Predicting Inflammatory Arthritis in At-Risk Persons: Development of Scores for Risk Stratification

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Abstract
Inflammatory arthritis (IA) is an immune-related condition defined by the presence of clinical synovitis. Its most common form is rheumatoid arthritis. To develop scores for predicting IA in at-risk persons using multidimensional biomarkers. Prospective observational cohort study. Single-center, Leeds, United Kingdom. Persons with new musculoskeletal symptoms, a positive test result for anticitrullinated protein antibodies, and no clinical synovitis and followed for 48 weeks or more or until IA occurred. A simple score was developed using logistic regression, and a comprehensive score was developed using the least absolute shrinkage and selection operator Cox proportional hazards regression. Internal validation with bootstrapping was estimated, and a decision curve analysis was done. Of 455 participants, 32.5% (148 of 455) developed IA, and 15.4% (70 of 455) developed it within 1 year. The simple score identified 249 low-risk participants with a false negative rate of 5% (and 206 high-risk participants with a false-positive rate of 72%). The comprehensive score identified 119 high-risk participants with a false-positive rate of 29% (and 336 low-risk participants with a false-negative rate of 19%); 40% of high-risk participants developed IA within 1 year and 71% within 5 years. External validation is required. Recruitment occurred over 13 years, with lower rates of IA in later years. There was geographic variation in laboratory testing and recruitment availability. The simple score identified persons at low risk for IA who were less likely to need secondary care. The comprehensive score identified high-risk persons who could benefit from risk stratification and preventive measures. Both scores may be useful in clinical care and should also be useful in clinical trials. National Institute for Health and Care Research Leeds Biomedical Research Centre.

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