Data-Driven Clustering Approach to Identify Different Phenotypes of Primary Central Nervous System Vasculitis

To determine whether hierarchical unsupervised cluster analysis identifies a phenotypic distinction in adult patients with primary CNS vasculitis (PCNSV).

An agglomerative hierarchical cluster analysis based on the Ward method was conducted, including 153 patients with complete baseline phenotypic characterization in the COVAC' registry.

The hierarchical analysis identified two main clusters. In Cluster 1 (n = 109 patients, 71%), patients more frequently had a motor deficit (p = 0.039), ≥ 1 acute brain infarct (p < 0.001), and ≥ 1 intracranial stenosis on CT or MR angiogram (p < 0.001) than patients in Cluster 2 (n = 44 patients, 29%). Conversely, patients in Cluster 2 more frequently had seizures (p < 0.001), cognitive impairment (p = 0.002), gadolinium-enhanced parenchymal lesions (p < 0.001), leptomeningeal enhancement (p < 0.001), ≥ 1 cerebral microbleed (p < 0.001), and intracranial hemorrhage(s) (p < 0.001). In multivariable logistic regression, gadolinium-enhanced parenchymal lesions were significantly associated with Cluster 2 lesions (OR = 35.53 [95% CI: 3.91-322.81], p = 0.002). Conversely, ≥ 1 acute brain infarct was significantly associated with Cluster 1 (OR = 0.003 [95% CI: 0.01-0.03], p < 0.001). A CNS biopsy was positive in 11/40 (28%) patients from Cluster 1 and 35/37 (95%) patients from Cluster 2 (p < 0.001). At 12 months, functional independence (modified Rankin scale score ≤ 2) did not differ between the two groups (p = 0.17). Relapse and mortality rates did not differ between the clusters (p = 0.17 and p = 0.23, respectively).

This unsupervised analysis of a large PCNSV cohort identified two different clinical and radiological phenotypes with different diagnostic work-ups, which confirms the relevance of distinguishing PCNSV phenotypes according to the sizes of affected vessels.