Development and validation of a clinical risk prediction rule to identify inflammatory arthritis at the point of rheumatology triage

Abstract Objectives To develop and validate a clinical prediction rule to identify inflammatory arthritis (IA) using routinely collected triage data, without ACPA. Methods Prospective observational data from 184 patients referred to a tertiary care rheumatology clinic for joint pain over a 9-month period and meeting inclusion criteria were used to derive a clinical risk prediction rule for the diagnosis of IA versus non-IA, utilizing penalized and stepwise logistic regression modelling, including age, sex, CRP and RF. Internal validation was performed using 5-fold cross-validation. A population within the UK Biobank with a diagnosis of incident IA or non-IA, within 180 days of baseline assessment (N = 2828), was used for broad external domain validation. Model performance was assessed by the c-statistic (cvAUC) and the Hosmer–Lemeshow test. Results In the derivation cohort, CRP had the strongest association with diagnosis of IA in both models, other important predictors being sex and CRP twice the upper limit of normal in the stepwise model (cvAUC = 0.72, 95% CI 0.58, 0.85) and additionally RF and age in the penalized regression model (cvAUC = 0.77, 95% CI 0.59, 0.95). When applied in the UK Biobank, AUC values decreased (0.54–0.55); however, limitations in accurately defining the timing of incident arthritis indicate the need for geographical validation in a cohort that better fits the target population and setting. Conclusions This simple prediction rule, utilizing demographic and laboratory data, performed well in discriminating IA from non-IA in the derivation population. Additional validation studies in similar clinical settings are required to further refine the model and determine generalizability.