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The Patient Self-Administered Inflammatory Arthritis Detection Study.

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Patient questionnaires like the Early Inflammatory Arthritis Questionnaire (EIAQ) can help identify inflammatory arthritis (IA) early. Combining questions from EIAQ and CaFaSpA improved diagnostic accuracy, supporting their routine use.

Keywords:
arthritisquestionnairereferralsrheumatologytriagewaiting lists

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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.