Different clinical and laboratory features can be used to construct a novel, clinically relevant antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) classification system, a study published in Clinical Rheumatology suggests.
The researchers used the agglomerative hierarchical clustering method to identify the phenotypic subclasses of AAV patients and built an algorithm to help assign patients to one of the identified subclasses.
The study enrolled 210 Korean patients with AAV and classified them into mutually exclusive clusters according to Birmingham Vasculitis Activity Score items, ANCA specificity, sex, and age. There were 116 patients with AAV (55%) with microscopic polyangiitis, 53 (25%) with granulomatosis with polyangiitis, and 42 (20%) with eosinophilic granulomatosis with polyangiitis (EGPA).
According to the results, the model identified 5 clusters, including “limited proteinase 3 (PR3)-ANCA vasculitis,” “generalized PR3-ANCA vasculitis,” “ANCA-negative vasculitis,” “renal-limited vasculitis,” and “myeloperoxidase-ANCA vasculitis.”
Read more about AAV types
To determine the clinical significance of this classification system, the clinical outcomes of all the clusters were analyzed. A distance-based algorithm of the patient assignment was proposed, as well as its clinically relevant modification.
“The resulting clusters were derived from and specified by the presence or absence of each clustering variable in the study population. We evaluated all clustering variables instead of identifying a cluster based on the presence of only a few variables,” Lee and colleagues noted.
Patients belonging to the “generalized PR3-ANCA vasculitis” cluster had a high relapse rate, whereas those clustered under “renal-limited vasculitis” had a higher incidence of end-stage renal disease, and those in the “ANCA-negative vasculitis” cluster had a relatively milder clinical course of AAV with low mortality.
“Even if a patient does not present all the matching phenotypes described in a specific cluster, they can still be a member of the cluster if it is their “closest” cluster. In other words, a patient is assigned to the most similar cluster based on the clustering variables,” the study authors explained.
Read more about AAV diagnosis
“Another strength of this algorithm is that the distance may be selectively modified based on clinical needs.”
The overlapping clinical phenotypes of the existing AAV subgroups create confusion about their diagnostic and classification criteria, warranting a new classification system.
Reference
Lee LE, Pyo JY, Ahn SS, Song JJ, Park YB, Lee SW. Antineutrophil cytoplasmic antibody-associated vasculitis classification by cluster analysis based on clinical phenotypes: a single-center retrospective cohort study. Clin Rheumatol. Published online August 2, 2023. doi:10.1007/s10067-023-06720-7
