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Establishment and validation of a risk stratification model for stroke risk within three years in patients with
1Department of Radiology, Cardio-Cerebrovascular Disease Hospital, Affiliated Hospital of Yan' an University, Yan' an City, Shaanxi Province 716000, China.
Insights
This study developed a risk model for stroke in cerebral small vessel disease (CSVD) patients. Combining MRI data and machine learning accurately predicts stroke risk within three years.
Area of Science:
- Neurology
- Medical Imaging
- Artificial Intelligence
Background:
- Cerebral small vessel disease (CSVD) is a primary cause of stroke, especially in older adults, leading to high morbidity and mortality.
- Early identification of high-risk CSVD patients is crucial for effective stroke prevention and treatment strategies.
Purpose of the Study:
- To develop and validate a risk stratification model for predicting stroke occurrence within three years in CSVD patients.
- To integrate Magnetic Resonance Imaging (MRI) parameters with machine learning algorithms for enhanced stroke risk prediction.
Main Methods:
- Utilized demographic, clinical, biochemical, and MRI-derived data for patient assessment.
- Employed correlation analysis, logistic regression, ROC curve analysis, and a neural network algorithm (nnet) for predictive modeling.
- Evaluated the predictive performance of individual MRI parameters and combined machine learning models.
Main Results:
- MRI parameters (WMH volume, perfusion deficit, ischemic core, microbleeds, perivascular spaces) strongly correlated with stroke risk (P < 0.001).
- MRI parameters showed high sensitivity (0.719-0.906), specificity (0.704-0.877), and AUC (0.815-0.871).
- The combined model achieved an AUC of 0.925, demonstrating high accuracy in predicting 3-year stroke risk in CSVD patients.
Conclusions:
- An integrated risk stratification model combining machine learning and MRI parameters shows significant predictive power for 3-year stroke risk in CSVD patients.
- This model provides valuable insights for personalized interventions and clinical decision-making in CSVD management.
Background:
Cerebral small vessel disease (CSVD) is a major cause of stroke, particularly in the elderly population, leading to significant morbidity and mortality. Accurate identification of high-risk patients and timing of stroke occurrence plays a crucial role in patient prevention and treatment. The study aimed to establish and validate a risk stratification model for stroke within three years in patients with CSVD using a combined MRI and machine learning algorithm approach.
Methods:
The assessment encompassed demographic, clinical, biochemical, and MRI-derived parameters. Correlation analysis, logistic regression, receiver operating characteristic (ROC) curve analysis, and nnet neural network algorithm were employed to evaluate the predictive value of machine learning algorithms and MRI parameters for stroke occurrence within 3 years in patients with CSVD.
Results:
MRI-derived parameters, including average WMH volume, perfusion deficit volume, ischemic core volume, microbleed count, and perivascular spaces, exhibited strong correlations with stroke occurrence (P < 0.001). MRI-derived parameters demonstrated high sensitivities (0.719 to 0.906), specificities (0.704 to 0.877), and AUC values (0.815 to 0.871). The combined model of machine learning algorithms and MRI parameters yielded an AUC value of 0.925, indicating significantly high predictive accuracy for identifying the risk of stroke within three years in CSVD patients.
Conclusion:
The integrated risk stratification model, incorporating machine learning algorithms and MRI parameters, demonstrated strong predictive potential for stroke within three years in patients with CSVD. This model offered valuable insights for personalized interventions and clinical decision-making in the management of CSVD.
