Evaluating the accuracy of the frysian questionnaire for differentiation of musculoskeletal complaints for triage of musculoskeletal diseases: algorithm development and validation study

Maarseveen, T.D.; Reimann, F.; Hasan, A. al; Schilder, A.; Zhang, D.; Wink, F.; ... ; Bos, R. (2025)

Article / Letter to editor

Background:

Inflammatory rheumatic diseases (IRDs) affect 5% of the general population, whereas 35% of the population experiences musculoskeletal concerns. IRDs cause early disability, reduced life expectancy, and considerable health care costs. Early diagnosis is essential to prevent long-term damage. Similarly important is the early identification of patients with musculoskeletal concerns without IRDs to prevent unnecessary health care expenses. Of the population referred to the rheumatologist, 60% have noninflammatory musculoskeletal concerns, whereas only 20% of patients with an IRD see a rheumatologist within 3 months of symptom onset. The need for digital predictive (triage) tools for rheumatic and musculoskeletal diseases led to the development of the Frysian Questionnaire for Differentiation of Musculoskeletal Complaints (FRYQ).

Objective:

This study aimed to assess whether the FRYQ can distinguish IRD from noninflammatory musculoskeletal concerns in general, and rheumatoid arthritis and fibromyalgia specifically, in newly referred patients.

Methods: The FRYQ is an 87-item tool (20 open-ended and 67 closed-ended questions) used to triage new rheumatology patients at Frisius Medical Center in the Netherlands. We analyzed data from 2 sources: dataset A with 728 outpatient clinic patients and dataset B with 373 patients from the Joint Pain Assessment Scoring Tool study. We built a classifier using Extreme Gradient Boosting to distinguish inflammatory from noninflammatory conditions based on closed-ended questions. Using elastic net regularization, we identified the most informative questions. We evaluated classification using receiver operating characteristic curve analysis and assessed feature importance through Shapley Additive Explanation analysis. To test generalizability, we replicated our analysis on dataset B. Finally, we examined whether the questions of the FRYQ could be used to identify specific conditions beyond the general categories of IRD and non-IRD, specifically for detecting fibromyalgia and rheumatoid arthritis.

Results:

Feature selection reduced the questionnaire from 67 to 28 items while maintaining discriminative power. After initial development, the model achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.72 (95% CI 0.67-0.78) for distinguishing inflammatory from noninflammatory conditions in an external validation set. Using a probability threshold of 0.30, the model achieved 71% sensitivity and 56% specificity on external validation. The FRYQ demonstrated stronger performance in identifying specific diagnoses such as fibromyalgia (AUC-ROC=0.81) and rheumatoid arthritis (AUC-ROC=0.77). Key discriminating features included symptom duration, pain response to movement, and anti-inflammatory medication effectiveness.

Conclusions:

The FRYQ effectively distinguishes inflammatory from noninflammatory rheumatic conditions before specialist consultation and shows particular strength in identifying fibromyalgia and rheumatoid arthritis. This tool could improve rheumatology triage by prioritizing referrals with high likelihood of IRD for early rheumatologist evaluation while directing other patients to appropriate alternative resources. Prospective studies are needed to determine the FRYQ's impact on clinical outcomes and health care efficiency.

• medical informatics
• questionnaire
• machine learning
• fibromyalgia
• rheumatic diseases
• rheumatoid arthritis
• triage
• decision support system
• symptom checker

• All authors: Maarseveen, T.D.; Reimann, F.; Hasan, A. al; Schilder, A.; Zhang, D.; Wink, F.; Hendriks, L.; Knevel, R.; Bos, R.
• Date: 2025-01-01
• Journal: JMIR Medical Informatics
• Volume: 13
• DOI: doi:10.2196/77345