Prediction of the short-term prognosis of acute ischaemic stroke in patients with high treatment platelet reactivity using explainable machine learning

作者：Jiaming Liu, Yumeng Gu, Dongliang Wang, Xiaoshuang Xia, Xin Li · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-20763-7 · 被引用次数：2 · 研究领域：Acute Ischemic Stroke Management、Antiplatelet Therapy and Cardiovascular Diseases、Artificial Intelligence in Healthcare

The aim of this study is to establish and validate an optimal explainable prediction model based on a machine learning (ML) approach to predict the short-term prognosis in high on-treatment platelet reactivity (HTPR) individuals with acute ischaemic stroke (AIS). Using individual basic characteristics, blood test indices, and the CYP2C19 genotype, a model to predict a poor functional prognosis (modified Rankin scale score ≥ 3) was constructed based on ML models, including logistic regression, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and light gradient boosting machine. On this basis, global and local interpretability techniques were used to interpret selected ML models and explore the risk factors affecting the short-term prognosis of AIS in patients with HTPR. In this study, the performance of the model was futher evaluated through sensitivity analysis and subgroup analysis. A total of 515 AIS patients with HTPR were retrospectively enrolled, and approximately 129 (25%) had a poor outcome in the short term. Among the 6 ML models, RF performed best in discriminative ability in terms of area under the curve (0.84 [0.71-0.97]), accuracy (0.80 [0.71-0.89]), and precision (0.71 [0.61-0.81], which are far superior to the other models. Interpretability techniques showed that high levels of diastolic blood pressure, blood urea nitrogen, homocysteine, C-reactive protein, white blood cells, and CYP2C19 poor metabolizers were significant pre...