To identify the influencing factors of post-traumatic stress disorder (PTSD) in young and middle-aged stroke patients, construct a risk prediction nomogram model, and validate its predictive accuracy.

A total of 514 young and middle-aged stroke patients treated in the Department of Neurology, Huai'an Hospital Affiliated to Xuzhou Medical University from September 2023 to December 2024 were recruited as research participants using convenience sampling. Data were collected using the General Information Questionnaire, Impact of Event Scale-Revised (IES-R), Activities of Daily Living (ADL) Scale, Perceived Social Support Scale, and Connor-Davidson Resilience Scale. Univariate analysis and logistic regression analysis were conducted to identify the influencing factors of PTSD. Subsequently, a nomogram-based prediction model was developed and validated.

Education level, dysphagia, hemiplegia, and ADL score were identified as independent influencing factors for PTSD in young and middle-aged stroke patients (P<0.05). The Hosmer-Lemeshow goodness-of-fit test indicated an adequate model fit (χ²=1.468, P=0.690). The area under the receiver operating characteristic curve (AUC) for the training set was 0.838 (95% confidence interval [CI]: 0.792~0.880), with the optimal cutoff value at 0.239, achieving a sensitivity of 78.69% and specificity of 76.89%. For the test set, the AUC was 0.822 (95%CI: 0.715~0.882), with the optimal cutoff value at 0.290, resulting in a sensitivity of 77.08% and specificity of 75.52%.

The risk prediction model developed in this study is capable of effectively forecasting the incidence of PTSD, thereby assisting medical professionals in the early identification of high-risk groups among young and middle-aged stroke patients and the development of personalized intervention strategies.