OBJECTIVE: The benefits of early intensive treatment of inflammatory arthritis (IA) are dependent on timely and accurate case identification. In our study, a scoring algorithm for a self-administered IA detection tool was developed and validated for the rheumatology triage clinical setting. METHODS: A total of 143 consecutive consenting adults, newly referred to 2 outpatient rheumatology practices, completed the tool. A scoring algorithm was derived from the best-fit logistic regression model using age, sex, and responses to the 12 tool items as candidate predictors of the rheumatologists' blinded classification of IA. Bootstrapping was used to internally validate and refine the model. RESULTS: The 30 IA cases were younger than the 113 non-cases (p < 0.0001) and included clinical diagnoses of early IA (n = 10), rheumatoid arthritis (n = 9), and spondyloarthropathies (n = 11). Non-cases included osteoarthritis (n = 46), pain syndromes (n = 19), systemic lupus erythematosus (n = 5), and miscellaneous, noninflammatory musculoskeletal complaints (n = 43). The best-fit model included younger age, male sex, "trouble making a fist," "morning stiffness," "ever told you have RA," and "psoriasis diagnosis." The overall predictive performance (standard error, SE) of the derivation model was 0.91 (0.03). Internal validation of the derivation model across 200 bootstrap samples resulted in a mean predictive performance (SE) of 0.904 (0.002). The refined tool had a mean predictive performance (SE) of 0.915 (0.002), a sensitivity of 0.855 (0.005), and specificity of 0.873 (0.003). CONCLUSION: A simple, self-administered tool was developed and internally validated for the sensitive and specific detection of IA in a rheumatology waiting list sample. The tool may be used to triage IA from rheumatology referrals.
OBJECTIVE: The benefits of early intensive treatment of inflammatory arthritis (IA) are dependent on timely and accurate case identification. In our study, a scoring algorithm for a self-administered IA detection tool was developed and validated for the rheumatology triage clinical setting. METHODS: A total of 143 consecutive consenting adults, newly referred to 2 outpatient rheumatology practices, completed the tool. A scoring algorithm was derived from the best-fit logistic regression model using age, sex, and responses to the 12 tool items as candidate predictors of the rheumatologists' blinded classification of IA. Bootstrapping was used to internally validate and refine the model. RESULTS: The 30 IA cases were younger than the 113 non-cases (p < 0.0001) and included clinical diagnoses of early IA (n = 10), rheumatoid arthritis (n = 9), and spondyloarthropathies (n = 11). Non-cases included osteoarthritis (n = 46), pain syndromes (n = 19), systemic lupus erythematosus (n = 5), and miscellaneous, noninflammatory musculoskeletal complaints (n = 43). The best-fit model included younger age, male sex, "trouble making a fist," "morning stiffness," "ever told you have RA," and "psoriasis diagnosis." The overall predictive performance (standard error, SE) of the derivation model was 0.91 (0.03). Internal validation of the derivation model across 200 bootstrap samples resulted in a mean predictive performance (SE) of 0.904 (0.002). The refined tool had a mean predictive performance (SE) of 0.915 (0.002), a sensitivity of 0.855 (0.005), and specificity of 0.873 (0.003). CONCLUSION: A simple, self-administered tool was developed and internally validated for the sensitive and specific detection of IA in a rheumatology waiting list sample. The tool may be used to triage IA from rheumatology referrals.
Authors: Ana-Maria Orbai; Katherine C Smith; Susan J Bartlett; Elaine De Leon; Clifton O Bingham Journal: Arthritis Care Res (Hoboken) Date: 2014-11 Impact factor: 4.794