老年病人缺血性脑卒中后衰弱预测模型的前瞻性研究OA

Objective:To investigate the influencing factors of frailty after ischemic stroke in elderly patients and construct frailty risk decision tree prediction model.Methods:A total of 485 elderly patients with ischemic stroke admitted to the department of neurology in a third grade A hospital in Qingdao from July 2022 to April 2023.Among them,441 patients completed follow-up and were divided into a frailty group(134 cases)and a non-frailty group(307 cases)according to whether frailty occurred within 3 months after discharge.Univariate analysis and Logistic regression were used to analyze the influencing factors of frailty in elderly patients with ischemic stroke.Python software was used to construct a decision tree model for predicting frailty in elderly patients with ischemic stroke.Results:Stroke frequency(OR=5.899),National Institutes of Health Stroke Scale(NIHSS)score(OR=2.150),geriatric depression(OR=1.673),grip strength(OR=0.921),Barthel index(OR=0.954),general self-efficacy(OR=0.779),social support(OR=0.860)were the important factors affecting the debilitation of elderly patients with ischemic stroke.The results of CART decision tree model showed that NIHSS score,geriatric depression,grip strength,general self-efficacy and social support were predictive factors of occurrence of frailty in elderly patients with ischemic stroke.The top three variables with high contribution to the model were NIHSS score(45.342％),geriatric depression(26.124％)and grip strength(15.297％).The performance of the decision tree model is excellent,and the area under the ROC curve on the training set and the test set reach 0.94 and 0.92 respectively.Conclusions:CART decision tree model can easily and accurately screen out high-risk patients with frailty,which can provide basis for formulating targeted intervention measures to avoid frailty in elderly patients with ischemic stroke.