Development of a multidimensional machine learning framework for predicting post‐stroke cognitive impairment: A prospective cohort study

作者：Aini He, Houlin Lai, Xuefan Yao, Benke Zhao, Wenjing Yan, Wei Sun, Xiao Wu, Ke Ma, Yuan Wang, Haiqing Song · 发表于：Clinical and Translational Medicine · 年份：2025 · DOI：10.1002/ctm2.70546 · 被引用次数：1 · 研究领域：Acute Ischemic Stroke Management、Dementia and Cognitive Impairment Research、Stroke Rehabilitation and Recovery

Dear Editor, Post-stroke cognitive impairment (PSCI) remains a prevalent and debilitating complication that profoundly impacts stroke survivors’ quality of life and long-term outcomes.1 Building upon our previous report that 78.7% of Chinese patients with first-ever ischemic stroke developed PSCI,2 we conducted a prospective cohort study to establish an interpretable, multidimensional prediction framework using multiple machine learning (ML) algorithms. A total of 518 acute ischemic stroke (AIS) patients were recruited at Xuanwu Hospital between January and December 2022. Following rigorous screening, 437 patients completed a 3-month cognitive follow-up using the Telephone Interview for Cognitive Status-40 (TICS-40), and 190 (43.5%) were identified as having PSCI (see Figure S1 for details). We collected 89 clinical, neuroimaging, and serological variables (see Table S1 for details). The dataset was split 8:2 into training and test sets. All preprocessing steps, namely outlier removal, imputation, and normalisation, were applied exclusively to the training set. Using 10-fold cross-validation combined with Recursive Feature Elimination (RFE), six ML algorithms, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost), were trained and optimised. Model performance was subsequently evaluated on the test set (see Supplementary Information for deta...