Longitudinal real world correlation study of blood pressure and novel features of cerebral magnetic resonance angiography by artificial intelligence analysis on elderly cognitive impairment
ObjectiveThis study aims to investigate novel clinical risk factors for cognitive impairment (CI) in elderly.MethodsA total of 3221 patients (259 patients with CI and 2,962 subjects without CI) were recruited into this nested case-control study who underwent cerebral magnetic resonance angiography (...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Aging Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2023.1121152/full |
| _version_ | 1811173486859649024 |
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| author | Shasha Sun Dongyue Liu Yanfeng Zhou Yanfeng Zhou Ge Yang Ge Yang Long-Biao Cui Xian Xu Yuanhao Guo Yuanhao Guo Ting Sun Jiacheng Jiang Na Li Yabin Wang Sulei Li Xinjiang Wang Li Fan Feng Cao |
| author_facet | Shasha Sun Dongyue Liu Yanfeng Zhou Yanfeng Zhou Ge Yang Ge Yang Long-Biao Cui Xian Xu Yuanhao Guo Yuanhao Guo Ting Sun Jiacheng Jiang Na Li Yabin Wang Sulei Li Xinjiang Wang Li Fan Feng Cao |
| author_sort | Shasha Sun |
| collection | DOAJ |
| description | ObjectiveThis study aims to investigate novel clinical risk factors for cognitive impairment (CI) in elderly.MethodsA total of 3221 patients (259 patients with CI and 2,962 subjects without CI) were recruited into this nested case-control study who underwent cerebral magnetic resonance angiography (MRA) from 2007 to 2021. All of the clinical data with MRA imaging were recorded followed by standardization processing blindly. The maximum stenosis score of the posterior circulatory artery, including the basilar artery, and bilateral posterior cerebral artery (PCA), was calculated by the cerebral MRA automatic quantitative analysis method. Logistic regression (LR) analysis was used to evaluate the relationship between risk factors and CI. Four machine learning approaches, including LR, decision tree (DT), random forest (RF), and support vector machine (SVM), employing 5-fold cross-validation were used to establish CI predictive models.ResultsAfter matching with age and gender, 208 CI patients and 208 control subjects were finalized the follow-up (3.46 ± 3.19 years) with mean age at 84.47 ± 6.50 years old. Pulse pressure (PP) in first tertile (<58 mmHg) (OR 0.588, 95% confidence interval (CI): 0.362–0.955) was associated with a decreased risk for CI, and ≥50% stenosis of the left PCA (OR 2.854, 95% CI: 1.387–5.872) was associated with an increased risk for CI after adjusting for body mass index, myocardial infarction, and stroke history. Based on the means of various blood pressure (BP) parameters, the performance of the LR, DT, RF and SVM models accurately predicted CI (AUC 0.740, 0.786, 0.762, and 0.753, respectively) after adding the stenosis score of posterior circulatory artery.ConclusionElderly with low pulse differential pressure may have lower risk for cognitive impairment. The hybrid model combined with the stenosis score of posterior circulatory artery, clinical indicators, and the means of various BP parameters can effectively predict the risk of CI in elderly individuals. |
| first_indexed | 2024-04-10T17:47:45Z |
| format | Article |
| id | doaj.art-931ab1bb5a244f56839efb4a62667465 |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-04-10T17:47:45Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-931ab1bb5a244f56839efb4a626674652023-02-03T04:41:59ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652023-02-011510.3389/fnagi.2023.11211521121152Longitudinal real world correlation study of blood pressure and novel features of cerebral magnetic resonance angiography by artificial intelligence analysis on elderly cognitive impairmentShasha Sun0Dongyue Liu1Yanfeng Zhou2Yanfeng Zhou3Ge Yang4Ge Yang5Long-Biao Cui6Xian Xu7Yuanhao Guo8Yuanhao Guo9Ting Sun10Jiacheng Jiang11Na Li12Yabin Wang13Sulei Li14Xinjiang Wang15Li Fan16Feng Cao17Department of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaDepartment of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, ChinaLaboratory of Computational Biology and Machine Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, ChinaLaboratory of Computational Biology and Machine Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaDepartment of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaDepartment of Radiology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaSchool of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, ChinaLaboratory of Computational Biology and Machine Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaDepartment of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaDepartment of Radiology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaNankai University School of Medicine, Nankai University, Tianjin, ChinaDepartment of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaDepartment of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaDepartment of Radiology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaDepartment of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaDepartment of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, ChinaObjectiveThis study aims to investigate novel clinical risk factors for cognitive impairment (CI) in elderly.MethodsA total of 3221 patients (259 patients with CI and 2,962 subjects without CI) were recruited into this nested case-control study who underwent cerebral magnetic resonance angiography (MRA) from 2007 to 2021. All of the clinical data with MRA imaging were recorded followed by standardization processing blindly. The maximum stenosis score of the posterior circulatory artery, including the basilar artery, and bilateral posterior cerebral artery (PCA), was calculated by the cerebral MRA automatic quantitative analysis method. Logistic regression (LR) analysis was used to evaluate the relationship between risk factors and CI. Four machine learning approaches, including LR, decision tree (DT), random forest (RF), and support vector machine (SVM), employing 5-fold cross-validation were used to establish CI predictive models.ResultsAfter matching with age and gender, 208 CI patients and 208 control subjects were finalized the follow-up (3.46 ± 3.19 years) with mean age at 84.47 ± 6.50 years old. Pulse pressure (PP) in first tertile (<58 mmHg) (OR 0.588, 95% confidence interval (CI): 0.362–0.955) was associated with a decreased risk for CI, and ≥50% stenosis of the left PCA (OR 2.854, 95% CI: 1.387–5.872) was associated with an increased risk for CI after adjusting for body mass index, myocardial infarction, and stroke history. Based on the means of various blood pressure (BP) parameters, the performance of the LR, DT, RF and SVM models accurately predicted CI (AUC 0.740, 0.786, 0.762, and 0.753, respectively) after adding the stenosis score of posterior circulatory artery.ConclusionElderly with low pulse differential pressure may have lower risk for cognitive impairment. The hybrid model combined with the stenosis score of posterior circulatory artery, clinical indicators, and the means of various BP parameters can effectively predict the risk of CI in elderly individuals.https://www.frontiersin.org/articles/10.3389/fnagi.2023.1121152/fullblood pressurecognitive impairmentposterior circulatory arterystenosisaging |
| spellingShingle | Shasha Sun Dongyue Liu Yanfeng Zhou Yanfeng Zhou Ge Yang Ge Yang Long-Biao Cui Xian Xu Yuanhao Guo Yuanhao Guo Ting Sun Jiacheng Jiang Na Li Yabin Wang Sulei Li Xinjiang Wang Li Fan Feng Cao Longitudinal real world correlation study of blood pressure and novel features of cerebral magnetic resonance angiography by artificial intelligence analysis on elderly cognitive impairment Frontiers in Aging Neuroscience blood pressure cognitive impairment posterior circulatory artery stenosis aging |
| title | Longitudinal real world correlation study of blood pressure and novel features of cerebral magnetic resonance angiography by artificial intelligence analysis on elderly cognitive impairment |
| title_full | Longitudinal real world correlation study of blood pressure and novel features of cerebral magnetic resonance angiography by artificial intelligence analysis on elderly cognitive impairment |
| title_fullStr | Longitudinal real world correlation study of blood pressure and novel features of cerebral magnetic resonance angiography by artificial intelligence analysis on elderly cognitive impairment |
| title_full_unstemmed | Longitudinal real world correlation study of blood pressure and novel features of cerebral magnetic resonance angiography by artificial intelligence analysis on elderly cognitive impairment |
| title_short | Longitudinal real world correlation study of blood pressure and novel features of cerebral magnetic resonance angiography by artificial intelligence analysis on elderly cognitive impairment |
| title_sort | longitudinal real world correlation study of blood pressure and novel features of cerebral magnetic resonance angiography by artificial intelligence analysis on elderly cognitive impairment |
| topic | blood pressure cognitive impairment posterior circulatory artery stenosis aging |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2023.1121152/full |
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