A novel nomogram for predicting postoperative stiffness after arthroscopic rotator cuff repair
10.3760/cma.j.cn121113-20240614-00347
- VernacularTitle:肩袖修补术后关节僵硬风险评估模型
- Author:
Bo YUAN
1
;
Shaolong ZHANG
;
Dong MA
;
Ming TIAN
;
Shitong FENG
;
Junjie ZENG
Author Information
1. 北京大学民航临床医学院（民航总医院骨科），北京 100123
- Keywords:
Rotator cuff injuries;
Shoulder joint;
Bursitis;
Nomograms;
Risk assessment
- From:
Chinese Journal of Orthopaedics
2024;44(20):1321-1330
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the risk factors for postoperative stiffness following rotator cuff repair and to develop a predictive risk assessment model.Methods:A retrospective analysis was conducted on 251 patients (111 males and 140 females) who underwent rotator cuff repair at the Department of Orthopedics, Civil Aviation General Hospital, from June 2016 to December 2022. Patients were divided into two groups based on the time of admission: the modeling group, comprising patients treated from June 2016 to June 2021, was used to construct the risk assessment model, while the validation group, including those treated from July 2021 to December 2022, was used to evaluate the model's effectiveness. In the modeling group, the incidence of postoperative stiffness one year after surgery was assessed. The study collected data on age, sex, body mass index, disease duration, smoking history, diabetes history, preoperative fat infiltration of the rotator cuff muscles, tear size, suturing technique, preoperative stiffness, re-tear rate, visual analogue scale (VAS) scores at two and six weeks postoperatively, Constant-Murley scores at six weeks postoperatively, and both preoperative and postoperative critical shoulder angle (CSA), acromial index (AI), and lateral acromion angle (LAA). Univariate analysis was used to identify potential risk factors for postoperative stiffness, followed by multivariate logistic regression to construct the risk assessment model. The validation group was used to reassess the identified risk factors.Results:Postoperative stiffness occurred in 21 out of 176 patients in the modeling group. Logistic regression analysis revealed that diabetes history, higher fat infiltration of the rotator cuff muscles, larger tear size, preoperative stiffness, higher VAS score at six weeks postoperatively, and lower Constant-Murley score at six weeks postoperatively were significant risk factors for postoperative stiffness. Based on the logistic regression model, a nomogram was created using R software. In the validation group, postoperative stiffness was observed in 11 out of 75 patients. The area under the ROC curve (AUC=0.926) indicated good discriminative ability in predicting postoperative stiffness. The goodness-of-fit test (H-L test: χ 2=2.215, P=0.947) demonstrated moderate calibration of the model. Conclusion:A history of diabetes, high fat infiltration of the rotator cuff muscles, large or massive rotator cuff tears, preoperative stiffness, higher VAS scores at six weeks postoperatively, and lower Constant-Murley scores at six weeks postoperatively are significant risk factors for postoperative stiffness after rotator cuff repair. The risk assessment model shows good discriminative power and calibration, making it a useful tool for predicting the risk of postoperative stiffness following rotator cuff repair.
