Cho Sung-Hyun, Kim Yang-Soo
Department of Orthopedic Surgery, Seoul St. Mary's Hospital, The Catholic University of Korea, Banpo-Daero 222, Secho-gu, Seoul 06591, Republic of Korea.
J Clin Med. 2025 Mar 9;14(6):1843. doi: 10.3390/jcm14061843.
: This study aimed to identify the risk factors for retears after arthroscopic rotator cuff repair (ARCR) and to establish a hierarchy of their importance using machine learning. : This study analyzed 788 primary ARCR cases performed by a single senior surgeon from January 2016 to December 2022. The condition of the repaired supraspinatus was assessed via magnetic resonance imaging (MRI) or sonography within 2 years after surgery. In total, 27 preoperative demographic, objective, and subjective clinical variables were analyzed using five well-established models: Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), and logistic regression (LR). The models were trained on an 8:2 split training and test set, with three-fold validation. The primary metric for evaluating model performance was the area under the receiver operating characteristic curve (AUC). The top five influential features were extracted from the best-performing models. Univariate and multivariate LRs were performed independently as a reference. : The overall retear rate was 11.9%. The two best-performing prediction models were RF (validation AUC = 0.9790) and XGBoost (validation AUC = 0.9785). Both models consistently identified the tear size in the medial-lateral (ML) and anterior-posterior (AP) dimensions, full-thickness tears, and BMI among the top five risk factors. XGBoost uniquely included female sex, while RF highlighted the visual analogue scale (VAS) pain score. While conventional univariate regression indicated multiple significant factors associated with retears (age, full-thickness tear, AP and ML tear size, biceps conditions, fatty infiltration of three rotator cuff muscles, and atrophy of supraspinatus), multivariate analysis demonstrated that only age and the ML tear size are significant factors. : Machine learning models demonstrated enhanced predictive accuracy compared to traditional LR in predicting retears, and the importance of risk factors was derived. Tear size, full-thickness tears, BMI, female sex, and VAS pain score emerged as the most influential risk factors.
本研究旨在确定关节镜下肩袖修补术（ARCR）后再撕裂的危险因素，并使用机器学习确定其重要性等级。本研究分析了一位资深外科医生在2016年1月至2022年12月期间进行的788例原发性ARCR病例。术后2年内通过磁共振成像（MRI）或超声检查评估修复的冈上肌状况。总共使用五个成熟模型分析了27个术前人口统计学、客观和主观临床变量：极端梯度提升（XGBoost）、随机森林（RF）、支持向量机（SVM）、神经网络（NN）和逻辑回归（LR）。模型在8:2的训练集和测试集上进行训练，并进行三倍验证。评估模型性能的主要指标是受试者工作特征曲线下面积（AUC）。从性能最佳的模型中提取了前五个有影响力的特征。独立进行单变量和多变量LR作为参考。总体再撕裂率为11.9%。两个性能最佳的预测模型是RF（验证AUC = 0.9790）和XGBoost（验证AUC = 0.9785）。两个模型都一致地将内侧-外侧（ML）和前后（AP）维度的撕裂大小、全层撕裂和BMI列为前五个危险因素。XGBoost独特地纳入了女性性别，而RF突出了视觉模拟量表（VAS）疼痛评分。虽然传统单变量回归表明多个与再撕裂相关的显著因素（年龄、全层撕裂、AP和ML撕裂大小、二头肌状况、三个肩袖肌肉的脂肪浸润以及冈上肌萎缩），但多变量分析表明只有年龄和ML撕裂大小是显著因素。机器学习模型在预测再撕裂方面显示出比传统LR更高的预测准确性，并得出了危险因素的重要性。撕裂大小、全层撕裂、BMI、女性性别和VAS疼痛评分成为最有影响力的危险因素。
