AI Summary & Key Takeaways
AI InsightMore-over, we developed new cutoff values to minimize the discrepant activity state categorization with different indices.
📌 3 Key Takeaways
Abstract
Objective: Disease activity indices for rheumatoid arthritis (RA) are important in clinical practice and research. Although they are closely correlated, they are not in good agreement. We derived formulae to convert values from one of the four 28-joint count indices (disease activity score using erythrocyte sedimentation rate [DAS28-ESR], disease activity score using C-reactive protein [DAS28-CRP], clinical disease activity index [CDAI], and simple disease activity index [SDAI]) to any of the others.Methods: We obtained data from 175 patients from our RA registry with concurrent CRP and ESR and established the nature of relationships between the indices using these data. Subsequently, we developed empiric conversion formulae. Furthermore, we developed new cutoff values for classifying disease activity to minimize the disparity among indices, using an iterative method.Results: The relationships between DAS28-ESR and DAS28-CRP and between SDAI and CDAI were approximately linear; the others were quadratic. Quadratic equations approximated the relationship between DAS, SDAI, and CDAI, whereas natural logarithms function approximated the relationship between DAS28-ESR and DAS28-CRP. Patients are frequently categorized into inconsistent disease activity states with any two indices, with the disparity ranging from 9.7% to 40.6%. The new cutoff val-ues were developed to minimize the discrepant activity state categorization, reducing the disparity range to 6.3%-32.6%. Conclusion: We derived empiric formulae that connect DAS28-ESR, DAS28-CRP, SDAI, and CDAI. More-over, we developed new cutoff values to minimize the discrepant activity state categorization with different indices.