One of the five recipients is Karl Gisslander. His project is about the rare but life-threatening disease anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis, which has diverse disease presentations and clinical courses. Despite this patients with ANCA-associated vasculitis are all largely treated the same. There is an unmet need to better characterise disease presentation and trajectory and to evaluate what type of treatment could benefit which person.
Knowledge-integration in a rare disease
However, ANCA-associated vasculitis is a rare disease and investigations have long been hampered by the scarcity of data. Recently the European collaborative project FAIRVASC (Findable, Accessible, Interoperable, Reusable, Vasculitis) has enabled the integration of six European registries and epidemiological cohorts which have led to the creation of a real-world cohort of unprecedented size. In parallel, the European Vasculitis Society (EUVAS) has investigated the efficacy of different treatment regimes in several clinical trials. In this project data from these efforts will be combined to increase our understanding of disease presentation and its influence on treatment outcome.
Insights into disease diversity and personalized medicine
Using unsupervised machine learning Karl Gisslander aims to find and group patients displaying similar disease traits in the real-world FAIRVASC cohort. The identified disease groups will then be used to explore and compare the response to different types of treatments and treatment protocols in the EUVAS led clinical trials. This novel approach will provide new insights into the diversity of disease manifestations of ANCA-associated vasculitis and may in the long run allow for the right type of treatment to find the right patient.
