AUTHOR=Li Yingying , Li Yanchun , Zhang Zhenmei 

TITLE=Network analysis of post-traumatic stress disorder symptoms in stroke patients

JOURNAL=Frontiers in Psychiatry

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2025.1663366

DOI=10.3389/fpsyt.2025.1663366

ISSN=1664-0640

ABSTRACT=BackgroundStroke patients have a high incidence of Post-traumatic stress disorder (PTSD). Previous studies on PTSD in stroke patients mainly focus on the risk factors and possible harms caused by PTSD and use the overall score to explain the severity of PTSD. The interconnections and effects of symptoms are ignored. Network analysis is a statistical method that can discover and visualize complex relationships between multiple variables. The purpose of this study was to identify the central and core symptoms in the symptom network of PTSD in stroke patients.Methods315 patients diagnosed with cerebral apoplexy were selected as the study objects. Symptoms of PTSD were assessed using the Event Impact Scale (IES-R). The graph Gaussian model is used to estimate the network model. To clarify the network relationship and core symptoms of PTSD in stroke patients. The network’s stability and accuracy are tested using the discard example method and non-parametric bootstrap method.ResultThe network analysis found that A11 (I tried not to think about it) has the most substantial relationship with I3 (Other things kept making me think about it). I6 (I thought about it when I didn’t mean to) has the most substantial relationship with I9 (Pictures about it popped into my mind). “I was jumpy and easily startled”(H10) is the core symptom of PTSD in stroke patients. The network structure is suitable for stability and accuracy tests.ConclusionIt is possible to reduce the severity of PTSD in stroke patients and promote their personal growth by taking timely intervention measures according to the identified central symptoms of PTSD.