Antoine Vanier1,2, Josef S Smolen3, Cornelia F Allaart4, Ronald Van Vollenhoven5, Patrick Verschueren6, Nathan Vastesaeger7, Saedis Saevarsdottir5, Karen Visser4, Daniel Aletaha3, Bernard Combe8, Bruno Fautrel9. 1. Department of Biostatistics Public Health and Medical Informatics, Sorbonne University, APHP, University Hospitals Pitié-Salpêtrière Charles-Foix, Paris. 2. University Bretagne-Loire, University of Nantes, University of Tours, Inserm UMR U1246 SPHERE 'Methods in patient-centered outcomes and health research', Nantes. 3. Division of Rheumatology and Department of Medicine 3, University of Vienna, Vienna, Austria. 4. Department of Rheumatology, Leiden University Medical Centre, Leiden, The Netherlands. 5. Rheumatology Unit, Department of Medicine, Karolinska Institute and Karolinska University Hospital, Solna, Stockholm, Sweden. 6. KU Leuven, Skeletal Biology and Engineering Research Center, Leuven. 7. MSD, Brussels, Belgium. 8. Department of Rheumatology, Montpellier 1 University, Montpellier University Hospital. 9. Department of Rheumatology, Sorbonne University, GRC-08 (EEMOIS), APHP, University Hospitals Pitié-Salpêtrière Charles-Foix, Paris, France.
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.
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.
Authors: Edit Végh; János Gaál; Pál Géher; Edina Gömöri; Attila Kovács; László Kovács; Katalin Nagy; Edit Feketéné Posta; László Tamási; Edit Tóth; Eszter Varga; Andrea Domján; Zoltán Szekanecz; Gabriella Szűcs Journal: BMC Musculoskelet Disord Date: 2021-04-02 Impact factor: 2.362