Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion
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 neurologica...
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Frontiers Media S.A.
2021-12-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2021.749599/full |
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| author | Junzhao Cui Jingyi Yang Kun Zhang Guodong Xu Ruijie Zhao Xipeng Li Luji Liu Yipu Zhu Lixia Zhou Ping Yu Lei Xu Tong Li Jing Tian Pandi Zhao Si Yuan Qisong Wang Li Guo Xiaoyun Liu Xiaoyun Liu |
| author_facet | Junzhao Cui Jingyi Yang Kun Zhang Guodong Xu Ruijie Zhao Xipeng Li Luji Liu Yipu Zhu Lixia Zhou Ping Yu Lei Xu Tong Li Jing Tian Pandi Zhao Si Yuan Qisong Wang Li Guo Xiaoyun Liu Xiaoyun Liu |
| author_sort | Junzhao Cui |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T12:53:31Z |
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| id | doaj.art-f6014f0c89214bd2b22a759e6578ce2f |
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| language | English |
| last_indexed | 2024-12-14T12:53:31Z |
| publishDate | 2021-12-01 |
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| spelling | doaj.art-f6014f0c89214bd2b22a759e6578ce2f2022-12-21T23:00:37ZengFrontiers Media S.A.Frontiers in Neurology1664-22952021-12-011210.3389/fneur.2021.749599749599Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel OcclusionJunzhao Cui0Jingyi Yang1Kun Zhang2Guodong Xu3Ruijie Zhao4Xipeng Li5Luji Liu6Yipu Zhu7Lixia Zhou8Ping Yu9Lei Xu10Tong Li11Jing Tian12Pandi Zhao13Si Yuan14Qisong Wang15Li Guo16Xiaoyun Liu17Xiaoyun Liu18Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Information Center, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Neurology, Hebei Province People's Hospital, Shijiazhuang, ChinaDepartment of Neurology, Xingtai People's Hospital, Xingtai, ChinaDepartment of Neurology, Xingtai People's Hospital, Xingtai, ChinaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Medical Iconography, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaDepartment of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, ChinaNeuroscience Research Center, Medicine and Health Institute, Hebei Medical University, Shijiazhuang, ChinaObjectives: 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.https://www.frontiersin.org/articles/10.3389/fneur.2021.749599/fullanterior circulation large vessel occlusionacute ischemic strokemachine learningprediction modelneurological impairment |
| spellingShingle | Junzhao Cui Jingyi Yang Kun Zhang Guodong Xu Ruijie Zhao Xipeng Li Luji Liu Yipu Zhu Lixia Zhou Ping Yu Lei Xu Tong Li Jing Tian Pandi Zhao Si Yuan Qisong Wang Li Guo Xiaoyun Liu Xiaoyun Liu Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion Frontiers in Neurology anterior circulation large vessel occlusion acute ischemic stroke machine learning prediction model neurological impairment |
| title | Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion |
| title_full | Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion |
| title_fullStr | Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion |
| title_full_unstemmed | Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion |
| title_short | Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion |
| title_sort | machine learning based model for predicting incidence and severity of acute ischemic stroke in anterior circulation large vessel occlusion |
| topic | anterior circulation large vessel occlusion acute ischemic stroke machine learning prediction model neurological impairment |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2021.749599/full |
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