Multidimensional clustering of clinical, metabolic, and ultrasound features for 30-day mortality risk stratification in ischemic stroke patients with carotid atherosclerotic plaque.

Background: Carotid atherosclerotic plaques are a major cause of ischemic stroke, but stenosis-based assessment does not fully capture plaque heterogeneity or prognostic risk. This study aimed to explore data-driven patient phenotypes and preliminary risk stratification by integrating clinical, metabolic, and carotid ultrasound plaque features using unsupervised clustering. Methods: Ischemic stroke patients admitted within 72 h of onset were retrospectively enrolled from four centers. Baseline demographics, National Institutes of Health Stroke Scale scores, laboratory indicators, carotid ultrasound plaque characteristics (for example, stenosis rate, Plaque Reporting and Data System grade), and 30-day mortality were extracted. After preprocessing and standardization, Results: Among 3,764 patients, five exploratory clusters were identified, showing marked heterogeneity in age, neurological severity, metabolic profile, and plaque characteristics. Cluster 0 (29.2%) comprised the youngest patients, with the lowest National Institutes of Health Stroke Scale scores, smallest plaques, and lowest 30-day mortality (2.9%), whereas Cluster 4 (13.6%) had the highest stenosis rates, more unstable plaque features, pronounced metabolic abnormalities, and the highest mortality (11.2%). Overall mortality differences were significant (chi-squared = 12.36, Conclusion: Integrating multidimensional clinical, metabolic, and ultrasound features with