Treatment innovation in ANCA-associated vasculitis: unleashing real-world data

Abstract

ANCA-associated vasculitis (AAV) is a rare systemic autoimmune disease that causes inflammation of small blood vessels, potentially leading to life-threatening organ damage. In this thesis, we aimed to innovate treatment of AAV by unleashing real-world data captured in electronic health records.

First, we developed and validated an artificial intelligence (AI)-based method to identify AAV patients by analyzing free text in patient records. This method enabled the collection of large sets of real-world data.

Subsequently, we analyzed real-world outcomes of novel treatment strategies that might improve treatment of AAV by preventing relapses, reducing toxicity and/or improving cost-effectiveness. We demonstrated that steroid-related toxicity has a significantly higher impact on hospital costs than disease relapses. We found that obinutuzumab might have a higher immunological and clinical efficacy than rituximab and can be a cost-effective alternative. We showed that avacopan can be beneficial for difficult-to-treat patients by improving disease control and reducing steroid-toxicity. We provided a safety summary for avacopan outside clinical trials and proposed different avacopan-based treatment strategies for subgroups of AAV patients with high risk for steroid-toxicity.

Lastly, we outlined how the ENDURRANCE study explores if combining rituximab with low-dose cyclophosphamide is a more effect induction treatment than rituximab alone.