Annals of Internal Medicine · 2023 · 41 citations · 36 references
MedicineBiostatisticsImmunologyInflammationRheumatologyRisk AssessmentDisease Risk AssessmentInflammatory ArthritisInflammatory BiomarkersRheumatoid ArthritisRisk PredictionRisk ScoringArthritis EpidemiologyFalse Negative Rate AnalysisLogistic RegressionLogistic Regression ScoringPredictive Risk ScoringProspective Cohort DataRisk Prediction ScoresSynovial Biomarkers
BACKGROUND: Inflammatory arthritis (IA) is an immune-related condition defined by the presence of clinical synovitis. Its most common form is rheumatoid arthritis. OBJECTIVE: To develop scores for predicting IA in at-risk persons using multidimensional biomarkers. DESIGN: Prospective observational cohort study. SETTING: Single-center, Leeds, United Kingdom. PARTICIPANTS: 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. MEASUREMENTS: 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. RESULTS: 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. LIMITATIONS: 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. CONCLUSION: 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. PRIMARY FUNDING SOURCE: National Institute for Health and Care Research Leeds Biomedical Research Centre.
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Daniel Aletaha, Tuhina Neogi, Alan J. Silman et al. · Arthritis & Rheumatism · 2010 · 9.6K citations · Full text
Medicine, Internal Medicine, Clinical Classification Systems +13
Prognosis and prognostic research: Developing a prognostic model
Patrick Royston, Karel G.M. Moons, Doug Altman et al. · BMJ · 2009 · 1.2K citations