Validation of rheumatoid arthritis diagnoses in health care utilization data

INTRODUCTION: Health care utilization databases have been increasingly used for studies of rheumatoid arthritis (RA). However, the accuracy of RA diagnoses in these data has been inconsistent. METHODS: Using medical records and a standardized abstraction form, we examined the positive predictive value (PPV) of several algorithms to define RA diagnosis using claims data: A) at least two visits coded for RA (ICD-9, 714); B) at least three visits coded for RA; and C) at least two visits to a rheumatologist for RA. We also calculated the PPVs for the subgroups identified by these algorithms combined with pharmacy claims data for at least one disease-modifying anti-rheumatic drug (DMARD) prescription. RESULTS: We invited 9,482 Medicare beneficiaries with pharmacy benefits in Pennsylvania to participate; 2% responded and consented for review of their medical records. There was no difference in characteristics between respondents and non-respondents. Using 'RA diagnosis per rheumatologists' as the gold standard, the PPVs were 55.7% for at least two claims coded for RA, 65.5% for at least three claims for RA, and 66.7% for at least two rheumatology claims for RA. The PPVs of these algorithms in patients with at least one DMARD prescription increased to 86.2%-88.9%. When fulfillment of 4 or more of the ACR RA criteria was used as the gold standard, the PPVs of the algorithms combined with at least one DMARD prescriptions were 55.6%-60.7%. CONCLUSIONS: To accurately identify RA patients in health care utilization databases, algorithms that include both diagnosis codes and DMARD prescriptions are recommended.