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Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a
Yunyun Yang1,2, Lindan Huang1,2,3, Ying Gu2
1Department of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China.
Insights
A new blood test using machine learning can help diagnose stroke (cerebral infarction) and transient ischemic attack (TIA), especially where advanced imaging is unavailable. This simple test aids early detection in healthy individuals and those with metabolic disorders.
Area of Science:
- Neurology and Computational Medicine
- Biomarker Discovery for Stroke Diagnosis
Background:
- Ischemic cerebral infarction (CI) and transient ischemic attack (TIA) are leading causes of disability and death.
- Individuals with dysmetabolism (hypertension, hypercholesterolemia, diabetes) face increased stroke risk.
- Diagnosing CI and TIA is challenging in resource-limited settings due to limited advanced imaging and expertise.
Purpose of the Study:
- To develop a simple, blood-based diagnostic approach for CI and TIA.
- To assist clinicians in diagnosing CI and TIA, particularly in underserved regions.
- To differentiate between CI and TIA using accessible blood indicators.
Main Methods:
- Employed five machine learning models, including Extreme Gradient Boosting (XGBoost).
- Utilized 21 blood routine indicators, 30 blood biochemical indicators, age, and gender for model construction.
- Assessed model performance using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and employed SHAP for interpretation.
Main Results:
- XGBoost demonstrated superior performance across all predictive models.
- High AUC values (0.9958 for CI, 0.9928 for TIA) were achieved in healthy individuals.
- Models for dysmetabolic patients showed AUCs ranging from 0.6990 to 0.8591, highlighting metabolic indicators like glucose and HDL-C.
Conclusions:
- A blood-based approach using machine learning can effectively differentiate CI and TIA from healthy states and dysmetabolic conditions.
- Key predictive features include indicators of glucose, lipid, and liver metabolism.
- This cost-effective method offers a practical adjunct for early stroke diagnosis, especially where advanced resources are scarce.
Abstract:
Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability and death worldwide. Transient ischemic attack (TIA) is a strong predictor of subsequent stroke. Individuals with dysmetabolism, such as hypertension, hypercholesterolemia, and diabetes, are at increased risk for cerebral infarction (CI) and TIA. In resource-limited settings, diagnosing CI and TIA can be particularly difficult due to a lack of advanced imaging and specialized expertise. Therefore, we aim to develop a simple, convenient, blood-based approach that could assist clinicians in diagnosing CI and TIA, especially in regions where advanced imaging or stroke-specific expertise is limited. All study subjects were patients admitted to the First Hospital of Xiamen University and healthy check-up populations between January 2018 and September 2023. This study employed five machine learning methods alongside 21 blood routine indicators, 30 blood biochemical indicators, age, and gender to construct predictive models for CI and TIA in both healthy individuals and those with dysmetabolism. The Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) served as the metric to assess the comprehensive predictive capability of the models. Subsequently, the SHAP package was employed for model interpretation. Extreme Gradient Boosting (XGBoost) outperforms other models in all predictive models. In the models predicting CI and TIA among healthy, the AUC is 0.9958 (0.9947-0.9969) and 0.9928 (0.9899-0.9951), respectively. Among the nine shared key features of the two models are indicators of glucose metabolism, lipid metabolism, and liver metabolism. In the models for predicting CI and TIA among patients with hypertension, hypercholesterolemia, diabetes, and those with all three metabolic disorders combined, the AUCs ranged from 0.6990 to 0.8591. We found that the indicators K significantly contributed to predict CI and TIA from those with dysmetabolism. Additionally, metabolic-related indicators, such as glucose (GLU) and high-density lipoprotein cholesterol (HDL-C), are ranked highly among the top ten contributing features. XGBoost performed the best in all models. It can effectively differentiate CI and TIA from healthy and dysmetabolic patients by combining blood routine and blood biochemical indicators. Moreover, it can also differentiate CI from TIA. Although any suspicious findings from this model would still require confirmatory imaging, the simplicity and low cost of blood-based testing may offer a practical adjunct for clinicians-particularly in areas lacking advanced imaging or extensive stroke expertise-and could facilitate earlier diagnostic decision-making.
