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Published on: February 8, 2019
Development of a diagnostic prediction model for giant cell arteritis by sequential application of Southend Giant
Alwin Sebastian1, Kornelis S M van der Geest2, Alessandro Tomelleri3
1Rheumatology, Southend University Hospital, Mid and South Essex NHS Foundation Trust, Westcliff-on-sea, UK; School of Sport, Rehabilitation and Exercise science, University of Essex, Colchester, UK; Rheumatology, University Hospital Limerick, Dooradoyle, Ireland.
A new HAS-GCA score combines clinical assessment (SGCAPS) and ultrasound halo count to accurately diagnose giant cell arteritis (GCA). This tool helps differentiate GCA from similar conditions in fast-track clinics.
Area of Science:
- Rheumatology
- Vascular Medicine
- Diagnostic Imaging
Background:
- Giant cell arteritis (GCA) is a serious ischemic disease requiring prompt diagnosis and treatment.
- European Alliance of Associations for Rheumatology (EULAR) guidelines recommend ultrasonography as the initial diagnostic step for suspected GCA.
- A novel prediction tool has been developed to integrate clinical assessment with quantitative ultrasonography for improved GCA diagnosis.
Purpose of the Study:
- To develop and validate a prediction tool for diagnosing giant cell arteritis (GCA).
- To sequentially combine clinical assessment (Southend Giant Cell Arteritis Probability Score - SGCAPS) with quantitative ultrasonography findings.
- To improve the accuracy and efficiency of GCA diagnosis in fast-track clinical settings.
Main Methods:
- A prospective, multicentre, inception cohort study involving 229 patients with suspected new-onset GCA.
- Clinical assessment using SGCAPS and quantitative ultrasonography (temporal and axillary arteries) with three scores (halo count, halo score, OMERACT GCA Score [OGUS]).
- Multivariable logistic regression was used to develop prediction models, with the HAS-GCA score (SGCAPS + halo count) identified as the most accurate.
Main Results:
- The HAS-GCA score demonstrated high accuracy (C statistic 0.969) in diagnosing GCA, correctly classifying 74% of patients.
- Ultrasonographic scores and SGCAPS effectively discriminated between GCA patients and controls.
- High inter-rater and intra-rater reliability were confirmed for the ultrasonographic scores.
Conclusions:
- The HAS-GCA score, integrating SGCAPS and halo count, reliably aids in confirming or excluding giant cell arteritis (GCA) in fast-track clinics.
- This prediction tool offers a valuable method for differentiating GCA from its mimics.
- Further validation in an independent, multicentre study is recommended.
