OBJECTIVE: In patients with undifferentiated arthritis (UA), methotrexate is effective for inhibiting symptoms, structural damage, and progression to rheumatoid arthritis (RA). However, 40-50% of patients with UA experience spontaneous remission. Thus, adequate decision-making regarding treatment of patients with early UA requires identification of those patients in whom RA will develop. METHODS: A prediction rule was developed using data from the Leiden Early Arthritis Clinic, an inception cohort of patients with recent-onset arthritis (n = 1,700). The patients who presented with UA were selected (n = 570), and progression to RA or another diagnosis in this group was monitored for 1 year of followup. The clinical characteristics with independent predictive value for the development of RA were selected using logistic regression analysis. The diagnostic performance of the prediction rule was evaluated using the area under the curve (AUC). Cross-validation controlled for overfitting of the data (internal validation). An independent cohort of patients with UA was used for external validation. RESULTS: The prediction rule consisted of 9 clinical variables: sex, age, localization of symptoms, morning stiffness, the tender joint count, the swollen joint count, the C-reactive protein level, rheumatoid factor positivity, and the presence of anti-cyclic citrullinated peptide antibodies. Each prediction score varied from 0 to 14 and corresponded to the percent chance of RA developing. For several cutoff values, the positive and negative predictive values were determined. The AUC values for the prediction rule, the prediction model after cross-validation, and the external validation cohort were 0.89, 0.87, and 0.97, respectively. CONCLUSION: In patients who present with UA, the risk of developing RA can be predicted, thereby allowing individualized decisions regarding the initiation of treatment with disease-modifying antirheumatic drugs in such patients.
OBJECTIVE: In patients with undifferentiated arthritis (UA), methotrexate is effective for inhibiting symptoms, structural damage, and progression to rheumatoid arthritis (RA). However, 40-50% of patients with UA experience spontaneous remission. Thus, adequate decision-making regarding treatment of patients with early UA requires identification of those patients in whom RA will develop. METHODS: A prediction rule was developed using data from the Leiden Early Arthritis Clinic, an inception cohort of patients with recent-onset arthritis (n = 1,700). The patients who presented with UA were selected (n = 570), and progression to RA or another diagnosis in this group was monitored for 1 year of followup. The clinical characteristics with independent predictive value for the development of RA were selected using logistic regression analysis. The diagnostic performance of the prediction rule was evaluated using the area under the curve (AUC). Cross-validation controlled for overfitting of the data (internal validation). An independent cohort of patients with UA was used for external validation. RESULTS: The prediction rule consisted of 9 clinical variables: sex, age, localization of symptoms, morning stiffness, the tender joint count, the swollen joint count, the C-reactive protein level, rheumatoid factor positivity, and the presence of anti-cyclic citrullinated peptide antibodies. Each prediction score varied from 0 to 14 and corresponded to the percent chance of RA developing. For several cutoff values, the positive and negative predictive values were determined. The AUC values for the prediction rule, the prediction model after cross-validation, and the external validation cohort were 0.89, 0.87, and 0.97, respectively. CONCLUSION: In patients who present with UA, the risk of developing RA can be predicted, thereby allowing individualized decisions regarding the initiation of treatment with disease-modifying antirheumatic drugs in such patients.
Authors: Jing Shi; Rachel Knevel; Parawee Suwannalai; Michael P van der Linden; George M C Janssen; Peter A van Veelen; Nivine E W Levarht; Annette H M van der Helm-van Mil; Anthony Cerami; Tom W J Huizinga; Rene E M Toes; Leendert A Trouw Journal: Proc Natl Acad Sci U S A Date: 2011-10-10 Impact factor: 11.205
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: Klaus P Machold; Hans Peter Brezinsek; Burkhard F Leeb; Stephan Pflugbeil; Franz Rainer; Franz Singer; Martin Skoumal; Tanja A Stamm; Manfred Herold Journal: Wien Klin Wochenschr Date: 2008 Impact factor: 1.704
Authors: Rachel Knevel; Saskia le Cessie; Chikashi C Terao; Kamil Slowikowski; Jing Cui; Tom W J Huizinga; Karen H Costenbader; Katherine P Liao; Elizabeth W Karlson; Soumya Raychaudhuri Journal: Sci Transl Med Date: 2020-05-27 Impact factor: 17.956