An updated matrix to predict rapid radiographic progression of early rheumatoid arthritis patients: pooled analyses from several databases. (13th November 2019)

• Record Type: Journal Article
• Authors: Vanier, Antoine; Smolen, Josef S; Allaart, Cornelia F; Van Vollenhoven, Ronald; Verschueren, Patrick; Vastesaeger, Nathan; Saevarsdottir, Saedis; Visser, Karen; Aletaha, Daniel; Combe, Bernard; Fautrel, Bruno
• Is Part Of: Rheumatology. Volume 59:Number 8(2020)
• Page Start: 1842
• Page End: 1852
• Publication Date: 2019-11-13
• Subjects: early rheumatoid arthritis -- rapid radiographic progression -- risk prediction -- prognosis factors; Rheumatism -- Periodicals; Rheumatology -- Periodicals; 616.723005
• DOI: 10.1093/rheumatology/kez542
• Languages: English
• ISSNs: 1462-0324

Abstract

Abstract: Objective: In early RA, some patients exhibit rapid radiographic progression (RRP) after one year, associated with poor functional prognosis. Matrices predicting this risk have been proposed, lacking precision or inadequately calibrated. We developed a matrix to predict RRP with high precision and adequate calibration. Methods: Post-hoc analysis by pooling individual data from cohorts (ESPOIR and Leuven cohorts) and clinical trials (ASPIRE, BeSt and SWEFOT trials). Adult DMARD-naïve patients with active early RA for which the first therapeutic strategy after inclusion was to prescribe methotrexate or leflunomide were included. A logistic regression model to predict RRP was built. The best model was selected by 10-fold stratified cross-validation by maximizing the Area Under the Curve. Calibration and discriminatory power of the model were checked. The probabilities of RRP for each combination of levels of baseline characteristics were estimated. Results: 1306 patients were pooled. 20.6% exhibited RRP. Four predictors were retained: rheumatoid factor positivity, presence of at least one RA erosion on X-rays, CRP > 30mg/l, number of swollen joints. The matrix estimates RRP probability for 36 combinations of level of baseline characteristics with a greatly enhanced precision compared with previously published matrices (95% CI: from ± 0.02 minimum to ± 0.08 maximum) and model calibration is excellent ( P = 0.79). Conclusion: A matrix proposing RRP probability with high precision and excellent calibration in early RA was built. Although the matrix has moderate sensitivity and specificity, it is easily usable and may help physicians and patients to make treatment decisions in daily clinical practice.