Classification and Diagnostics of Ischemic Stroke Subtypes

Ischemic stroke is heterogeneous in aetiology, encompassing large artery atherosclerosis, small vessel occlusion and cardioembolism, among other less common causes. Precise subclassification underpins targeted secondary prevention and informs prognosis. Traditional systems such as the TOAST classification and the Causative Classification System employ clinical assessment, vascular imaging and cardiac evaluation to assign strokes to broad categories, but a substantial proportion remains cryptogenic. Advances in neuroimaging—particularly diffusion-weighted MRI and CT angiography—have refined lesion characterisation, revealing patterns of infarct topography and volume that correlate with specific aetiologies. Emerging methodologies leverage noninvasive molecular imaging to localise thrombus and identify activated platelet signatures in vivo, offering direct insight into stroke origin. Concurrently, machine learning approaches applied to electronic health records and multimodal data have demonstrated promising accuracy in predicting stroke subtype, reducing undetermined diagnoses and matching expert adjudication. Seasonality and regional variation further modulate subtype prevalence and lesion distribution, emphasising the need for context-sensitive risk stratification. Integrating clinical, imaging and computational tools is transforming the landscape of ischaemic stroke diagnostics, with global implications for personalised management and resource allocation.

Research from Nature Portfolio

A large retrospective analysis explored seasonal influences on lesion distribution in acute ischaemic stroke across four onset seasons. Winter onset was associated with a more than two-fold higher risk of bilateral and multiple infarctions, and double circulation involvement, particularly within large artery atherosclerosis and small artery occlusion subtypes. These findings suggest meteorological factors may modulate vascular vulnerability and highlight opportunities for season-tailored warning systems and preventive measures in high-risk populations.

Classification and Diagnostics of Ischemic Stroke Subtypes publication trend

The graph below shows the total number of articles in classification and diagnostics of ischemic stroke subtypes across all publications each year (not limited to Nature Index journals).

Technical terms

TOAST classification: A system for categorising ischaemic stroke by aetiology into large artery atherosclerosis, small artery occlusion, cardioembolism, other causes and undetermined.

Large artery atherosclerosis (LAA): Infarction due to stenosis or occlusion of major extracranial or intracranial arteries by atherosclerotic plaque.

Small artery occlusion (SAO): Also known as lacunar infarct; occlusion of small penetrating arteries leading to subcortical lesions.

Cardioembolism: Stroke resulting from emboli formed in the heart, frequently associated with atrial fibrillation.

Machine learning classifier: Computational algorithms that analyse patterns in clinical and imaging data to assign cases to predefined stroke subtypes.

References

1. StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records. npj Digital Medicine (2024).
2. Heterogeneity in the diagnosis and prognosis of ischemic stroke subtypes: 9-year follow-up of 22,000 cases in Chinese adults. International Journal of Stroke (2023).
3. Noninvasive In Vivo Thrombus Imaging in Patients With Ischemic Stroke or Transient Ischemic Attack—Brief Report. Arteriosclerosis Thrombosis and Vascular Biology (2023).
4. Seasonal variability of lesions distribution in acute ischemic stroke: A retrospective study. Scientific Reports (2024).
5. Causative Classification of Ischemic Stroke by the Machine Learning Algorithm Random Forests. Frontiers in Aging Neuroscience (2022).