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

作者：Xueyang Wang, Jinhao Lyu, Zhihua Meng, Xiaoyan Wu, Wen Chen, Guohua Wang, Qingliang Niu, Xin Li, Yitong Bian, Dan Han, Weiting Guo, Shuai Yang, Xiangbing Bian, Yina Lan, Liuxian Wang, Qi Duan, Tingyang Zhang, Caohui Duan, Chenglin Tian, Ling Chen, Xin Lou, the MR‐STARS Investigators · 发表于：CNS Neuroscience & Therapeutics · 年份：2023 · DOI：10.1111/cns.14071 · 被引用次数：20 · 研究领域：Intracerebral and Subarachnoid Hemorrhage Research、Acute Ischemic Stroke Management、Cerebrovascular and Carotid Artery Diseases

AIMS: 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). METHODS: 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. RESULTS: 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)]. CONCLUSIONS: 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.