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Ischemic Stroke ll: Pathophysiology

Ischemic Stroke ll: Pathophysiology

An ischemic stroke occurs when a cerebral blood vessel becomes obstructed, most often by a thrombus or embolus, interrupting the delivery of oxygen and glucose to brain tissue. Because neurons rely on continuous aerobic metabolism, energy failure begins within minutes of reduced perfusion. The region receiving the least blood flow becomes the infarct core, an area of irreversible cellular death. Surrounding this core lies the penumbra, a zone of hypoperfused but still viable tissue that is...

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A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia

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Hybrid Clot Histomic-Transcriptomic Models Predict Functional Outcome After Mechanical Thrombectomy in Acute Ischemic

Briana A Santo^1,2,3, Kerry E Poppenberg^1,2, Shiau-Sing K Ciecierska¹

¹Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo , New York , USA.

|December 5, 2024

Summary

Combining clot histomics and mRNA expression significantly improves prediction of stroke recovery. These hybrid models offer better prognostication for early neurological improvement and long-term outcomes after stroke.

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Area of Science:

Computational pathology
Genomics
Neurology

Background:

Stroke clot analysis reveals features linked to patient outcomes.
Previous studies analyzed histology or gene expression separately.
Few studies integrated both histomic and transcriptomic data for stroke prognostication.

Purpose of the Study:

To investigate if combined clot histomics and mRNA expression predict early neurological improvement (ENI) and 90-day functional outcome (modified Rankin Scale Score, mRS).
To compare the predictive performance of hybrid models against models using only histology or transcriptomics.

Main Methods:

Paired histological and transcriptomic analysis of 32 stroke clots.
Extraction of 237 histomic features and mRNA expression profiling.
Machine learning (recursive feature elimination) to build predictive models for ENI and mRS.

Main Results:

Hybrid models integrating histomic and transcriptomic features achieved high accuracy (90.8% for ENI, 93.7% for mRS).
These models outperformed those based on histomics, transcriptomics, or clot composition alone.
Optimal models identified 9 features for ENI and 7 for mRS.

Conclusions:

Hybrid computational models enhance stroke outcome prognostication.
Digital histology and mRNA signatures show potential as biomarkers for post-thrombectomy functional outcomes.