Risk prediction model for post-stroke cognitive impairment in elderly patients:a decision curve analysis
Ren Jifeng
Tan Honglei
Wang Xiaoli
Zhao Mingmei
Wang Tingting
Mei Xiqing
Abstract：Objective To construct a risk prediction model of post-stroke cognitive impairment in elderly patients based on serum metabolic indicators and assess its predictive value by decision curve analysis.Methods From August 2019 to August 2022,297 elderly stroke patients admitted to our department were prospectively enrolled,and according to the follow-up results,294 of them were finally included and then randomly assigned into a training set(206 cases)and a verification set(88 cases)in a ratio of 3∶1.The patients in the training set were divided into cognitive impair-ment group(88 cases)and non-cognitive impairment group(118 cases)according to whether cog-nitive impairment occurred or not.Logistic regression analysis was used to analyze the influencing factors of cognitive dysfunction in elderly stroke patients,and a logistic regression prediction model was constructed.The logistic regression prediction model was visualized by drawing a no-mogram in R4.1.3.Its prediction efficiency was analyzed by ROC curve and decision curve analy-ses,and verified with the verification set.Results Multivariate logistic regression analysis showed that in the training set,TG(OR=1.266,95％CI:1.089-1.471,P=0.002),LDL-C(OR=1.321,95％CI:1.136-1.537,P=0.000),Cys C(OR=1.847,95％CI:1.421-2.401,P=0.000)and SAA(OR=1.120,95％CI:1.057-1.187,P=0.000)were independent risk factors for post-stroke cognitive impairment in elderly stroke patients.ROC curve analysis indicated that the AUC value of TG,HDL-C,Cys C and SAA for predicting cognitive dysfunction in the elderly after stroke in the training set was 0.732,0.726,0.756 and 0.736,respectively,and the AUC value of above indi-cators combined together in the prediction was 0.891.In the verification set,the AUC value of above indicators for the prediction was 0.759,0.703,0.769 and 0.756,respectively,and the AUC value of the combined indicators was 0.914.The two models had good prediction consistency.The accuracy of the combined prediction model in the training set and the verification set were 83.98％and 86.36％,respectively,and all of them were higher than the prediction of single indicator.Deci-sion curve analysis showed that the threshold probabilities of the training set and the validation set were 11％-48％and 13％-45％,respectively,which may benefit the most from clinical in-tervention in elderly stroke patients.Conclusion Elevated levels of TG,HDL-C,Cys C and SAA are independent risk factors for cognitive dysfunction in elderly stroke patients after stroke.The combined decision curve prediction model based on serum metabolic indicators shows higher pre-dictive efficacy.
Keywords：strokecognitive dysfunctionproportional hazards modelsforecastinglogistic mod-elsdecision support techniques
Publication Date：2024-04-15
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 431-435 )