OBJECTIVE: The decision to start disease-modifying antirheumatic drugs in patients with recent-onset undifferentiated arthritis (UA) is complicated by a varied natural disease course in which the disease in one-third of patients progresses to rheumatoid arthritis (RA), whereas 40-50% of patients experience spontaneous remission. Recently, a prediction rule was developed to estimate the chance of progression to RA in individual patients presenting with UA. This study investigates the accuracy of this prediction rule in independent cohorts of patients with UA. METHODS: In 3 cohorts of patients with recent-onset UA, from the UK, Germany, and The Netherlands, the prediction score and the corresponding chance of developing RA were calculated. These data were compared with the observed disease outcome after > or =1 year of followup. Positive predictive values (PPVs) and negative predictive values (NPVs) were calculated and the overall discriminative ability of the prediction rule was assessed using area under the receiver operating characteristic curves (AUCs). RESULTS: Since data on the severity of morning stiffness were not available in all validation cohorts, the prediction rule was rederived with the duration of morning stiffness as a substitute. The AUC for this rule was 0.88 (SEM 0.015). For each validation cohort, the AUC was 0.83 (SEM 0.041), 0.82 (SEM 0.037), and 0.95 (SEM 0.031) in the British, German, and Dutch cohorts, respectively. The NPV (for a prediction score < or =6) in these 3 cohorts was 83%, 83%, and 86%, respectively; the PPV (for a prediction score > or =8) was 100%, 93%, and 100%, respectively. CONCLUSION: The recently derived prediction rule, when applied to 3 independent cohorts of patients with UA, has an excellent discriminative ability for assessing the likelihood of progression to RA. Application of this rule will allow individualized treatment decision-making for patients with UA.
OBJECTIVE: The decision to start disease-modifying antirheumatic drugs in patients with recent-onset undifferentiated arthritis (UA) is complicated by a varied natural disease course in which the disease in one-third of patients progresses to rheumatoid arthritis (RA), whereas 40-50% of patients experience spontaneous remission. Recently, a prediction rule was developed to estimate the chance of progression to RA in individual patients presenting with UA. This study investigates the accuracy of this prediction rule in independent cohorts of patients with UA. METHODS: In 3 cohorts of patients with recent-onset UA, from the UK, Germany, and The Netherlands, the prediction score and the corresponding chance of developing RA were calculated. These data were compared with the observed disease outcome after > or =1 year of followup. Positive predictive values (PPVs) and negative predictive values (NPVs) were calculated and the overall discriminative ability of the prediction rule was assessed using area under the receiver operating characteristic curves (AUCs). RESULTS: Since data on the severity of morning stiffness were not available in all validation cohorts, the prediction rule was rederived with the duration of morning stiffness as a substitute. The AUC for this rule was 0.88 (SEM 0.015). For each validation cohort, the AUC was 0.83 (SEM 0.041), 0.82 (SEM 0.037), and 0.95 (SEM 0.031) in the British, German, and Dutch cohorts, respectively. The NPV (for a prediction score < or =6) in these 3 cohorts was 83%, 83%, and 86%, respectively; the PPV (for a prediction score > or =8) was 100%, 93%, and 100%, respectively. CONCLUSION: The recently derived prediction rule, when applied to 3 independent cohorts of patients with UA, has an excellent discriminative ability for assessing the likelihood of progression to RA. Application of this rule will allow individualized treatment decision-making for patients with UA.
Authors: Jeffrey A Sparks; Chia-Yen Chen; Xia Jiang; Johan Askling; Linda T Hiraki; Susan Malspeis; Lars Klareskog; Lars Alfredsson; Karen H Costenbader; Elizabeth W Karlson Journal: Ann Rheum Dis Date: 2014-03-31 Impact factor: 19.103
Authors: Katherine P Liao; Fina Kurreeman; Gang Li; Grant Duclos; Shawn Murphy; Raul Guzman; Tianxi Cai; Namrata Gupta; Vivian Gainer; Peter Schur; Jing Cui; Joshua C Denny; Peter Szolovits; Susanne Churchill; Isaac Kohane; Elizabeth W Karlson; Robert M Plenge Journal: Arthritis Rheum Date: 2013-03
Authors: Mohamed M Thabet; Thomas W J Huizinga; Désirée M van der Heijde; Annette H M van der Helm-van Mil Journal: Arthritis Res Ther Date: 2009-10-15 Impact factor: 5.156
Authors: Carole L Galligan; Janet C Siebert; Katherine A Siminovitch; Edward C Keystone; Vivian Bykerk; Omar D Perez; Eleanor N Fish Journal: PLoS One Date: 2009-08-20 Impact factor: 3.240