OBJECTIVE: To construct a prognostic algorithm to predict 5-year functional outcome in rheumatoid arthritis (RA), based on the Health Assessment Questionnaire (HAQ). METHODS: Data from all patients with 5-year followup (n = 985) were used from an inception cohort, the Early Rheumatoid Arthritis Study (ERAS). Possibly relevant prognostic factors considered in the initial stage of the model-building process were standard clinical, radiological, and laboratory features measured at baseline and at 1 year. Multivariate analysis was performed using logistic regression, and the predictive performance of the model was tested using measures of discrimination and calibration. RESULTS: Bootstrap resampling identified 6 variables that consistently predicted severe functional outcome. Functional grade III/IV (odds ratio 6.7) and HAQ at 1 year (odds ratio 2.4) were the most important. Other variables included socioeconomic status, hemoglobin, and radiographic and disease activity scores. Estimates of the regression coefficients and performance were corrected for over-fitting. Reasonably large values for the c-index (0.82) and the Nagelkerke R(2) (0.39) indicate that the set of prognostic factors explains the variation in outcome to a degree that implies good prediction for individual patients. CONCLUSION: The algorithm identifies patients in the first year of RA who are likely to have poor function by 5 years and who could potentially benefit from aggressive drug therapy. A nomogram is produced for simple application of the model in clinical practice. While further external validation is necessary, this model could allow clinicians to target aggressive therapy earlier in a patient's disease course.
OBJECTIVE: To construct a prognostic algorithm to predict 5-year functional outcome in rheumatoid arthritis (RA), based on the Health Assessment Questionnaire (HAQ). METHODS: Data from all patients with 5-year followup (n = 985) were used from an inception cohort, the Early Rheumatoid Arthritis Study (ERAS). Possibly relevant prognostic factors considered in the initial stage of the model-building process were standard clinical, radiological, and laboratory features measured at baseline and at 1 year. Multivariate analysis was performed using logistic regression, and the predictive performance of the model was tested using measures of discrimination and calibration. RESULTS: Bootstrap resampling identified 6 variables that consistently predicted severe functional outcome. Functional grade III/IV (odds ratio 6.7) and HAQ at 1 year (odds ratio 2.4) were the most important. Other variables included socioeconomic status, hemoglobin, and radiographic and disease activity scores. Estimates of the regression coefficients and performance were corrected for over-fitting. Reasonably large values for the c-index (0.82) and the Nagelkerke R(2) (0.39) indicate that the set of prognostic factors explains the variation in outcome to a degree that implies good prediction for individual patients. CONCLUSION: The algorithm identifies patients in the first year of RA who are likely to have poor function by 5 years and who could potentially benefit from aggressive drug therapy. A nomogram is produced for simple application of the model in clinical practice. While further external validation is necessary, this model could allow clinicians to target aggressive therapy earlier in a patient's disease course.
Authors: Myrthe van Vilsteren; Cecile R L Boot; Dirk L Knol; Dirkjan van Schaardenburg; Alexandre E Voskuyl; Romy Steenbeek; Johannes R Anema Journal: BMC Musculoskelet Disord Date: 2015-05-06 Impact factor: 2.362
Authors: Sam Norton; Bo Fu; David L Scott; Chris Deighton; Deborah P M Symmons; Allan J Wailoo; Jonathan Tosh; Mark Lunt; Rebecca Davies; Adam Young; Suzanne M M Verstappen Journal: Semin Arthritis Rheum Date: 2014-05-09 Impact factor: 5.532