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Related Concept Videos

Regulation of Stroke Volume01:27

Regulation of Stroke Volume

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The regulation of stroke volume, which is the amount of blood the heart pumps out during each heartbeat, is critical for maintaining a healthy circulatory system. Stroke volume is influenced by three main factors: preload, contractility, and afterload.
Preload refers to the degree of stretch on the heart before it contracts. It's analogous to the stretching of a rubber band; the more it's stretched, the more forcefully it snaps back. This concept is encapsulated in the Frank-Starling law of the...
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Factors Influencing Stroke Severity Based on Collateral Circulation, Clinical Markers and Machine Learning.

Jia-Lang Xu1

  • 1Department of Applied Statistics, National Taichung University of Science and Technology, Taichung 404336, Taiwan.

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|December 11, 2025
PubMed
Summary
This summary is machine-generated.

Stroke severity is significantly influenced by collateral circulation and stroke laterality. Machine learning models, particularly tree-based ensembles, accurately predict stroke severity using clinical and imaging data, aiding personalized patient care.

Keywords:
Clinical Decision Support SystemsXGBoostlateral branch circulationrandom foreststroke prognosis

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Area of Science:

  • Neurology
  • Medical Imaging
  • Machine Learning

Background:

  • Stroke is a leading cause of disability, with severity varying based on multiple factors.
  • Understanding stroke severity determinants is crucial for patient outcomes and treatment planning.
  • Collateral circulation plays a significant, yet often understudied, role in stroke severity.

Purpose of the Study:

  • To identify and analyze key variables influencing stroke severity.
  • To investigate the specific role of collateral circulation in determining stroke severity.
  • To evaluate the predictive performance of machine learning models for stroke severity.

Main Methods:

  • Analysis of clinical (SBP, FPG, BUN), imaging (ipsilateral collateral flow, unilateral-bilateral stroke), and biochemical data.
  • Application of statistical tests (chi-square, Mann-Whitney U) for group comparisons.
  • Utilized SMOTE for class imbalance, followed by cross-validation of Logistic Regression, Random Forest, XGBoost, and SVM models.

Main Results:

  • Reduced or absent ipsilateral collateral flow and unilateral-bilateral stroke were strongly linked to increased severity (p < 0.001).
  • Systolic blood pressure (SBP) and fasting plasma glucose (FPG) showed significant associations with stroke severity.
  • Random Forest and XGBoost models, trained on SMOTE-balanced data, demonstrated high predictive accuracy (83.3% and 80.2% respectively).

Conclusions:

  • Collateral status and stroke laterality are primary determinants of stroke severity.
  • SBP and FPG provide additional prognostic value, while BUN is borderline significant.
  • Tree-based ensemble models trained with SMOTE offer reliable stroke severity prediction for risk stratification and personalized care planning.