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Published on: August 11, 2023
Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation
Junzhao Cui1, Jingyi Yang2, Kun Zhang1
1Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
Insights
Machine learning models can predict acute ischemic stroke and neurological impairment in patients with anterior circulation large vessel occlusion (AC-LVO). These models utilize patient history and neuroimaging data for improved stroke risk assessment.
Area of Science:
- Neurology
- Medical Informatics
- Cardiovascular Diseases
Background:
- Anterior circulation large vessel occlusion (AC-LVO) poses a high risk of disabling or fatal acute ischemic stroke.
- Accurate prediction of stroke risk and severity is crucial for timely intervention.
Purpose of the Study:
- To develop and validate machine learning models for predicting acute ischemic stroke (AIS) and neurological impairment severity in AC-LVO patients.
- To assess the performance of logistic regression (LR), regularized LR (RLR), support vector machine (SVM), and random forest (RF) models.
Main Methods:
- Utilized medical history and neuroimaging data from 927 patients in a derivation cohort and 150 patients in an external validation cohort.
- Developed and compared four machine learning models (LR, RLR, SVM, RF) using 5-fold cross-validation.
- Evaluated model performance using receiver operating characteristic area under the curve (ROC-AUC).
Main Results:
- Logistic regression, regularized logistic regression, and SVM models showed significantly higher predictive performance for AIS than random forest in external validation.
- Model 1 (AIS prediction) achieved ROC-AUCs of 0.66 (LR, RLR) and 0.67 (SVM).
- No significant difference in AUC was observed among the four algorithms for predicting disabling stroke severity (Model 2).
Conclusions:
- Machine learning models incorporating clinical variables effectively predict acute ischemic stroke in AC-LVO patients.
- These models also demonstrate potential in predicting the severity of neurological impairment.
- The findings support the use of machine learning for enhanced stroke risk stratification in AC-LVO.
Abstract:
Objectives: Patients with anterior circulation large vessel occlusion are at high risk of acute ischemic stroke, which could be disabling or fatal. In this study, we applied machine learning to develop and validate two prediction models for acute ischemic stroke (Model 1) and severity of neurological impairment (Model 2), both caused by anterior circulation large vessel occlusion (AC-LVO), based on medical history and neuroimaging data of patients on admission. Methods: A total of 1,100 patients with AC- LVO from the Second Hospital of Hebei Medical University in North China were enrolled, of which 713 patients presented with acute ischemic stroke (AIS) related to AC- LVO and 387 presented with the non-acute ischemic cerebrovascular event. Among patients with the non-acute ischemic cerebrovascular events, 173 with prior stroke or TIA were excluded. Finally, 927 patients with AC-LVO were entered into the derivation cohort. In the external validation cohort, 150 patients with AC-LVO from the Hebei Province People's Hospital, including 99 patients with AIS related to AC- LVO and 51 asymptomatic AC-LVO patients, were retrospectively reviewed. We developed four machine learning models [logistic regression (LR), regularized LR (RLR), support vector machine (SVM), and random forest (RF)], whose performance was internally validated using 5-fold cross-validation. The performance of each machine learning model for the area under the receiver operating characteristic curve (ROC-AUC) was compared and the variables of each algorithm were ranked. Results: In model 1, among the included patients with AC-LVO, 713 (76.9%) and 99 (66%) suffered an acute ischemic stroke in the derivation and external validation cohorts, respectively. The ROC-AUC of LR, RLR and SVM were significantly higher than that of the RF in the external validation cohorts [0.66 (95% CI 0.57-0.74) for LR, 0.66 (95% CI 0.57-0.74) for RLR, 0.55 (95% CI 0.45-0.64) for RF and 0.67 (95% CI 0.58-0.76) for SVM]. In model 2, 254 (53.9%) and 31 (37.8%) patients suffered disabling ischemic stroke in the derivation and external validation cohorts, respectively. There was no difference in AUC among the four machine learning algorithms in the external validation cohorts. Conclusions: Machine learning methods with multiple clinical variables have the ability to predict acute ischemic stroke and the severity of neurological impairment in patients with AC-LVO.
