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Published on: November 1, 2015
Using a Multi-Institutional Pediatric Learning Health System to Identify Systemic Lupus Erythematosus and Lupus
Scott E Wenderfer1, Joyce C Chang2, Amy Goodwin Davies3
1Pediatric Nephrology, Baylor College of Medicine, Texas Children's Hospital, Houston, Texas.
Insights
Electronic health record algorithms accurately identified children with Systemic Lupus Erythematosus (SLE) and lupus nephritis. These tools improve patient identification for clinical trials, addressing a key challenge in pediatric rheumatology research.
Area of Science:
- Pediatric Rheumatology
- Clinical Informatics
- Health Outcomes Research
Background:
- Pediatric clinical trials, particularly for Systemic Lupus Erythematosus (SLE), face recruitment challenges.
- Identifying eligible patients efficiently is crucial for advancing research in childhood diseases.
Purpose of the Study:
- To develop and validate electronic health record (EHR)-based algorithms for identifying pediatric patients with SLE.
- To specifically identify children with SLE who also have lupus nephritis.
Main Methods:
- Developed computable phenotypes using diagnosis, medication, procedure, and utilization codes from single-center EHR data.
- Evaluated algorithm performance against a manually curated patient database.
- Validated the highest-performing algorithms across multiple institutions using the PEDSnet national network.
Main Results:
- The final algorithms demonstrated high classification accuracy for SLE and lupus nephritis.
- For SLE, sensitivity was 100% and specificity was 92%.
- For lupus nephritis, sensitivity was 90% and specificity was 93%.
Conclusions:
- EHR-based algorithms show excellent accuracy in identifying pediatric SLE and lupus nephritis patients.
- These validated algorithms can significantly aid in patient recruitment for clinical trials.
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 (n=350) and noncases (n=350).
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.
