Validity and Agreement between the 28-Joint Disease Activity Score Based on C-Reactive Protein and Erythrocyte Sedimentation Rate in Patients with Rheumatoid Arthritis.
Louise NielungRobin ChristensenBente Danneskiold-SamsøeHenning BliddalChristian Cato HolmKaren EllegaardHanne Slott JensenElse Marie BartelsPublished in: Arthritis (2015)
Objective. To validate the agreement between the 28-joint disease activity score based on erythrocyte sedimentation rate (DAS28-ESR) and the 28-joint disease activity score based on C-reactive protein (DAS28-CRP) in a group of Danish patients with rheumatoid arthritis (RA). Methods. Data from 109 Danish RA patients initiating biologic treatment were analysed at baseline and following one year of treatment. Participants were retrospectively enrolled from a previous cohort study and were considered eligible for this project if CRP and ESR were measured at baseline and at the follow-up visit. To assess the extent of agreement between the two DAS28 definitions, the "European League Against Rheumatism" (EULAR) response criteria based on each definition were calculated with cross-classification. Weighted Kappa (κ) coefficients were calculated, and Bland-Altman plots were used to illustrate degree of agreement between DAS28 definitions. Results. The 75 eligible patients were classified as EULAR good, moderate, and nonresponders with good agreement (61/75; 81%) between DAS28-CRP and DAS28-ESR (κ = 0.75 (95% CI: 0.63 to 0.88)). Conclusions. According to our findings, DAS28-CRP and DAS28-ESR are interchangeable when assessing RA patients and the two versions of DAS28 are comparable between studies.
Keyphrases
- disease activity
- rheumatoid arthritis
- systemic lupus erythematosus
- rheumatoid arthritis patients
- ankylosing spondylitis
- end stage renal disease
- juvenile idiopathic arthritis
- ejection fraction
- chronic kidney disease
- newly diagnosed
- prognostic factors
- peritoneal dialysis
- magnetic resonance
- machine learning
- magnetic resonance imaging
- patient reported outcomes
- deep learning
- replacement therapy