Key result
High-risk vascular phenotype is linked to ~21% stroke recurrence versus 7% to 8% in other clusters.
Why the study?
Effective secondary prevention for ischemic stroke requires accurate risk stratification and individualized treatment strategies. This study sought to identify distinct ischemic stroke phenotypes using a systematic, data-driven clustering approach based on clinical, demographical, and imaging features to compare long-term stroke recurrence across clusters.
Does data-driven cluster analysis of clinical, demographic, and laboratory features identify distinct ischemic stroke phenotypes associated with different rates of long-term stroke recurrence?
Cohort (n=799)
No
Does data-driven cluster analysis of clinical, demographic, and laboratory features identify distinct ischemic stroke phenotypes associated with different rates of long-term stroke recurrence?
Data-driven cluster analysis of ischemic stroke patients identified four distinct clinical phenotypes, revealing a high-risk subgroup of male patients with extensive vascular comorbidities and elevated inflammation who experienced a threefold higher rate of long-term stroke recurrence (21% vs 7-8%).
Answers are grounded in this paper and cite their sources.
Cluster-2 phenotype warrants enhanced surveillance for recurrence; hypothesis-generating for phenotype-specific prevention trials.
Effective secondary prevention for ischemic stroke (IS) requires accurate risk stratification and individualized treatment strategies. This study aimed to identify distinct IS phenotypes using a systematic, data-driven clustering approach based on clinical, demographical, and imaging features, and compared to recurrent stroke across clusters using survival analysis. Patients hospitalized with diagnosis of IS in the stroke unit of Istanbul Faculty of Medicine between 2011 and 2018 were screened, and those who attended three-month follow-up outpatient clinic were included. Clustering analysis were performed using Gower distance on mixed dataset followed by partitioning around medoids for clustering. Variable selection was conducted using a systemic approach combining univariate methods, internal validity indices, and visualization techniques. 799 patients (60% male) with mean age 63.53 years (± 15.17) were included in the study. The dataset was ultimately classified into four clusters. Cluster-1 represented younger male patients with favorable in-hospital course, Cluster-2 captured high-risk male patients with substantial vascular comorbidity and inflammation, exhibiting the highest recurrence; Cluster-3 comprised older female patients with cancer and inflammatory profiles, and Cluster-4 composed of the oldest female patients with atrial fibrillation and prior stroke, characterized by more severe stroke. The median follow-up duration was 26 months, and recurrent stroke differed significantly among the four clusters. These findings suggest that cluster analysis may serve as a valuable tool for clinical risk stratification in IS. The higher frequency of recurrent stroke in Cluster 2, characterized by greater vascular comorbidities indicates that this Cluster may represent a population requiring closer monitoring and more intense secondary prevention strategies.
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Sezgi̇n et al. (2026) conducted a cohort in Acute ischemic stroke (n=799). Cluster-2 ischemic stroke phenotype (high-risk male patients with substantial vascular comorbidity and inflammation) derived via PAM clustering vs. Clusters 1, 3, and 4 (other data-driven ischemic stroke phenotypes) was evaluated on Recurrent stroke during follow-up (p=0.006). Cluster-2 (high-risk males with vascular comorbidity and inflammation) had the highest stroke recurrence at 21% versus 7-8% in other clusters (log-rank p=0.006).
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