Wang Kai, Shi Qianqian, Sun Chao, Liu Wencai, Yau Vicky, Xu Chan, Liu Haiyan, Sun Chenyu, Yin Chengliang, Wei Xiu'e, Li Wenle, Rong Liangqun
Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Key Laboratory of Neurological Diseases, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Front Neurosci. 2023 Mar 27;17:1130831. doi: 10.3389/fnins.2023.1130831. eCollection 2023.
Recurrent stroke accounts for 25-30% of all preventable strokes, and this study was conducted to establish a machine learning-based clinical predictive rice idol for predicting stroke recurrence within 1 year in patients with acute ischemic stroke (AIS).
A total of 645 AIS patients at The Second Affiliated Hospital of Xuzhou Medical University were screened, included and followed up for 1 year for comprehensive clinical data. Univariate and multivariate logistic regression (LR) were used to screen the risk factors of stroke recurrence. The data set was randomly divided into training set and test set according to the ratio of 7:3, and the following six prediction models were established by machine algorithm: random forest (RF), Naive Bayes model (NBC), decision tree (DT), extreme gradient boosting (XGB), gradient boosting machine (GBM) and LR. The model with the strongest prediction performance was selected by 10-fold cross-validation and receiver operating characteristic (ROC) curves, and the models were investigated for interpretability by SHAP. Finally, the models were constructed to be visualized using a web calculator.
Logistic regression analysis showed that right hemisphere, homocysteine (HCY), C-reactive protein (CRP), and stroke severity (SS) were independent risk factors for the development of stroke recurrence in AIS patients. In 10-fold cross-validation, area under curve (AUC) ranked from 0.777 to 0.959. In ROC curve analysis, AUC ranged from 0.887 to 0.946. RF model has the best ability to predict stroke recurrence, and HCY has the largest contribution to the model. A web-based calculator https://mlmedicine-re-stroke2-re-stroke2-baylee.streamlitapp.com/ has been developed accordingly.
This study identified four independent risk factors affecting recurrence within 1 year in stroke patients, and the constructed RF-based prediction model had good performance.
复发性卒中占所有可预防卒中的25%-30%，本研究旨在建立一种基于机器学习的临床预测模型，用于预测急性缺血性卒中（AIS）患者1年内的卒中复发情况。
对徐州医科大学第二附属医院的645例AIS患者进行筛选、纳入并随访1年，获取综合临床数据。采用单因素和多因素逻辑回归（LR）筛选卒中复发的危险因素。数据集按7:3的比例随机分为训练集和测试集，通过机器学习算法建立以下六种预测模型：随机森林（RF）、朴素贝叶斯模型（NBC）、决策树（DT）、极端梯度提升（XGB）、梯度提升机（GBM）和LR。通过10倍交叉验证和受试者工作特征（ROC）曲线选择预测性能最强的模型，并通过SHAP研究模型的可解释性。最后，构建模型并使用网络计算器进行可视化。
逻辑回归分析显示，右半球、同型半胱氨酸（HCY）、C反应蛋白（CRP）和卒中严重程度（SS）是AIS患者发生卒中复发的独立危险因素。在10倍交叉验证中，曲线下面积（AUC）在0.777至0.959之间。在ROC曲线分析中，AUC在0.887至0.946之间。RF模型预测卒中复发的能力最佳，HCY对模型的贡献最大。相应地开发了一个基于网络的计算器https://mlmedicine-re-stroke2-re-stroke2-baylee.streamlitapp.com/。
本研究确定了影响卒中患者1年内复发的四个独立危险因素，构建的基于RF的预测模型具有良好的性能。
