algorithms were developed: two for prevalent SLE and two for incident SLE (each with sensitive and specific versions).
- The prevalent, specific algorithm achieved the highest positive predictive value (89%).
- The prevalent, sensitive algorithm demonstrated the highest sensitivity (77%).
Conclusions:
- Data-driven phenotype algorithms for SLE were successfully developed.
- Validated algorithms enhance confidence in subject selection for observational studies.
- These algorithms facilitate quantitative bias analysis in SLE research.
