Factors Influencing Stroke Severity Based on Collateral Circulation, Clinical Markers and Machine Learning

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.