Prediction of ischemic stroke in patients with H-type hypertension based on biomarker
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 pat...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83662-3 |
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| author | Ke Chen Jianxun He Lan Fu Xiaohua Song Ning Cao Hui Yuan |
| author_facet | Ke Chen Jianxun He Lan Fu Xiaohua Song Ning Cao Hui Yuan |
| author_sort | Ke Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f7acffc8bab74a1399c5d117198b9c71 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f7acffc8bab74a1399c5d117198b9c712025-01-12T12:16:32ZengNature PortfolioScientific Reports2045-23222025-01-0115111010.1038/s41598-024-83662-3Prediction of ischemic stroke in patients with H-type hypertension based on biomarkerKe Chen0Jianxun He1Lan Fu2Xiaohua Song3Ning Cao4Hui Yuan5Department of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical UniversityPhysical Examination Center, Beijing Anzhen Hospital, Capital Medical UniversityPhysical Examination Center, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Clinical Laboratory, China-Japan Friendship HospitalDepartment of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical UniversityAbstract 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.https://doi.org/10.1038/s41598-024-83662-3H-type hypertensionIschemic strokePrediction model |
| spellingShingle | Ke Chen Jianxun He Lan Fu Xiaohua Song Ning Cao Hui Yuan Prediction of ischemic stroke in patients with H-type hypertension based on biomarker Scientific Reports H-type hypertension Ischemic stroke Prediction model |
| title | Prediction of ischemic stroke in patients with H-type hypertension based on biomarker |
| title_full | Prediction of ischemic stroke in patients with H-type hypertension based on biomarker |
| title_fullStr | Prediction of ischemic stroke in patients with H-type hypertension based on biomarker |
| title_full_unstemmed | Prediction of ischemic stroke in patients with H-type hypertension based on biomarker |
| title_short | Prediction of ischemic stroke in patients with H-type hypertension based on biomarker |
| title_sort | prediction of ischemic stroke in patients with h type hypertension based on biomarker |
| topic | H-type hypertension Ischemic stroke Prediction model |
| url | https://doi.org/10.1038/s41598-024-83662-3 |
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