Background and PurposeRecurrent ischemic stroke (RIS) induces additional functional limitations in patients. Prognosticating globally functional outcome (GFO) in RIS patients is thereby important to plan a suitable rehabilitation programme. This study sought to investigate the ability of baseline features for classifying the patients with and without improving GFO (task 1) and identifying patients with poor GFO (task 2) at the third month after discharging from RIS.MethodsA total of 86 RIS patients were recruited and divided into the training set and testing set (50:50). The clinical and pre‐clinical data were recorded. The outcome was the changes in Modified Rankin Scale (mRS) (task 1) and the mRS score at the third month (mRS 0–2: good GFO, mRS >2: poor GFO) (task 2). The permutation importance ranking method selected features. Four algorithms were trained on the training set with five‐fold cross‐validation. The best model was tested on the testing set.ResultsIn task 1, the support vector machine (SVM) model outperformed the other models, with the high performance matrix on the training set (sensitivity = 0.80; specificity = 1.00) and the testing set (sensitivity = 0.80; specificity = 0.95). In task 2, the SVM model with selected features also performed well on both datasets (training set: sensitivity = 0.76; specificity = 0.92; testing set: sensitivity = 0.72; specificity = 0.88).ConclusionA machine learning model could be used to classify GFO responses to treatment and identify the third‐month poor GFO in RIS patients, supporting physicians in clinical practice.

中文翻译：

---

使用基线临床和临床前特征预测复发性缺血性中风的整体功能结果：机器学习研究


背景和目的复发性缺血性中风（RIS）会导致患者出现额外的功能限制。因此，预测 RIS 患者的整体功能结果 (GFO) 对于规划合适的康复计划非常重要。本研究旨在探讨基线特征对 RIS 出院后第三个月有或没有改善 GFO 的患者进行分类（任务 1）以及识别 GFO 较差的患者（任务 2）的能力。 方法 总共招募了 86 名 RIS 患者并分为训练集和测试集（50:50）。记录临床和临床前数据。结果是改良Rankin量表（mRS）（任务1）和第三个月的mRS评分的变化（mRS 0-2：良好的GFO，mRS > 2：较差的GFO）（任务2）。排列重要性排序方法选择特征。通过五重交叉验证在训练集上训练了四种算法。最佳模型在测试集上进行了测试。结果在任务 1 中，支持向量机 (SVM) 模型优于其他模型，训练集上的高性能矩阵（灵敏度 = 0.80；特异性 = 1.00）和测试集（灵敏度 = 0.80；特异性 = 0.95）。在任务 2 中，具有选定特征的 SVM 模型在两个数据集上也表现良好（训练集：灵敏度 = 0.76；特异性 = 0.92；测试集：灵敏度 = 0.72；特异性 = 0.88）。结论机器学习模型可用于对 GFO 进行分类对治疗的反应并确定 RIS 患者第三个月的 GFO 较差，为临床实践中的医生提供支持。