Development and validation of a nomogram to predict the risk of post-stroke complex regional pain syndrome
ObjectiveThis study aims to assess risk factors and build a nomogram model to facilitate the early recognition of post-stroke complex regional pain syndrome (CRPS).MethodsA total of 587 stroke patients admitted to Dongguan Hospital of Guangzhou University of Traditional Chinese Medicine from Septemb...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1577256/full |
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| author | Qian Xie Qing Song Jianling Deng Xuanling Cheng Aiguo Xue Shuxiong Luo |
| author_facet | Qian Xie Qing Song Jianling Deng Xuanling Cheng Aiguo Xue Shuxiong Luo |
| author_sort | Qian Xie |
| collection | DOAJ |
| description | ObjectiveThis study aims to assess risk factors and build a nomogram model to facilitate the early recognition of post-stroke complex regional pain syndrome (CRPS).MethodsA 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.ResultsLASSO 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.ConclusionThis 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. |
| format | Article |
| id | doaj-art-39b34bc6a4274b0a9fe03e6ab24d8db8 |
| institution | DOAJ |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-39b34bc6a4274b0a9fe03e6ab24d8db82025-08-20T02:57:07ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-05-011710.3389/fnagi.2025.15772561577256Development and validation of a nomogram to predict the risk of post-stroke complex regional pain syndromeQian Xie0Qing Song1Jianling Deng2Xuanling Cheng3Aiguo Xue4Shuxiong Luo5Department of Tuina, Dongguan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Dongguan, ChinaDepartment of Tuina, Dongguan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Dongguan, ChinaDepartment of Tuina, Dongguan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Dongguan, ChinaDepartment of Tuina, Dongguan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Dongguan, ChinaDepartment of Acupuncture and Moxibustion, Dongguan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Dongguan, ChinaDepartment of Tuina, Dongguan Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Dongguan, ChinaObjectiveThis study aims to assess risk factors and build a nomogram model to facilitate the early recognition of post-stroke complex regional pain syndrome (CRPS).MethodsA 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.ResultsLASSO 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.ConclusionThis 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.https://www.frontiersin.org/articles/10.3389/fnagi.2025.1577256/fullpost-stroke complex regional pain syndromestrokenomogramprediction modelLASSO |
| spellingShingle | Qian Xie Qing Song Jianling Deng Xuanling Cheng Aiguo Xue Shuxiong Luo Development and validation of a nomogram to predict the risk of post-stroke complex regional pain syndrome Frontiers in Aging Neuroscience post-stroke complex regional pain syndrome stroke nomogram prediction model LASSO |
| title | Development and validation of a nomogram to predict the risk of post-stroke complex regional pain syndrome |
| title_full | Development and validation of a nomogram to predict the risk of post-stroke complex regional pain syndrome |
| title_fullStr | Development and validation of a nomogram to predict the risk of post-stroke complex regional pain syndrome |
| title_full_unstemmed | Development and validation of a nomogram to predict the risk of post-stroke complex regional pain syndrome |
| title_short | Development and validation of a nomogram to predict the risk of post-stroke complex regional pain syndrome |
| title_sort | development and validation of a nomogram to predict the risk of post stroke complex regional pain syndrome |
| topic | post-stroke complex regional pain syndrome stroke nomogram prediction model LASSO |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1577256/full |
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