Systematic review and meta-analysis of diagnostic algorithms for rheumatoid arthritis and juvenile idiopathic arthritis

OBJECTIVE: This systematic review aimed to assess the diagnostic accuracy of algorithms used to identify rheumatoid arthritis and juvenile idiopathic arthritis in electronic health records. METHODS: We searched Medline, Embase, and Cochrane Central Register for Controlled Trials databases and included studies that validated case definitions against a reference standard, such as rheumatologist-confirmed diagnosis or American College of Rheumatology/EULAR classification criteria. Title and abstract screening, full-text review, data extraction, and quality assessment were all completed in duplicate. Results were synthesized 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 International Classification of Diseases (ICD) codes to combinations including disease-modifying antirheumatic drugs (DMARDs), hospitalization records, and specialist diagnosis. Algorithms combining ICD codes with DMARD prescriptions (pooled sensitivity 0.79 [95% confidence interval (CI) 0.61-0.90], specificity 0.96 [95% CI 0.72-1.00], positive predictive value [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 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, prioritizing 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 health care systems.