The Patient Self-Administered Inflammatory Arthritis Detection Study.

Area of Science:

• Rheumatology
• Clinical Diagnostics
• Health Services Research

Background:

• Early diagnosis and treat-to-target strategies are crucial for improving outcomes in inflammatory arthritis (IA).
• Standardized patient-completed questionnaires can potentially reduce diagnostic delays by aiding referral decisions.
• This study investigated the effectiveness of two validated questionnaires in a Canadian rheumatology setting.

Purpose of the Study:

• To evaluate the discriminatory referral performance of the Early Inflammatory Arthritis Questionnaire (EIAQ) and Case Finding Axial Spondyloarthritis (CaFaSpA) in newly referred rheumatology patients.
• To assess the diagnostic accuracy of these questionnaires in identifying inflammatory arthritis (IA).
• To explore the potential of combining questionnaire items for improved IA prediction.

Main Methods:

• Patients completed the EIAQ and CaFaSpA questionnaires.
• Predictive scores were calculated using existing algorithms and compared against rheumatologist diagnoses (reference standard).
• Discriminative performance was assessed using Area Under the Curve (AUC), sensitivity, and specificity; exploratory regression models were used.

Main Results:

• Of 92 participants, 30 had time-sensitive IA, 35 other IA, and 27 non-IA. Referral times varied by IA type and patient demographics.
• The EIAQ alone showed moderate discriminative performance (AUC 0.59).
• Algorithms combining EIAQ and CaFaSpA questions achieved higher AUCs, up to 0.80, indicating improved predictive capability.

Conclusions:

• Routine collection of EIAQ and CaFaSpA questionnaires is feasible and useful for discriminating between patients with and without IA.
• Optimized algorithms using questionnaire data show promise for enhancing early IA detection and referral decisions.