When validated, the algorithms had positive predictive values of 91%, 94% and 88%. The algorithm with the highest positive predictive value used three or more counts of the SLE ICD-9 code, positive antinuclear antibody (ANA) (≥1:40) lab results and use of both disease-modifying anti-rheumatic drugs and steroids, while excluding individuals with systemic sclerosis and dermatomyositis ICD-9 codes. This algorithm was applied to the entire sample, resulting in 1,098 SLE cases.
“Since one algorithm incorporates the ANA and medications with the SLE ICD-9 code, another uses only the ANA and the SLE ICD-9 code, and the third uses medications and the SLE ICD-9 code, investigators can select which algorithm is best suited to their EHR or administrative database,” write the authors.
These algorithms were designed at a single center and, therefore, the authors acknowledge that biases inherent to the institution may affect their portability. However, “prior studies have demonstrated significant portability of EHR algorithms,” they conclude. “We have future plans to validate our algorithms within other institutions”
Barnado A, Casey C, Carroll RJ, et al. Developing electronic health record algorithms that accurately identify patients with systemic lupus erythematosus. Arthritis Care Res (Hoboken). 2016 Jul 7. doi:10.1002/acr.22989. [Epub ahead of print]
