How accurate are diagnoses for rheumatoid arthritis and juvenile idiopathic arthritis in the general practice research database?

Arthritis Rheum. 2008 Sep 15;59(9):1314-21. doi: 10.1002/art.24015.


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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Arthritis, Juvenile / diagnosis*
  • Databases, Factual / statistics & numerical data*
  • Diagnostic Errors / statistics & numerical data*
  • Family Practice / statistics & numerical data*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • Reproducibility of Results