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OBJECTIVE: To identify characteristics that predict a valid rheumatoid arthritis (RA) or juvenile idiopathic arthritis (JIA) diagnosis among RA- and JIA-coded individuals in the General Practice Research Database (GPRD), and to assess limitations of this type of diagnostic validation. METHODS: Four RA and 2 JIA diagnostic groups were created with differing strengths of evidence of RA/JIA (Group 1 = strongest evidence), based on RA/JIA medical codes. Individuals were sampled from each group and clinical and prescription data were extracted from anonymized hospital/practice correspondence and electronic records. American College of Rheumatology and International League of Associations for Rheumatology diagnostic criteria were used to validate diagnoses. A data-derived diagnostic algorithm that maximized sensitivity and specificity was identified using logistic regression. RESULTS: Among 223 RA-coded individuals, the diagnostic algorithm classified individuals as having RA if they had an appropriate GPRD disease-modifying antirheumatic drug prescription or 3 other GPRD characteristics: >1 RA code during followup, RA diagnostic Group 1 or 2, and no later alternative diagnostic code. This algorithm had >80% sensitivity and specificity when applied to a test data set. Among 101 JIA-coded individuals, the strongest predictor of a valid diagnosis was a Group 1 diagnostic code (>90% sensitivity and specificity). CONCLUSION: Validity of an RA diagnosis among RA-coded GPRD individuals appears high for patients with specific characteristics. The findings are important for both interpreting results of published GPRD studies and identifying RA/JIA patients for future GPRD-based research. However, several limitations were identified, and further debate is needed on how best to validate chronic disease diagnoses in the GPRD.

Original publication




Journal article


Arthritis rheum

Publication Date





1314 - 1321


Adolescent, Adult, Aged, Arthritis, Juvenile, Databases, Factual, Diagnostic Errors, Family Practice, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Reproducibility of Results