Data_Sheet_1_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

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症状性颅内出血（sICH）方面的性能。方法：本研究采用多中心研究设计，利用沈阳市卒中紧急地图数据库，该数据库包含了2019年1月至2021年12月期间29家综合性医院中进行的8,924例急性缺血性卒中患者的血栓溶解治疗数据。进一步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。结构化数据集包含了15个变量，包括临床和治疗指标。主要结局是血栓溶解后的sICH发生率。模型开发采用了80/20的划分进行训练和内部验证。性能评估使用了机器学习分类器，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）。在评估的模型中，具有最高曲线下面积（AUC）的模型被用于突出特征重要性。结果：训练队列（n=6,369）与外部验证队列（n=1,921）之间的基线特征进行了比较，训练队列中的sICH发生率（1.6%）略高于验证队列（1.1%）。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为0.87（95%置信区间[CI]: 0.79–0.95；p...