Frontiers in aging neuroscience · 2026
Development and validation of a nomogram for predicting the risk of shoulder-hand syndrome after ischemic stroke: a retrospective study.
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Luo Yuan, Xie Yujie, Zeng Xin, Huang Pan, Tang Yong, Miyamoto Akira, Li Fan, Ding Guomin, Pang Guoyin, Liang Bin, Liao Peng, Cao Huanhuan, Tang Xin, Zhang Chi
Аннотация
OBJECTIVE: This study aims to assess the relevant risk factors and construct a nomogram model to assist in the early identification of shoulder-hand syndrome (SHS) after ischemic stroke.
METHOD: One thousand five hundred and thirty-nine ischemic stroke patients admitted to the First People's Hospital of Neijiang from September 2021 to August 2025 were selected as the research subjects. After exclusion, 1,211 patients were selected. These patients were divided into the training group (N = 848) and the test group (N = 363) at a ratio of 7:3. The coefficients of the predictors in the Logistic regression were used to establish a nomogram and to verify its discriminative power, calibration, and clinical practicability.
RESULT: Multivariate Logistic regression analysis revealed that age, hypertension, alcohol drinking, muscle strength of the affected upper limb, NIHSS, albumin, and total cholesterol were independent risk factors for shoulder-hand syndrome after ischemic stroke. The nomogram model demonstrated reliable predictive efficacy, with an area under the curve (AUC value) of 0.891 (95% CI, 0.853-0.929). Both DCA and CIC confirmed that the nomogram model has strong clinical practical value.
CONCLUSION: This study developed a novel predictive model for shoulder-hand syndrome after ischemic stroke, providing a valuable tool for the early identification of high-risk patients in clinical settings.
METHOD: One thousand five hundred and thirty-nine ischemic stroke patients admitted to the First People's Hospital of Neijiang from September 2021 to August 2025 were selected as the research subjects. After exclusion, 1,211 patients were selected. These patients were divided into the training group (N = 848) and the test group (N = 363) at a ratio of 7:3. The coefficients of the predictors in the Logistic regression were used to establish a nomogram and to verify its discriminative power, calibration, and clinical practicability.
RESULT: Multivariate Logistic regression analysis revealed that age, hypertension, alcohol drinking, muscle strength of the affected upper limb, NIHSS, albumin, and total cholesterol were independent risk factors for shoulder-hand syndrome after ischemic stroke. The nomogram model demonstrated reliable predictive efficacy, with an area under the curve (AUC value) of 0.891 (95% CI, 0.853-0.929). Both DCA and CIC confirmed that the nomogram model has strong clinical practical value.
CONCLUSION: This study developed a novel predictive model for shoulder-hand syndrome after ischemic stroke, providing a valuable tool for the early identification of high-risk patients in clinical settings.