Abstract
Rheumatoid Arthritis (RA) is a common chronic and progressive disease. Early recognition and treatment provides better health outcomes in terms of fewer flares and slower progression of the disease. Anti-cyclic citrullinated peptide (CCP) antibodies are highly specific for RA and can help doctors in the diagnosis of the disease and to make decisions about treatment. However, the diagnostic accuracy of the CCP assay used and the consequences of misclassifications have not been investigated. CCP-False Positives patients may be managed as RA patients, causing extra costs until a correct diagnosis is made. CCP-False Negative patients, but affected by RA, may suffer from unfavorable disease outcomes. The aim is to simulate the number of patients correctly diagnosed and cost-savings resulting from performing automated CCP2 and CCP3 assays from different manufacturer’s and using Spanish unit cost data. A 12-months Markov model simulated, from the hospital perspective, 1000 RA-suspected individuals assayed with four automated CCP2 assays (Thermo Fisher Scientific, Abbott and Roche) and two CCP3 assays (Inova). Sensitivities and specificities were derived from a recent meta-analysis1. Costs came from a published study and public Spanish tariff2-4. Uncertainty for prevalence and transition probabilities were addressed with sensitivity analysis. At a prevalence of 14% in secondary care, the Thermo Fisher Scientific CCP2 assay presented with the highest number of patients correctly diagnosed (929) while the Abbott CCP2 test presented with the lowest number correct diagnoses (890). Cost-savings per patient/year achieved using Thermo Fisher Scientific CCP2 test ranged from 11€ compared to Roche CCP2 assay to 31€ compared to Abbott Axsym CCP2 assay. The Thermo Fisher Scientific CCP2 assay demonstrated increased value from a patient's, health care provider's and payer's perspectives as a direct consequence of the higher number of correctly diagnosed patients in comparison to other automated CCP2 and CCP3 assays included in this analyses.
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