关键词: 急性缺血性卒中, 静脉溶栓, CT灌注成像, TyG指数, NLR, XGBoost, 机器学习

Abstract: Objective To explore the value of head CT perfusion imaging (CTP) parameters combined with triglyceride-glucose (TyG) index and neutrophil-to-lymphocyte ratio (NLR) in evaluating the efficacy of intravenous thrombolysis in patients with acute ischemic stroke (AIS),and to construct a prediction model based on the XGBoost algorithm. Methods A total of 140 AIS patients who received intravenous thrombolysis treatment at Hefei First People's Hospital from February 2023 to October 2025 were retrospectively selected.The patients were divided into the effective group (n=90) and the ineffective group (n=50) based on the change rate of NIHSS score 7 days after thrombolysis.Baseline clinical data and CTP parameters (CBV,CBF,MTT,TTP) of the patients were collected.LASSO regression was used to screen the predictor variables,and the XGBoost algorithm was used to construct a thrombolysis efficacy prediction model,and the importance of model features was explained by the SHAP algorithm. Results The LASSO regression identified six key predictive variables:NLR,TyG,TTP,CBV,CBF,and MTT.The AUC of the XGBoost model in the training set and validation set was 0.900 (95% CI:0.844~0.957) and 0.868 (95% CI:0.784~0.951),respectively,demonstrating excellent discrimination.The calibration curve and clinical decision curve (DCA) indicated that the model had good calibration and clinical practical value.The SHAP analysis showed that the top six feature importance rankings were:MTT,TyG,TTP,NLR,CBV,and CBF. Conclusion The machine learning model constructed based on CT perfusion parameters combined with TyG and NLR indicators and using XGBoost algorithm has shown certain predictive value in the efficacy of intravenous thrombolysis in early AIS patients,aiming to provide a reference for clinical treatment of AIS.

Key words: Acute ischemic stroke, Intravenous thrombolysis, CT perfusion imaging, TyG index, NLR, XGBoost, Machine Learning