Objective To explore the predictive value of Logistic regression model and decision tree model for poor prognosis of large area acute insular infarction.Methods Clinical data of 100 patients with large area acute insular infarction admitted from January 2019 to December 2022 were retrospectively analyzed.According to the modified Rankin scale at 3 months after the onset,they were divided into good prognosis group and poor prognosis group.Baseline data such as clinical data,laboratory indicators and disease history were collected.Logistic regression model and decision tree model were construc-ted to analyze the influencing factors of poor prognosis.Receiver operating characteristic(ROC)curve was drawn to analyze the predictive efficacy of the two models for poor prognosis.Results Three patients were lost to follow-up at 3 months after disease onset,and the incidence of poor prognosis was 39.18%(38/97),and good prognosis was 60.82%(59/97).Logis-tic regression analysis showed that age,atrial fibrillation,baseline troponin T(cTnT),irisin and Occludin were influencing factors of poor prognosis(P<0.01).Decision tree model analysis showed that age,atrial fibrillation,baseline cTnT and Oc-cludin were influencing factors of poor prognosis.The are under the ROC curve(AUC)of decision tree model in predicting poor prognosis was greater than that of Logistic regression model.Conclusion Age,atrial fibrillation,baseline cTnT,and Occludin are poor prognostic factors for patients with large area acute insular infarction,and the Logistic regression model and decision tree model constructed based on the above factors have good application value.Therefore,the advantages of the two models should be combined to provide a new idea for clinical diagnosis and treatment.
关键词
急性岛叶梗死/决策树模型/Logistic回归/临床转归/预测/心房颤动/肌钙蛋白T/影响因素
Key words
Acute insular infarction/Decision tree model/Logistic regression/Clinical outcome/Prediction/Atrial fibrillation/Troponin T/Influencing factor
维普
万方数据