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

• Published in: CNS neuroscience & therapeutics Vol. 29; no. 4; pp. 1024 - 1033
• Main Authors: Wang, Xueyang, Lyu, Jinhao, Meng, Zhihua, Wu, Xiaoyan, Chen, Wen, Wang, Guohua, Niu, Qingliang, Li, Xin, Bian, Yitong, Han, Dan, Guo, Weiting, Yang, Shuai, Bian, Xiangbing, Lan, Yina, Wang, Liuxian, Duan, Qi, Zhang, Tingyang, Duan, Caohui, Tian, Chenglin, Chen, Ling, Lou, Xin
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01-04-2023; John Wiley and Sons Inc
• Subjects: acute ischemic stroke; Algorithms; Arteriosclerosis; Atherosclerosis; Cardiac arrhythmia; Cardiovascular disease; Cerebral blood flow; cerebral small vessel disease; Cerebral Small Vessel Diseases - complications; Cerebral Small Vessel Diseases - diagnostic imaging; Cost of Illness; Decision trees; Hospitals; Humans; Hyperglycemia; Hyperlipidemia; Hypertension; Ischemia; Ischemic Stroke - diagnostic imaging; Ischemic Stroke - therapy; Learning algorithms; Machine Learning; Magnetic resonance imaging; Medical imaging; Methods; Neuroimaging; Original; prediction model; Prediction models; Prognosis; Software; Stroke; Stroke - diagnostic imaging; Stroke - therapy; Substantia alba; Vascular diseases; Veins & arteries; cerebral small vessel disease; prediction model; machine learning; acute ischemic stroke
• ISSN: 1755-5930; 1755-5949
• DOI: 10.1111/cns.14071

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.
Characteristics of cerebral small vessel disease (CSVD) in acute ischemic stroke (AIS) patients can affect outcomes at 90 days. Meanwhile, different imaging markers of CSVD have different weights of impact on large artery atherosclerosis and small vessel occlusion subtype AIS.