Machine learning models of ischemia/hemorrhage in moyamoya disease and analysis of its risk factors

Zhongjun Chen,Lijun Xu,Haowen Luo

doi:10.1016/j.clineuro.2021.106919

Zhongjun Chen, Lijun Xu + Show 1 more

Open Access

https://doi.org/10.1016/j.clineuro.2021.106919

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Abstract

ObjectThis study aimed to determine the risk factors of ischemic/hemorrhagic stroke in patients suffering moyamoya disease (MMD), as well as to compare the effects of six analysis methods. MethodsIn the present retrospective study, the data originated from the database of Jiang Xi Province Medical Big Data Engineering & Technology Research Center. In addition, the information of patients with MMD that were admitted to the second affiliated hospital of Nanchang university from January 1st, 2012 to December 31st, 2019 was acquired. Six different machine learning methods were adopted to build the models, and XGboost, Logistic regression (LR) and Support vector machine (SVM) models were adopted to determine the risk factors of ischemic/hemorrhagic stroke in patients with MMD because of their excellent performance. Next, the effects of the built models were compared and validated in internal and independent external validation sets. The external validation set involving 204 cases from January 1st, 2018 to December 31st, 2019. ResultOn the whole, 790 patients with MMD were screened, i.e., 397 patients with cerebral infarction and 393 patients with cerebral hemorrhage. In the internal validation set, XGboost model exhibited significant discrimination (AUC>0.75), with its area under the curve (AUC) reaching 0.874 (95% CI: 0.859, 0.889). Compared with the LR and SVM models, the XGboost model in the internal validation set achieved the improved accuracy by 3.2% and 3.1%, respectively, whereas no significant difference was identified. ConclusionXGboost model could be more efficient in analyzing the risk factors of ischemic/hemorrhagic stroke in patients with MMD; the risk factors of hemorrhagic stroke in MMD might be closely related to Suzuki stages, presence of an aneurysm, rural residence, hospitalization times and age of onset.

Highlights

Moyamoya disease (MMD) is a rare cerebrovascular disease characterized by chronic progressive stenosis or occlusion at the onset of bilateral internal carotid arteries, anterior cerebral arteries and middle cerebral arteries, and abnormal vascular proliferation
In terms of the accuracy of the models, XGboost model is better than Logistic regression (LR) and Multilayer Perceptron (MLP) models in the training set, and there is a significant difference; in the test set, XGboost model is better than LR and MLP models, and there is a significant difference with LR model
In terms of Net Reclassification Index (NRI), an important index to evaluate the prediction accuracy of the model, XGboost model is superior to LR and MLP model in training set with significant difference, and its prediction ability is improved by 18.11% and 3.77% respectively; XGboost model is superior to LR and MLP models in the test set, and with significant difference compared with LR model, but no significant difference with MLP model, and its prediction ability is improved by 11.07% and 3.06% respectively; The prediction ability of MLP model in training set and test set is better than LR model, and there is a significant difference; Compared with LR model, the prediction ability of MLP model in training set and test set is improved by 18.11% and 8.01% respectively

Summary

Introduction

Moyamoya disease (MMD) is a rare cerebrovascular disease characterized by chronic progressive stenosis or occlusion at the onset of bilateral internal carotid arteries, anterior cerebral arteries and middle cerebral arteries, and abnormal vascular proliferation. The etiology is not clear, and no clear pathogen has been detected; it is considered that multiple factors lead to the occurrence of moyamoya disease. Related studies report that the prevalence of familial MMD in China is 1.5% [4]. Vascular injury has been proved in autopsy reports of moyamoya disease patients [5]. Studies have shown that hyperhomocysteinemia, hypertension and smoking are the risk factors of vascular injury in patients with moyamoya disease [6]

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