Xie Qian, Song Qing, Deng Jianling, Cheng Xuanling, Xue Aiguo, Luo Shuxiong
Department of Tuina, Dongguan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Dongguan, China.
Department of Acupuncture and Moxibustion, Dongguan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Dongguan, China.
Front Aging Neurosci. 2025 May 7;17:1577256. doi: 10.3389/fnagi.2025.1577256. eCollection 2025.
This study aims to assess risk factors and build a nomogram model to facilitate the early recognition of post-stroke complex regional pain syndrome (CRPS).
A total of 587 stroke patients admitted to Dongguan Hospital of Guangzhou University of Traditional Chinese Medicine from September 2021 to October 2024 were initially included in this study. After exclusions, 376 patients were selected. Among these, there were 90 patients with post-stroke CRPS, while the non-stroke CRPS group consisted of 286 patients. Feature selection and optimization to generate the predictive model and nomogram were performed using LASSO regression and multivariable logistic regression analysis. We also utilized calibration plots, receiver operating characteristic (ROC) curves, decision curves (DCA), and clinical impact curves (CIC) for model validation.
LASSO regression analysis and multivariate logistic regression identified gender, age, NIHSS score, cervical spondylosis, sleep disorders, fasting blood glucose (FBG), and albumin (ALB) as significant predictors. The nomogram model showcased reliable predictive effectiveness, achieving an area under the curve (AUC) of 0.858 (95% CI, 0.801-0.915). Both DCA and CIC demonstrated that the nomogram model holds substantial clinical utility.
This study has developed a novel predictive model for post-stroke CRPS, providing a valuable tool to facilitate the early detection of high-risk patients in a clinical environment.
本研究旨在评估中风后复杂性区域疼痛综合征（CRPS）的危险因素，并构建列线图模型以促进其早期识别。
本研究初步纳入了2021年9月至2024年10月在广州中医药大学附属东莞医院住院的587例中风患者。排除后，选取了376例患者。其中，中风后CRPS患者90例，非中风CRPS组286例。使用LASSO回归和多变量逻辑回归分析进行特征选择和优化，以生成预测模型和列线图。我们还利用校准图、受试者工作特征（ROC）曲线、决策曲线（DCA）和临床影响曲线（CIC）对模型进行验证。
LASSO回归分析和多变量逻辑回归确定性别、年龄、美国国立卫生研究院卒中量表（NIHSS）评分、颈椎病、睡眠障碍、空腹血糖（FBG）和白蛋白（ALB）为显著预测因素。列线图模型显示出可靠的预测效果，曲线下面积（AUC）为0.858（95%CI，0.801-0.915）。DCA和CIC均表明列线图模型具有重要的临床应用价值。
本研究开发了一种新型的中风后CRPS预测模型，为临床环境中促进高危患者的早期检测提供了有价值的工具。
