Performance of a triage approach for identification of patients with inflammatory rheumatic and musculoskeletal diseases among rheumatology referrals.

Authors

Imke Redeker, Maria Zacharopoulou, Andrea Schmidt, Uta Kiltz, David Kiefer, Jürgen Braun, Xenofon Baraliakos

Published in

Annals of the rheumatic diseases. Sep 05, 2026. Epub Sep 05, 2026.

Abstract

This study aims to evaluate a triage approach prioritising assessment of patients with inflammatory rheumatic and musculoskeletal disease (iRMD) among rheumatology referrals.
In this prospective study, 1180 consecutive referrals to a tertiary rheumatology centre underwent a telephone interview (step 1), followed by a 10-minute rheumatologist consultation within 4 weeks (step 2), during which patients were scheduled for comprehensive inpatient assessment (step 3) immediately, within 4 weeks or later. Diagnostic performance and wait times were analysed. Machine learning (ML) models for steps 1 and 2 were retrospectively investigated.
Among 1180 referred patients, iRMD was suspected in 413 patients (35%) and not suspected in 767 (65%), of whom 52 (4.4%) were considered sufficiently unlikely to have an iRMD to forgo further evaluation. The remaining 1128 patients were scheduled for comprehensive assessment (step 3). Of these, 148 (13.1%) dropped out, whereas 980 patients proceeded. iRMD was diagnosed in 314 patients (32%); 666 (68%) had noninflammatory conditions. Step 2 correctly identified 211 patients with iRMD (sensitivity 67.2%) and 502 patients without iRMD (specificity 75.4%). Mean (SD) time from telephone interview to final assessment was shorter for patients with iRMD than for those without iRMD (33 [30] vs 55 [24] days). The step 1 and step 2 ML models achieved area under the receiver operating characteristic curve (AUC-ROC) values of 0.73 (95% CI: 0.65-0.80) and 0.78 (95% CI: 0.70-0.86), respectively.
The triage approach accelerated assessment for more than two-thirds of patients with iRMD, while highlighting the necessity of a full workup to capture the one-third of cases otherwise missed. Using ML may further improve triage performance.

PMID:
42701083
Bibliographic data and abstract were imported from PubMed on 06 Sep 2026.

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