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Small vessel disease burden predicts functional outcomes in patients with acute ischemic stroke using machine learning

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收录情况： ◇ SCIE

作者：
Wang Xueyang[1,2] * ; Lyu Jinhao[1,2] ; Meng Zhihua[3] ; Wu Xiaoyan[4] ; Chen Wen[5] ; Wang Guohua[6] ; Niu Qingliang[7] ; Li Xin[8] ; Bian Yitong[9] ; Han Dan[10] ; Guo Weiting[11] ; Yang Shuai[12] ; Bian Xiangbing[2] ; Lan Yina[2] ; Wang Liuxian[2] ; Duan Qi[2] ; Zhang Tingyang[2] ; Duan Caohui[2] ; Tian Chenglin[13] ; Chen Ling[1,14,*1] ; Lou Xin[1,2,*1] ;
机构： [1]Medical School of Chinese PLA, Beijing, China [2]Department of Radiology, Chinese PLA General Hospital, Beijing, China [3]Department of Radiology, Yuebei People's Hospital, Guangdong, China [4]Department of Radiology, Anshan Changda Hospital, Liaoning, China [5]Department of Radiology, Shiyan Taihe Hospital, Hubei, China [6]Department of Radiology, Qingdao Municipal Hospital Affiliated to Qingdao University, Qingdao, China [7]Department of Radiology, WeiFang Traditional Chinese Hospital, Shandong, China [8]Department of Radiology, The Second Hospital of Jilin university, Jilin, China [9]Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, China [10]Department of Radiology, The First Affiliated Hospital of Kunming Medical University, Yunnan, China [11]Department of Radiology, Shanxi Provincial People's Hospital, Shanxi, China [12]Department of Radiology, Xiangya Hospital Central South University, Hunan, China [13]Department of Neurology, Chinese PLA General Hospital, Beijing, China [14]Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China
出处：
DOI： 10.1111/cns.14071
ISSN：

关键词： acute ischemic stroke cerebral small vessel disease machine learning prediction model

摘要：
Our purpose is to assess the role of cerebral small vessel disease (SVD) in prediction models in patients with different subtypes of acute ischemic stroke (AIS).We enrolled 398 small-vessel occlusion (SVO) and 175 large artery atherosclerosis (LAA) AIS patients. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. MRI was performed to assess white matter hyperintensity (WMH), perivascular space (PVS), lacune, and cerebral microbleed (CMB). Logistic regression (LR) and machine learning (ML) were used to develop predictive models to assess the influences of SVD on the prognosis.In the feature evaluation of SVO-AIS for different outcomes, the modified total SVD score (Gain: 0.38, 0.28) has the maximum weight, and periventricular WMH (Gain: 0.07, 0.09) was more important than deep WMH (Gain: 0.01, 0.01) in prognosis. In SVO-AIS, SVD performed better than regular clinical data, which is the opposite of LAA-AIS. Among all models, eXtreme gradient boosting (XGBoost) method with optimal index (OI) has the best performance to predict excellent outcome in SVO-AIS. [0.91 (0.84-0.97)].Our results revealed that different SVD markers had distinct prognostic weights in AIS patients, and SVD burden alone may accurately predict the SVO-AIS patients' prognosis.© 2023 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.

基金：
National Natural Science Foundation of China, Grant/Award Number: 81730048, 81825012 and 81901708
语种：
外文
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出版当年[2024]版：
无
最新[2023]版：
大类 | 1 区 医学
小类 | 2 区 神经科学 2 区 药学
JCR分区：
出版当年[2023]版：
Q1 PHARMACOLOGY & PHARMACY Q1 NEUROSCIENCES
最新[2023]版：
Q1 PHARMACOLOGY & PHARMACY Q1 NEUROSCIENCES

影响因子： 最新[2023版] 最新五年平均 出版当年[2023版] 出版当年五年平均 出版前一年[2022版]

第一作者：
第一作者机构： [1]Medical School of Chinese PLA, Beijing, China [2]Department of Radiology, Chinese PLA General Hospital, Beijing, China
通讯作者：
通讯机构： [1]Medical School of Chinese PLA, Beijing, China [2]Department of Radiology, Chinese PLA General Hospital, Beijing, China [14]Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China [*1]Medical School of Chinese PLA, Department of Radiology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing 100853, China.
推荐引用方式(GB/T 7714)：
Wang Xueyang,Lyu Jinhao,Meng Zhihua,et al.Small vessel disease burden predicts functional outcomes in patients with acute ischemic stroke using machine learning[J].CNS NEUROSCIENCE & THERAPEUTICS.2023,29(4):1024-1033.doi:10.1111/cns.14071.
APA：
Wang Xueyang,Lyu Jinhao,Meng Zhihua,Wu Xiaoyan,Chen Wen...&Lou Xin.(2023).Small vessel disease burden predicts functional outcomes in patients with acute ischemic stroke using machine learning.CNS NEUROSCIENCE & THERAPEUTICS,29,(4)
MLA：
Wang Xueyang,et al."Small vessel disease burden predicts functional outcomes in patients with acute ischemic stroke using machine learning".CNS NEUROSCIENCE & THERAPEUTICS 29..4(2023):1024-1033

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