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Giant cell arteritis (GCA) is the most common vasculitis in the elderly and is usually treated with long-term corticosteroid therapy. Many patients experience relapses and treatment-related side effects. Current diagnostic and monitoring methods provide limited prognostic information and cannot reliably distinguish active from inactive disease during relapse. This project addresses the clinical need for improved tools to identify patients at high risk of relapse and to develop more effective methods for disease monitoring. The aim is to develop new tools that enable more personalized treatment of GCA. By combining vascular ultrasound with novel blood biomarkers, we seek to predict disease course and relapse risk. The specific objectives are: * To identify ultrasound and blood biomarkers that can predict long-term disease control. * To determine which ultrasound parameters and blood bio
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