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Prediction of ischemic stroke in patients with H-type hypertension based on biomarker
Ke Chen1, Jianxun He1, Lan Fu2
1Department of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical University, Anzhen Road No. 2, Chaoyang District, Beijing, 100029, China.
Insights
A new biomarker model accurately predicts ischemic stroke risk in patients with hyperhomocysteinemia-type hypertension. This tool aids clinicians in identifying high-risk individuals for targeted interventions.
Area of Science:
- Cardiovascular Medicine
- Neurology
- Biomarker Research
Background:
- Hypertension and hyperhomocysteinemia are significant risk factors for ischemic stroke.
- Identifying high-risk patients with Hyperhomocysteinemia-type (H-type) hypertension is crucial for stroke prevention.
Purpose of the Study:
- To develop and validate a biomarker-based prediction model for ischemic stroke in H-type hypertension patients.
- To assess the model's predictive performance using machine learning algorithms and external validation.
Main Methods:
- Retrospective analysis of 3,305 patients for model development and 103 for external validation.
- Utilized logistic regression, LASSO, best subset selection, and four machine learning algorithms.
- Evaluated model performance using Area Under Curve (AUC), calibration plots, and decision-curve analysis.
Main Results:
- The final model, A₂BC ischemic stroke model, includes 8 predictors: age, antihypertensive therapy, serum magnesium, serum potassium, proteinuria, hypersensitive C-reactive protein, atrial fibrillation, and hyperlipidemia.
- Achieved good discrimination and calibration with AUCs of 0.91 (internal) and 0.87 (external).
- Demonstrated satisfactory predictive performance in both development and validation cohorts.
Conclusions:
- The A₂BC ischemic stroke model effectively predicts ischemic stroke risk in H-type hypertension patients.
- This model can assist clinicians in accurate risk stratification and personalized management strategies.
- Biomarker and comorbidity assessment is vital for predicting ischemic stroke in this patient population.
Abstract:
Hypertension combined with hyperhomocysteinemia significantly raises the risk of ischemic stroke. Our study aimed to develop and validate a biomarker-based prediction model for ischemic stroke in Hyperhomocysteinemia-type (H-type) hypertension patients. We retrospectively included 3,305 patients in the development cohort, and externally validated in 103 patients from another cohort. Logistic regression, least absolute shrinkage and selection operator regression, and best subset selection analysis were used to assess the contribution of variables to ischemic stroke, and models were derived using four machine learning algorithms. Area Under Curve (AUC), calibration plot and decision-curve analysis respectively evaluated the discrimination and calibration of four models, then external validation and visualization of the best-performing model. There were 1,415 and 42 patients with ischemic stroke in the development and validation cohorts. The final model included 8 predictors: age, antihypertensive therapy, biomarkers (serum magnesium, serum potassium, proteinuria and hypersensitive C-reactive protein), and comorbidities (atrial fibrillation and hyperlipidemia). The optimal model, named A2BC ischemic stroke model, showed good discrimination and calibration ability for ischemic stroke with AUC of 0.91 and 0.87 in the internal and external validation cohorts. The A2BC ischemic stroke model had satisfactory predictive performances to assist clinicians in accurately identifying the risk of ischemic stroke for patients with H-type hypertension.
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