Abstract: BACKGROUND: Spastic hemiplegia remains a challenging clinical problem that urgently needs to be addressed. Currently, most research primarily focuses on discussing the influencing factors of spasticity onset. This study, however, utilizes binary logistic regression to primarily explore the key factors affecting the severity of spasticity in stroke patients, providing a reliable basis for personalized treatment plans for patients.
OBJECTIVE: To identify the influencing factors of spasticity severity in stroke patients with hemiplegia through univariate and multivariate logistic regression analyses, and to construct a risk prediction model. 
METHODS: A total of 120 patients with post-stroke spasticity hospitalized at the Affiliated Hospital of Shandong University of Traditional Chinese Medicine from November 2024 to March 2025 were enrolled. A self-designed questionnaire was used for data collection. Logistic regression analysis was performed to screen the influencing factors of spasticity severity in hemiplegic patients, and a risk prediction model was constructed. The predictive performance of the model was evaluated via a receiver operating characteristic curve analysis.
RESULTS AND CONCLUSION: Among 120 stroke spasticity patients, 66 had mild spasticity with Modified Ashworth Scale < 2 and 54 had severe spasticity with Modified Ashworth Scale ≥ 2. The results of logistic regression analysis showed that for stroke spasticity patients, advanced age, normal sensory function, higher Barthel index, and Fugl-Meyer motor function score were protective factors for the severity of spasticity; whereas negative emotions of depression and anxiety, poor sleep quality, and pain were risk factors for the severity of spasticity. The area under the receiver operating characteristic curve of the logistic regression model for spasticity severity in post-stroke hemiplegia patients was 0.969 [95% confidence interval (0.944, 0.994)], indicating that the predictive model constructed based on these factors has high predictive performance. Clinicians should actively prevent and reduce spasticity through multidimensional and individualized intervention strategies to improve patients' functional prognosis and quality of life.

Key words: stroke, hemiplegia, spasticity, Modified Ashworth Scale, influencing factors, model analysis