A data-driven analysis of a large cohort of patients with ANCA-associated vasculitis subclassified the condition into five distinct clusters, offering more accurate survival predictions.
A data-driven subclassification of antineutrophil cytoplasmic antibody –associated vasculitis has identified five distinct clusters with varying degrees of kidney involvement and systemic inflammation, offering insights into improved patient stratification and treatment approaches.
ANCA-associated vasculitis is a rare and complex autoimmune disease that is traditionally classified into granulomatosis with polyangiitis and microscopic polyangiitis . Researchers employed advanced artificial intelligence and big data techniques to identify phenotypically distinct subgroups of ANCA-associated vasculitis and developed a classification system using real-world patient data from the Federated Vasculitis Registry consortium. They included 3868 patients diagnosed with ANCA-associated vasculitis between November 1, 1966, and March 1, 2023 , across six European vasculitis registries; while a majority of patients were diagnosed with GPA, the remaining 37.1% were diagnosed with MPA. Overall, 17 clinical and demographic variables such as the age at diagnosis, gender, serum creatinine and C-reactive protein levels, the type of ANCA, and the involvement of various organ systems were used to create a model for categorizing patients into different clusters.Five distinct clusters were identified in ANCA-associated vasculitis; three had significant kidney involvement and two had minimal kidney involvement . The clusters with significant kidney involvement were associated with poorer outcomes, including a higher risk for kidney failure and death. The severe kidney cluster had the poorest prognosis, with mortality and the rate of end-stage kidney failure being 30.5% and 41.6%, respectively. The young respiratory cluster, characterized by predominant ear-nose-throat involvement and low systemic inflammation, showed the best prognostic outcomes. This cluster membership model showed a greater predictive accuracy for patient and kidney survival than traditional methods based on clinical diagnosis or ANCA specificity."These findings highlight the necessity of recognizing severe kidney disease at the time of diagnosis as an indicator of poor outcome, thereby necessitating intensified treatment approaches," experts from the Department of Internal Medicine IV , Medical University of Innsbruck, Innsbruck, Austria, wrote inThis study was led by Karl Gisslander, Department of Clinical Sciences, Lund University, Lund, Sweden, and wasData on estimated glomerular filtration rate recovery in clusters with kidney disease were lacking. Populations from East Asia, where myeloperoxidase-ANCA positivity is more prevalent, were not included.This study received funding from the European Union's Horizon 2020 research and innovation program under the European Joint Programme on Rare Diseases. Some authors declared serving on advisory boards or receiving grants, contracts, travel support, consulting fees, payments, or honoraria from various pharmaceutical companies and other institutions. This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.
Vasculitides Angiitis Kidneys Chronic Renal Failure Chronic Kidney Failure CKD - Chronic Kidney Disease Chronic Kidney Disease CKD Renal Disease Kidney Disease Kidney Disorder Renal Disorder Nephropathy Renal Failure Renal Insufficiency Kidney Insufficiency Kidney Failure Renal Impairment Artificial Intelligence Deep Learning AI NPL Machine Learning
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