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An Immunohistopathologic Study to Profile the Folate Receptor Beta Macrophage and Vascular Immune Microenvironment in Giant Cell Arteritis
Published on: February 8, 2019
Ultrasonography-led multimodal diagnostic pathway for giant cell arteritis
Chetan B Mukhtyar1, Clare Beadsmoore1, Georgina Ducker1
1Norfolk and Norwich University Hospitals, NHS Foundation Trust, Norwich, UK.
A multimodal diagnostic pathway using ultrasonography, PET-CT, and temporal artery biopsy significantly improves giant cell arteritis diagnosis. This approach enhances sensitivity and negative predictive value for accurate GCA detection.
Area of Science:
- Rheumatology
- Diagnostic Imaging
- Internal Medicine
Background:
- Giant cell arteritis (GCA) diagnosis relies on clinical suspicion and invasive tests.
- Current diagnostic methods have limitations in sensitivity and specificity.
- A multimodal approach may improve diagnostic accuracy for GCA.
Purpose of the Study:
- To evaluate the sensitivity and negative predictive value of a diagnostic pathway for GCA.
- To assess the utility of ultrasonography, 18-fluorodeoxyglucose labelled PET-CT, and temporal artery biopsy in GCA diagnosis.
Main Methods:
- 1000 consecutive GCA referrals were analyzed.
- A protocolized examination included ultrasonography, CRP levels, and extended ultrasound if indicated.
- A second test (temporal artery biopsy or PET-CT) was offered for negative initial assessments.
Main Results:
- Positive ultrasonography was found in 279/1000 patients.
- Extended ultrasonography and a second test increased diagnostic sensitivity to 95.7% and negative predictive value to 98.0% for GCA.
- Temporal artery biopsy yielded positive results in 24/139, and PET-CT in 7/42.
Conclusions:
- This study provides real-world evidence for the effectiveness of a multimodal diagnostic pathway for GCA.
- Ultrasonography, as part of a multimodal strategy, is valuable for diagnosing giant cell arteritis.
- The combined approach significantly enhances diagnostic performance compared to single-modality testing.
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