Analysis of influencing factors and risk prediction model for spasticity severity in stroke patients with hemiplegia

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 spasticity severity; while depression and anxiety, poor sleep quality, and pain were risk factors. The area under the receiver operating characteristic curve of the logistic regression model for spasticity severity in stroke hemiplegic patients was 0.969 [95%CI (0.944, 0.994)], indicating that the prediction model based on these factors has high predictive efficacy. Clinicians should adopt multidimensional and individualized intervention strategies to actively prevent and reduce spasticity, thereby improving functional prognosis and quality of life.

1. Introduction

Spastic hemiplegia is one of the most common motor disorders after stroke [1], and its epidemiological characteristics and clinical impact have attracted much attention. The incidence of post-stroke spasticity can reach 40% [2], among which severe spasticity accounts for 10.3% and disability rate reaches 9.4% [3]. Patients mainly present with motor dysfunction such as increased muscle tone, joint contracture, and limited mobility. These symptoms not only cause limb pain and postural abnormalities but also seriously affect the functional recovery process and significantly reduce quality of life [4].

From a pathophysiological perspective, post-stroke spasticity is a complex neurological dysfunction phenomenon, and its mechanism has not been fully elucidated. Currently, most scholars believe that the occurrence of spasticity is mainly related to increased excitability of the stretch reflex, characterized by velocity-dependent increase in muscle tone accompanied by hyperreflexia [5]. This change is mainly due to the weakening of descending inhibition caused by upper motor neuron injury, leading to dysfunction of limb movement and sensory regulation [6]. At the molecular level, gamma-aminobutyric acid (GABA) is an important inhibitory neurotransmitter in the central nervous system. A decrease in GABA content reduces its inhibitory effect on the central nervous system, leading to abnormally increased spinal excitability [7], which is closely related to the occurrence of spasticity. Brain-derived neurotrophic factor (BDNF) is a protein that promotes nerve growth activity and plays an important role in the growth and development of the central nervous system. After stroke, the decreased activity of GABA-A receptors prevents normal depolarization of neurons, affecting the release of BDNF and reducing the binding of BDNF to presynaptic tyrosine kinase B receptors, thereby further inhibiting the synthesis and release of GABA and aggravating spasticity [8-9]. However, because the pathogenesis of spasticity involves multiple neural pathways and molecular changes, and the clinical manifestations and severity are affected by various factors such as lesion location, degree of injury, and rehabilitation timing, ideal therapeutic effects have not been achieved. Currently, domestic research on factors influencing the severity of post-stroke spasticity is relatively scarce. Huang Sai'e et al. [10] mainly discussed risk factors for the occurrence of muscle spasticity, but did not involve clinical scale assessment and model prediction, lacking more comprehensive and objective evidence.