Sun Xiangyang, Qi Jiyuan, Zhang Linyuan, Hooblal Atiya Prajna, Wagner Timoné, Wong Zhi Yong, Wang Fusheng, Zhang Weiguo, Tian Kang
Department of Sports Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian, China.
Zhucheng People's Hospital Affiliated to Shandong Second Medical University, Weifang, China.
Ann Jt. 2025 Jul 30;10:24. doi: 10.21037/aoj-25-16. eCollection 2025.
Rotator cuff tears (RCTs) with shoulder stiffness have a high association rate; however, the mechanism and possible risk factors are unclear. This study aims to collect the factors that may affect RCT and shoulder stiffness, screen out the relevant risk factors through statistical analysis, and establish a simple model to predict the risk of RCT combined with shoulder stiffness.
A retrospective analysis was conducted on 406 patients diagnosed with RCT through arthroscopic surgery at the Department of Joint and Sports Medicine, the First Affiliated Hospital of Dalian Medical University, from December 2019 to June 2023. The analysis comprised two groups: 213 patients with both RCT and shoulder stiffness, and 193 patients without shoulder stiffness. A total of 21 potential risk factors associated with RCT and shoulder stiffness were considered, and a prediction model was developed using single-focus logistic regression analysis and multifocal logistic regression analysis in the training set (N=284), which was presented as nomograms. The validation set (N=122) was used to assess the model's discrimination, calibration and clinical practicability. The proportion of patients with RCT combined with shoulder stiffness in both the training set and the validation set was 52.5%.
The study identified eight pertinent risk factors: gender, dominant side, smoking, hypothyroidism, depression, hyperlipidemia, type III acromion, and partial tear. Based on these factors, a clinical prediction model was developed. The model demonstrated excellent predictive performance with an area under the receiver operating characteristic curve (AUROC) of 0.856 [95% confidence interval (CI): 0.812-0.900] for the training set and 0.867 (95% CI: 0.807-0.928) for the validation set. Calibration curves exhibited strong agreement between the actual disease probabilities and predicted probabilities using the model in both datasets. Decision curve analysis (DCA) further confirmed the clinical utility of the model.
Based on routine data, the prediction model offers clinicians a simple and reliable tool for predicting the combination of RCT and shoulder stiffness.
肩袖撕裂（RCT）合并肩关节僵硬的发生率较高；然而，其机制和可能的危险因素尚不清楚。本研究旨在收集可能影响RCT和肩关节僵硬的因素，通过统计分析筛选出相关危险因素，并建立一个简单模型来预测RCT合并肩关节僵硬的风险。
对2019年12月至2023年6月在大连医科大学附属第一医院关节与运动医学科通过关节镜手术诊断为RCT的406例患者进行回顾性分析。分析分为两组：213例RCT合并肩关节僵硬患者和193例无肩关节僵硬患者。共考虑了21个与RCT和肩关节僵硬相关的潜在危险因素，并在训练集（N = 284）中使用单因素逻辑回归分析和多因素逻辑回归分析建立了预测模型，并以列线图形式呈现。验证集（N = 122）用于评估模型的区分度、校准度和临床实用性。训练集和验证集中RCT合并肩关节僵硬患者的比例均为52.5%。
该研究确定了八个相关危险因素：性别、优势侧、吸烟、甲状腺功能减退、抑郁、高脂血症、III型肩峰和部分撕裂。基于这些因素，建立了一个临床预测模型。该模型显示出优异的预测性能，训练集的受试者操作特征曲线下面积（AUROC）为0.856 [95%置信区间（CI）：0.812 - 0.900]，验证集为0.867（95% CI：0.807 - 0.928）。校准曲线显示两个数据集中实际疾病概率与使用该模型预测的概率之间具有高度一致性。决策曲线分析（DCA）进一步证实了该模型的临床实用性。
基于常规数据，该预测模型为临床医生提供了一个简单可靠的工具，用于预测RCT和肩关节僵硬的合并情况。
