Accuracy of diagnostic codes and algorithms used to identify rheumatoid arthritis and juvenile idiopathic arthritis in administrative claims and electronic health records: systematic review and meta-analysis.

Abstract

Objective: This systematic review aimed to assess the diagnostic accuracy of algorithms used to identify rheumatoid arthritis (RA) and juvenile idiopathic arthritis (JIA) in electronic health records (EHRs).

Methods: We searched MEDLINE, Embase, and CENTRAL databases and included studies that validated case definitions against a reference standard such as rheumatologist-confirmed diagnosis or ACR/EULAR classification criteria. Title/abstract screening, full-text review, data extraction and quality assessment were all completed in duplicate. Results were synthesised narratively and using a bivariate random-effects meta-analysis of sensitivity and specificity.

Results: A total of 35 studies were included. Algorithms varied widely in complexity, ranging from single ICD codes to combinations including disease-modifying antirheumatic drugs (DMARDs), hospitalisation records, and specialist diagnosis. Algorithms combining ICD codes with DMARD prescriptions (pooled sensitivity= 0.79 95% CI 0.61-0.90, specificity= 0.96 95% CI 0.72-1.00, PPV= 0.78 95% CI 0.63-0.88) or requiring an ICD code assigned by a rheumatologist (pooled sensitivity= 0.91 95% CI 0.70-0.98, specificity= 0.94 95% CI 0.49-1.00, PPV= 0.70 95% CI 0.64-0.75) showed the highest accuracy, with balanced sensitivity, specificity, and positive predictive value (PPV). Less restrictive algorithms demonstrated high sensitivity but lower PPV. Substantial heterogeneity was observed across studies, likely due to differences in algorithm structure, data sources, and validation methods. Despite this variability, we used conceptually coherent categories to allow for meaningful synthesis, prioritising clinical interpretability.

Conclusions: These findings support the use of more specific algorithms when diagnostic certainty is essential and highlight the need for further validation of high-performing algorithms across diverse healthcare systems.