Development and Evaluation of a Patient Self-Report Case-Finding Method for Rheumatoid Arthritis

Abstract Objective To describe the development and evaluation of a patient self-report case-finding method for rheumatoid arthritis (RA) not dependent on direct contact with the treating physicians. Methods The American College of Rheumatology criteria for RA diagnosis were adapted for patient self-report using a questionnaire, and alternative scoring algorithms were evaluated to balance case-finding sensitivity and specificity. Positive rheumatoid factor tests were used to identify 1053 individuals in 2 large healthcare organizations; 440 agreed to receive study materials. Case-finding results were validated by medical record review (MRR) for a random sample of 90 patients. Three scoring algorithms were compared with MRR for likelihood of RA diagnosis. Cases not classifiable by algorithm were flagged and reviewed by 2 expert physicians for likelihood of RA diagnosis. Results Pilot testing demonstrated that patients comprehended the questionnaire and were willing to answer the questions. Completed questionnaires were returned by 265 (60%) of the 440 patients contacted. Following expert physician review of 16 flagged cases in the 90-patient MRR subsample, the most accurate scoring algorithm demonstrated 80% sensitivity, 67% specificity, 74% accuracy, and 77% positive predictive value for detecting early RA. Conclusion The case-finding method represents a promising tool for identifying RA patients, with potential application in research and quality-assurance activities. Relevance This case-finding method should be useful in research and quality-assurance efforts requiring identification of RA patients treated by all types of providers in healthcare organizations in which centralized laboratory data are available.