A multivariable prediction model to identify anti-CCP positive people in those with non-specific musculoskeletal symptoms in primary care

Objectives We aimed to develop a prediction model identifying people presenting to primary care with musculoskeletal symptoms likely to be anti-CCP positive and therefore at risk of developing RA.

Methods Participants aged ≥16 years, with new-onset non-specific musculoskeletal symptoms and no history of clinical synovitis, completed a symptom questionnaire and had an anti-CCP test. Model development used LASSO-penalized logistic regression, performance was assessed using area under the receiver operating characteristic curve (AUROC) and decision curve analysis, model over-fit was estimated using bootstrapping and cross-validation. Participants were followed-up at 12 months for RA or seronegative/undifferentiated inflammatory arthritis diagnosis.

Results Analysis included 6879 participants; 203 (2.95%) of whom were anti-CCP positive. Eleven predictors were retained: male sex, first-degree relative with RA, ever smoked and joint pain in: back, neck, shoulders, wrists, hands/fingers, thumbs, knees, feet/toes. AUROC was 0.65 (95% CI 0.61, 0.69, optimism = 0.03). Using a 4% decision threshold, the model recommended an anti-CCP test in 1288 (18.7%) participants, 78 (6.1%) of whom were anti-CCP positive, compared with 125/5591 (2.2%) below the threshold. Net benefit was 0.0040 (0.0020 corrected). Forty-eight participants were diagnosed with inflammatory arthritis/RA within 12 months. Of those who were above the threshold and anti-CCP positive, 32.1% developed inflammatory arthritis/RA compared with 0.4% of those who were anti-CCP negative. Of those below the threshold, 0.3% were diagnosed with inflammatory arthritis/RA.

Conclusions Targeted anti-CCP testing in primary care may aid earlier identification of people at risk of RA, prompting specialist referral to rheumatology for earlier diagnosis and initiation of disease-modifying therapy.