ABSTRACT NUMBER: ESOC2026A2135 IDENTIFICATION OF ISCHEMIC STROKE SUBTYPES BY CLUSTER ANALYSIS AND THEIR LONG-TERM VASCULAR EVENT RISK

Abstract Background and aims Ischemic stroke (IS) is a heterogeneous disease with substantially varying etiologies and outcomes, and long-term subtype-specific follow-up is often lacking. We aimed to determine whether patients with IS can be classified into distinct clusters with differing clinical characteristics and long-term outcomes. Methods Demographic and clinical characteristics of patients with IS diagnosed between 2012 and 2018 were recorded. Hierarchical cluster analysis (HCA) was used to identify the optimal number of clusters, applying Gower’s distance to generate distinct patient groups. Clusters were compared with respect to clinical characteristics. Stroke recurrence, mortality, and hospital admissions were recorded and analyzed across clusters using survival analysis to evaluate differences in real-world clinical outcomes. Results Of 1,276 IS patients, 799 with ≥3 months of follow-up were included. According to HCA, three clusters were identified as the optimal solution. Using prespecified clinical and laboratory variables, Gower’s distance was calculated, and three distinct clusters were formed (Figure-1A). Cluster A comprised younger patients (55±14 years) with higher hematocrit and LDL levels, whereas Cluster B included older patients (70±14 years) with higher NIHSS scores. Atrial fibrillation and hemorrhagic transformation were most frequent in Cluster B. Cluster C (65±12 years) had the highest burden of cardiovascular comorbidities, including hypertension, coronary artery disease and diabetes. The median follow-up duration was 26 months. Recurrent stroke rates were similar across clusters (Figure-1B); however, deaths were observed only in Cluster B. Conclusions Cluster analysis may identify clinically meaningful stroke subtypes, and future studies incorporating richer datasets could inform subtype-specific clinical management. Conflict of interest Mine Sezgin : Nothing to disclose, Muhammed Yusuf Cansever : Nothing to disclose, Elif Begik: Nothing to disclose, Cemal Kerem Karaduman: Nothing to disclose, Mehmet Sait Bingöl: Nothing to disclose, Ozan Alp: Nothing to disclose, Esme Ekizoğlu: Nothing to disclose, Nilüfer Yeşilot: Nothing to disclose, Mehmet Güven Günver: Nothing to disclose Figure 1 - belongs to Conclusions

Authors

Institutions

Publication Details

Journal
European Stroke Journal
Published
2026-05-01
DOI
https://doi.org/10.1093/esj/aakag023.1737
Primary Topic
Acute Ischemic Stroke Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ABSTRACT NUMBER: ESOC2026A2135 IDENTIFICATION OF ISCHEMIC STROKE SUBTYPES BY CLUSTER ANALYSIS AND THEIR LONG-TERM VASCULAR EVENT RISK

Mehmet Güven Günver, Mehmet Sait Bingöl, Nilufer Yesilot, Esme Ekizoglu et al.
European Stroke Journal
Acute Ischemic Stroke Management
article

ABSTRACT NUMBER: ESOC2026A2135 IDENTIFICATION OF ISCHEMIC STROKE SUBTYPES BY CLUSTER ANALYSIS AND THEIR LONG-TERM VASCULAR EVENT RISK

Mehmet Güven Günver, Mehmet Sait Bingöl, Nilufer Yesilot, Esme Ekizoglu, Cemal Kerem Karaduman, Elif Begik, Ozan Alp, Mine Sezgin, Muhammed Yusuf Cansever
article en

Abstract

Abstract Background and aims Ischemic stroke (IS) is a heterogeneous disease with substantially varying etiologies and outcomes, and long-term subtype-specific follow-up is often lacking. We aimed to determine whether patients with IS can be classified into distinct clusters with differing clinical characteristics and long-term outcomes. Methods Demographic and clinical characteristics of patients with IS diagnosed between 2012 and 2018 were recorded. Hierarchical cluster analysis (HCA) was used to identify the optimal number of clusters, applying Gower’s distance to generate distinct patient groups. Clusters were compared with respect to clinical characteristics. Stroke recurrence, mortality, and hospital admissions were recorded and analyzed across clusters using survival analysis to evaluate differences in real-world clinical outcomes. Results Of 1,276 IS patients, 799 with ≥3 months of follow-up were included. According to HCA, three clusters were identified as the optimal solution. Using prespecified clinical and laboratory variables, Gower’s distance was calculated, and three distinct clusters were formed (Figure-1A). Cluster A comprised younger patients (55±14 years) with higher hematocrit and LDL levels, whereas Cluster B included older patients (70±14 years) with higher NIHSS scores. Atrial fibrillation and hemorrhagic transformation were most frequent in Cluster B. Cluster C (65±12 years) had the highest burden of cardiovascular comorbidities, including hypertension, coronary artery disease and diabetes. The median follow-up duration was 26 months. Recurrent stroke rates were similar across clusters (Figure-1B); however, deaths were observed only in Cluster B. Conclusions Cluster analysis may identify clinically meaningful stroke subtypes, and future studies incorporating richer datasets could inform subtype-specific clinical management. Conflict of interest Mine Sezgin : Nothing to disclose, Muhammed Yusuf Cansever : Nothing to disclose, Elif Begik: Nothing to disclose, Cemal Kerem Karaduman: Nothing to disclose, Mehmet Sait Bingöl: Nothing to disclose, Ozan Alp: Nothing to disclose, Esme Ekizoğlu: Nothing to disclose, Nilüfer Yeşilot: Nothing to disclose, Mehmet Güven Günver: Nothing to disclose Figure 1 - belongs to Conclusions

European Stroke JournalVol. 11(Supplement_1)
Istanbul University (TR)
Good health and well-being
Openalex Percentile: Top 8%
Acute Ischemic Stroke Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.