Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning - Liu, Xiaoqing; Wang, Miaoran; Wen, Rui; Zhu, Haoyue; Xiao, Ying; He, Qian; Shi, Yangdi; Hong, Zhe; Xu, Bing (Frontiers in Pharmacology, 2025)
This cohort study aimed to evaluate the prognostic outcomes of patients with acute ischemic stroke (AIS) and diabetes mellitus following intravenous thrombolysis, utilizing machine learning techniques. The analysis was conducted using data from Shenyang First People’s Hospital, involving 3,478 AIS patients with diabetes who received thrombolytic therapy from January 2018 to December 2023, ultimately focusing on 1,314 patients after screening. The primary outcome measured was the 90-day Modified Rankin Scale (MRS). An 80/20 train-test split was implemented for model development and validation, employing various machine learning classifiers, including artificial neural networks (ANN), random forest (RF), XGBoost (XGB), and LASSO regression. Results indicated that the average accuracy of the XGB model was 0.7355 (±0.0307), outperforming the other models. Key predictors for prognosis post-thrombolysis included the National Institutes of Health Stroke Scale (NIHSS) and blood platelet count. The findings underscore the effectiveness of machine learning algorithms, particularly XGB, in predicting functional outcomes in diabetic AIS patients, providing clinicians with a valuable tool for treatment planning and improving patient outcome predictions based on receiver operating characteristic (ROC) analysis and accuracy assessments.