Data_Sheet_3_Machine learning-based prediction of symptomatic intracerebral hemorrhage after intravenous thrombolysis for stroke: a large multicenter study.PDF

BackgroundThis study aimed to compare the performance of different machine learning models in predicting symptomatic intracranial hemorrhage (sICH) after thrombolysis treatment for ischemic stroke.MethodsThis multicenter study utilized the Shenyang Stroke Emergency Map database, comprising 8,924 acute ischemic stroke patients from 29 comprehensive hospitals who underwent thrombolysis between January 2019 and December 2021. An independent testing cohort was further established, including 1,921 patients from the First People’s Hospital of Shenyang. The structured dataset encompassed 15 variables, including clinical and therapeutic metrics. The primary outcome was the sICH occurrence post-thrombolysis. Models were developed using an 80/20 split for training and internal validation. Performance was assessed using machine learning classifiers, including logistic regression with lasso regularization, support vector machine (SVM), random forest, gradient-boosted decision tree (GBDT), and multilayer perceptron (MLP). The model boasting the highest area under the curve (AUC) was specifically employed to highlight feature importance.ResultsBaseline characteristics were compared between the training cohort (n = 6,369) and the external validation cohort (n = 1,921), with the sICH incidence being slightly higher in the training cohort (1.6%) compared to the validation cohort (1.1%). Among the evaluated models, the logistic regression with lasso regularization achieved the highest AUC of 0.87 (95% confidence interval [CI]: 0.79–0.95; p

背景：本研究旨在比较不同机器学习模型在预测缺血性卒中血栓溶解治疗后出现症状性颅内出血（sICH）方面的性能。方法：本研究采用沈阳卒中紧急地图数据库，该数据库包含自2019年1月至2021年12月期间在29家综合医院接受血栓溶解治疗的8,924例急性缺血性卒中患者。此外，还建立了一个独立的测试队列，包括来自沈阳市第一人民医院的1,921名患者。结构化数据集包括15个变量，涵盖了临床和治疗方案。主要结局为血栓溶解后sICH的发生率。模型采用80/20的划分进行训练和内部验证。性能评估采用机器学习分类器，包括具有lasso正则化的逻辑回归、支持向量机（SVM）、随机森林、梯度提升决策树（GBDT）和多层感知器（MLP）。具有最高曲线下面积（AUC）的模型被特别选用以突出特征的重要性。结果：在训练队列（n=6,369）和外部验证队列（n=1,921）之间比较了基线特征，与验证队列（1.1%）相比，训练队列的sICH发生率略高（1.6%）。在评估的模型中，具有lasso正则化的逻辑回归实现了最高的AUC值0.87（95%置信区间[CI]: 0.79–0.95；p