• J Eval Clin Pract · Jul 2024

    Prognosticating global functional outcome in the recurrent ischemic stroke using baseline clinical and pre-clinical features: A machine learning study.

    • Tran Nhat Phong Dao, Hien Nguyen Thanh Dang, My Thi Kim Pham, Hien Thi Nguyen, Tran ChiCuongCCan Tho Stroke International Services (S.I.S) General Hospital, Can Tho, Vietnam., and LeMinh VanMV0009-0000-7752-9066Department of Neurology, Faculty of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, Vietnam.Department of Neurology, Can Tho University of Medicine and Pharmacy Hospital, Can Tho, Vietnam.Department of Neurol.
    • Faculty of Traditional Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, Vietnam.
    • J Eval Clin Pract. 2024 Jul 19.

    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.© 2024 John Wiley & Sons Ltd.

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