Development and validation of a clinical rule for recognition of early inflammatory arthritis

National and international guidelines recommend prompt referral of patients presenting with inflammatory arthritis (IA), but general practitioners (GPs) feel uncertain in their proficiency to detect synovitis through joint examination, the method of choice to identify IA. Our objective was to develop and validate a rule composed of clinical characteristics to assist GPs and other physicians in identifying IA when in doubt.

Split-sample derivation and validation study.

The Leiden Early Arthritis Recognition Clinic (EARC), a screening clinic for patients in whom GPs suspected but were unsure of the presence of IA.

1288 consecutive patients visiting the EARC.

Associations of clinical characteristics with presence of IA were determined using logistic regression in 644 patients, while validating the results in the other 644 patients (split-sample validation). To facilitate application in clinical practice, a simplified rule (with scores ranging from 0 to 7.5) was derived and validated.

IA was identified by a rheumatologist in 41% of patients. In univariable analysis, male gender, age ≥60 years, symptom duration <6 weeks, morning stiffness >60 min, a low number of painful joints (1-3 joints), presence of patient-reported joint swelling and difficulty with making a fist were associated with IA in the derivation data set. Using multivariable analysis, a simplified rule consisting of these seven items was derived and validated, yielding an area under the receiver operator characteristic curve (AUC) of 0.74 (95% CI 0.70 to 0.78) in the derivation data set. Validation yielded an AUC of 0.71 (95% CI 0.67 to 0.75). Finally, the model was repeated to study predicted probabilities with a lower prevalence of inflammatory arthritis to simulate performance in primary care settings.

Our rule, composed of clinical parameters, had reasonable discriminative ability for IA and could assist physicians in decision-making in patients with suspected IA, increasing appropriateness of healthcare utilisation.