Risk Factors for Excessive Daytime Sleepiness in Stroke Patients

ZHANG Yanping; ZHANG Qingmin; CHENG Yanli

doi:10.3969/j.issn.1004-437X.2026.15.013

Abstract：Objective To explore the influencing factors of excessive daytime sleepiness(EDS)in stroke patients and construct a logistic regression model,so as to provide reference for guiding the development of clinical intervention plans.Methods A total of 175 stroke patients diagnosed and treated in the Third Provincial People's Hospital of Henan Province from March 2021 to March 2024 were selected as the research objects.These patients were categorized into an occurrence group and a non-occurrence group based on whether they developed EDS during the recovery phase.The clinical data of the two groups were compared to analyze the risk factors for EDS in stroke patients,and a logistic regression model was constructed.The receiver operating characteristic(ROC)curve,calibration curve,Hosmer-Lemeshow test,and consistency index(C-index)were used to evaluate the predictive performance of the logistic regression model.Results Stroke location,depressive state,degree of neurological impairment,pre-stroke fatigue,post-stroke sleep disturbance,taking more than 2 medications,and levels of serum glial fibrillary acidic protein(GFAP),soluble intercellular adhesion molecule-1(sICAM-1),homocysteine(Hcy),and stress-induced protein 2(Sestrin2)were identified as independent risk factors for EDS in stroke patients(P＜0.05).The area under the curve(AUC)of this model for predicting EDS in stroke patients was 0.914(95％CI:0.862-0.951),and the calibration curve showed a high degree of fit with the ideal curve.The Hosmer-Lemeshow test yielded x2=7.441,P=0.093,and the consistency index(C-index)was 0.716.Conclusion Stroke location,depressive state,degree of neurological impairment,pre-stroke fatigue,post-stroke sleep disturbance,taking more than two medications,and serum levels of GFAP,sICAM-1,Hey,and Sestrin2 are independent risk factors for EDS in stroke patients.The logistic regression model established based on these risk factors has good predictive value for EDS in stroke patients.Clinically,targeted intervention plans can be formulated according to the corresponding risk factors to reduce the occurrence of EDS.