We used Vanderbilt’s Synthetic Derivative (SD), a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least one SLE ICD-9 code (710.0) yielding 5959 individuals. To create a training set, 200 were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist. Positive predictive values (PPVs) and sensitivity were calculated for combinations of code counts of the SLE ICD-9 code, a positive anti-nuclear antibody (ANA), ever use of commonly prescribed medications, and a keyword of “lupus” in the problem list. The algorithms with the highest PPV were each internally validated using a random set of 100 individuals from the remaining 5759 subjects.
Systemic lupus erythematosus (SLE)
Phenotype ID:
1058
Status:
List on the Collaboration Phenotypes List
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Files:
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Date Created:
Thursday, July 7, 2016
URLs:
https://www.healio.com/rheumatology/lupus/news/online/%7B71dbc80e-7b28-44e7-ad46-8002356746da%7D/ehr-algorithms-help-researchers-identify-patients-with-lupus
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Data Model:
Other
April Barnado, Carolyn Casey, Robert J. Carroll, Lee Wheless, Joshua C. Denny, Leslie J. Crofford. Vanderbilt University Medical Center. Systemic lupus erythematosus (SLE). PheKB; 2016 Available from: https://phekb.org/phenotype/1058