Using a Multi-Institutional Pediatric Learning Health System to Identify Systemic Lupus Erythematosus and Lupus Nephritis Development and Validation of Computable Phenotypes

Background and objectives Performing adequately powered clinical trials in pediatric diseases, such as SLE, is challenging. Improved recruitment strategies are needed for identifying patients. Design, setting, participants, & measurements Electronic health record algorithms were developed and tested to identify children with SLE both with and without lupus nephritis. We used single-center electronic health record data to develop computable phenotypes composed of diagnosis, medication, procedure, and utilization codes. These were evaluated iteratively against a manually assembled database of patients with SLE. The highest-performing phenotypes were then evaluated across institutions in PEDSnet, a national health care systems network of .6.7 million children. Reviewers blinded to case status used standardized forms to review random samples of cases (n5350) and noncases (n5350). Results Final algorithms consisted of both utilization and diagnostic criteria. For both, utilization criteria included two or more in-person visits with nephrology or rheumatology and $60 days follow-up. SLE diagnostic criteria included absence of neonatal lupus, one or more hydroxychloroquine exposures, and either three or more qualifying diagnosis codes separated by $30 days or one or more diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 100% (95% confidence interval [95% CI], 99 to 100), specificity was 92% (95% CI, 88 to 94), positive predictive value was 91% (95% CI, 87 to 94), and negative predictive value was 100% (95% CI, 99 to 100). Lupus nephritis diagnostic criteria included either three or more qualifying lupus nephritis diagnosis codes (or SLE codes on the same day as glomerular/kidney codes) separated by $30 days or one or more SLE diagnosis codes and one or more kidney biopsy procedure codes. Sensitivity was 90% (95% CI, 85 to 94), specificity was 93% (95% CI, 89 to 97), positive predictive value was 94% (95% CI, 89 to 97), and negative predictive value was 90% (95% CI, 84 to 94). Algorithms identified 1508 children with SLE at PEDSnet institutions (537 with lupus nephritis), 809 of whom were seen in the past 12 months. Conclusions Electronic health record–based algorithms for SLE and lupus nephritis demonstrated excellent classification accuracy across PEDSnet institutions.