Objective To explore the role and value of thromboelastography(TEG)combined with white matter hyperintensity(WMH)in predicting the functional prognosis of patients with non-cardiogenic acute ischemic stroke(AIS)through machine learning.Methods This study included 130 patients with non-cardiogenic AIS from August 2022 to February 2024.General clinical data,TEG and WMH information of all patients were collected.Three months later,functional outcomes were followed up using the modified Rankin scale(mRS),with an mRS score of≥2 indicating a poor prognosis.The prediction models were divided into four feature sets according to different ranges of predictors:set A(general clinical data+TEG indicators+WMH score),set B(general clinical data+TEG indicators),set C(general clinical data+WMH score),and set D(general clinical data).For each feature set,three machine learning algorithms,traditional logistic regression(LR)model,random forests(RF),neural network(NNET),and K-nearest neighbors(KNN),were used to construct models for predicting the 3-month neurological function outcome of patients with non-cardiogenic AIS.Bootstrap resampling internal validation was used to compare the performance of prediction models.Results The training and testing of the model were performed on 130 patient samples,and the AUC value and its confidence interval of the model were corrected by the 0.632+method(optimism correction).For the LR,NNET,and KNN models,the corrected AUC values of feature set A were significantly better than those of feature set D(DeLong test,P<0.05).For all models,the corrected AUC value of feature set A was higher than that of other feature sets.For feature set A,the corrected AUC value(0.830)of the NNET model was higher than that of other models.Among the 19 features of feature set A,six features with important associations with functional prognosis were selected including National Institute of Health stroke scale(NIHSS)score,stroke history,small artery occlusion subtype,periventricular white matter hyperintensities(PWMH)score,and TEG indicators maximum amplitude(MA)and LY30.Conclusion Combining TEG indicators and WMH information on the basis of general clinical data can significantly improve the accuracy of predicting poor functional prognosis in patients with non-cardiogenic AIS.The prediction models established by machine learning-based NNET and KNN algorithms have high predictive value.

Objective To explore the role and value of thromboelastography(TEG)combined with white matter hyperintensity(WMH)in predicting the functional prognosis of patients with non-cardiogenic acute ischemic stroke(AIS)through machine learning.Methods This study included 130 patients with non-cardiogenic AIS from August 2022 to February 2024.General clinical data,TEG and WMH information of all patients were collected.Three months later,functional outcomes were followed up using the modified Rankin scale(mRS),with an mRS score of≥2 indicating a poor prognosis.The prediction models were divided into four feature sets according to different ranges of predictors:set A(general clinical data+TEG indicators+WMH score),set B(general clinical data+TEG indicators),set C(general clinical data+WMH score),and set D(general clinical data).For each feature set,three machine learning algorithms,traditional logistic regression(LR)model,random forests(RF),neural network(NNET),and K-nearest neighbors(KNN),were used to construct models for predicting the 3-month neurological function outcome of patients with non-cardiogenic AIS.Bootstrap resampling internal validation was used to compare the performance of prediction models.Results The training and testing of the model were performed on 130 patient samples,and the AUC value and its confidence interval of the model were corrected by the 0.632+method(optimism correction).For the LR,NNET,and KNN models,the corrected AUC values of feature set A were significantly better than those of feature set D(DeLong test,P<0.05).For all models,the corrected AUC value of feature set A was higher than that of other feature sets.For feature set A,the corrected AUC value(0.830)of the NNET model was higher than that of other models.Among the 19 features of feature set A,six features with important associations with functional prognosis were selected including National Institute of Health stroke scale(NIHSS)score,stroke history,small artery occlusion subtype,periventricular white matter hyperintensities(PWMH)score,and TEG indicators maximum amplitude(MA)and LY30.Conclusion Combining TEG indicators and WMH information on the basis of general clinical data can significantly improve the accuracy of predicting poor functional prognosis in patients with non-cardiogenic AIS.The prediction models established by machine learning-based NNET and KNN algorithms have high predictive value.