Development and validation of a nomogram for early prediction of post-stroke shoulder–hand syndrome: a retrospective cohort study

 

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Development and validation of a nomogram for early prediction of post-stroke shoulder–hand syndrome: a retrospective cohort study

Xuezheng Li 1

Y

Yu Min 1



Lulu Cheng 1,2

Y

Yunyun Tao 3

M

Meifeng Zheng 4

H

Hua Guo 4



Xuefeng Fu 1

L

Lijun Lu 1

W

H



K

Kaifeng Guo 4*

• 1. Postgraduate Cultivation Base of Guangzhou University of Chinese Medicine, Panyu Central Hospital, Guangzhou, Guangdong, China

• 2. Department of Acupuncture and Moxibustion, Qingdao Central Hospital, University of Health and Rehabilitation Sciences (Qingdao Central Hospital), Qingdao, Shandong, China

Abstract

Background: 

Shoulder-hand syndrome (SHS) is a prevalent complication following strokes. At present, there is no established and dependable method for early prediction of SHS risk. Thus, we undertook this study to create and validate a nomogram for early prediction of SHS following ischemic stroke (IS), with the aim of informing the development of SHS-specific follow-up protocols in clinical practice.

Methods: 

We retrospectively collected data on IS patients admitted to the Affiliated Panyu Central Hospital of Guangzhou Medical University from October 1, 2019 to March 31, 2024. The data was randomly split into a training set and a validation set in a 7:3 ratio, and LASSO regression was used to filter the modeling variables. In addition, the consistency index, area under the receiver operating characteristic curve (AUC), and calibration curve were used to verify the accuracy and discriminant power of the nomogram.

Results: 

A total of 514 patients were enrolled in our study. Significant predictors contained sex, occupation, residence, osteoarthritis, gouty arthritis, myodynamia, heart rate, neutrophils, blood glucose, aspartate aminotransferase, and activated partial thromboplastin time. The AUC for the model constructed on the basis of these predictors was 0.777 (95% CI: 0.727–0.826) in the training set and 0.698 (95% CI: 0.615–0.781) in the validation set.

Conclusion: 

The nomogram constructed on the basis of common clinical features has a high performance in predicting the occurrence of SHS within 6 months after stroke. It can provide a reference for the development of specific prevention programs during clinical practice.

1 Background

Stroke is a serious cerebrovascular disease with high morbidity, disability and mortality (GBD 2016 Stroke Collaborators, 2019). Shoulder-hand syndrome (SHS), a common post-stroke complication (Taylor et al., 2021), is characterized by pain, swelling, and limited mobility of the affected shoulder and hand (Zhang et al., 2021). SHS is highly prevalent within 1–3 months after stroke (Dromerick et al., 2008), with a prevalence of 24–60% (Anwer and Alghadir, 2020). Coincidentally, this is a critical period for functional recovery in stroke patients (Stinear et al., 2020). Therefore, SHS has a significant impact on the daily life and rehabilitation process of patients, often hindering their functional recovery when symptoms are severe.

The pathogenesis of SHS is not fully understood and is generally thought to involve a variety of factors. Brain injury due to stroke causes abnormalities in the nervous system, leading to abnormal perception of pain in the shoulder and hand (Hansson, 2004). Inflammatory response to stroke triggers the release of inflammatory factors, such as IL-6 and TNF-α, which can lead to local tissue inflammation and pain (Heijmans-Antonissen et al., 2006; Huygen et al., 2002). Poor local blood circulation due to stroke causes insufficient nutrients in the tissues, which can result in tissue damage and pain (Iadecola and Anrather, 2011). Loss of motor function after stroke induces stiffness of shoulder and hand joints and muscle atrophy, which causes poor venous and lymphatic return, edema and pain in the upper extremities (Lee et al., 2021). Given the unclear pathogenesis of SHS, it is challenging to develop efficient prevention strategies.

Only one study has specifically constructed a predictive model for SHS, but the number of medical records was very small (Yu et al., 2023). Most studies focus only on risk factors for SHS (Birklein et al., 2018; Ganty and Chawla, 2013). This poses a challenge for the early detection of SHS and the production of specific prevention strategies in clinical practice. Hence, the aim of this study was to develop an accurate and personalized predictive nomogram to help clinicians identify high-risk populations for SHS, implement early intervention, and ultimately improve patient prognosis.

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