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Published on: February 8, 2019
Assessment and comparison of probability scores to predict giant cell arteritis
Chadi Sargi1, Stephanie Ducharme-Benard2, Valerie Benard2
1Department of Medicine, Montreal Sacre-Coeur Hospital, University of Montreal, Montreal, QC, Canada.
Insights
Combining the giant cell arteritis probability score (GCAPS) with ultrasound halo count accurately predicts giant cell arteritis (GCA). This approach offers high sensitivity and specificity, improving diagnostic speed in GCA Fast-Track clinics.
Area of Science:
- Rheumatology
- Vascular Medicine
- Diagnostic Imaging
Background:
- Giant cell arteritis (GCA) is a systemic vasculitis affecting large arteries, primarily the aorta and its branches.
- Accurate and timely diagnosis of GCA is crucial to prevent irreversible complications such as vision loss.
- Clinical assessment and imaging play vital roles in GCA diagnosis, but comparative performance data for various scoring systems are needed.
Purpose of the Study:
- To compare the diagnostic performance of three clinical prediction scores (GCAPS, Ing score, BK score) and two color Doppler ultrasound (CDUS) metrics (halo count, halo score) for predicting GCA.
- To evaluate the combined utility of clinical and CDUS scores in GCA diagnosis.
Main Methods:
- A prospective cohort study included 200 patients with suspected new-onset GCA.
- Clinical and CDUS data were collected at initial visits to calculate prediction scores.
- Final GCA diagnosis was confirmed by blinded vasculitis specialists after 6 months, with diagnostic accuracy assessed via ROC curves.
Main Results:
- The giant cell arteritis probability score (GCAPS) demonstrated the highest sensitivity (0.983) among clinical scores, while the Bhavsar-Khalidi (BK) score showed the highest specificity (0.711).
- Color Doppler ultrasound (CDUS) halo count of 1 or more exhibited high sensitivity (0.966) and specificity (0.979).
- Combining concordant clinical and CDUS prediction scores yielded excellent performance in predicting GCA.
Conclusions:
- A combined approach using a clinical score (e.g., GCAPS) and CDUS halo count provides an accurate and effective method for GCA prediction.
- This combined strategy is recommended for use in GCA Fast-Track clinics to expedite diagnosis and management.
- Ultrasound halo count alone is a sensitive and specific indicator for GCA diagnosis.
Introduction/Objectives:
To assess and compare the performance of the giant cell arteritis probability score (GCAPS), Ing score, Bhavsar-Khalidi score (BK score), color Doppler ultrasound (CDUS) halo count, and halo score, to predict a final diagnosis of giant cell arteritis (GCA).
Method:
A prospective cohort study was conducted from April to December 2021. Patients with suspected new-onset GCA referred to our quaternary CDUS clinic were included. Data required to calculate each clinical and CDUS probability score was systematically collected at the initial visit. Final diagnosis of GCA was confirmed clinically 6 months after the initial visit, by two blinded vasculitis specialists. Diagnostic accuracy and receiver operator characteristic (ROC) curves for each clinical and CDUS prediction scores were assessed.
Results:
Two hundred patients with suspected new-onset GCA were included: 58 with confirmed GCA and 142 without GCA. All patients with GCA satisfied the 2022 ACR/EULAR classification criteria. A total of 5/15 patients with GCA had a positive temporal artery biopsy. For clinical probability scores, the GCAPS showed the best sensitivity (Se, 0.983), whereas the BK score showed the best specificity (Sp, 0.711). As for CDUS, a halo count of 1 or more was found to have a Se of 0.966 and a Sp of 0.979. Combining concordant results of clinical and CDUS prediction scores showed excellent performance in predicting a final diagnosis of GCA.
Conclusion:
Using a combination of clinical score and CDUS halo count provided an accurate GCA prediction method which should be used in the setting of GCA Fast-Track clinics. Key Points • In this prospective cohort of participants with suspected GCA, 3 clinical prediction tools and 2 ultrasound scores were compared head-to-head to predict a final diagnosis of GCA. • For clinical prediction tools, the giant cell arteritis probability score (GCAPS) had the highest sensitivity, whereas the Bhavsar-Khalidi score (BK score) had the highest specificity. • Ultrasound halo count was both sensitive and specific in predicting GCA. • Combination of a clinical prediction tool such as the GCAPS, with ultrasound halo count, provides an accurate method to predict GCA.
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