Department of Orthopaedic Surgery, Arthroscopy and Joint Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Am J Sports Med. 2024 Dec;52(14):3512-3519. doi: 10.1177/03635465241287527. Epub 2024 Nov 3.
Repair of rotator cuff tear is not always feasible, depending on the severity. Although several studies have investigated factors related to reparability and various methods to predict it, inconsistent scoring methods and a lack of validation have hindered the utility of these methods.
To develop machine learning models to predict the reparability of rotator cuff tears, compare them with previous scoring systems, and provide an accessible online model.
Cohort study; Level of evidence, 3.
Arthroscopic rotator cuff repairs for tears with both anteroposterior and mediolateral diameters >1 cm on preoperative magnetic resonance imaging were included and divided into a training set (70%) and an internal validation set (30%). For external validation, rotator cuff repairs performed by 2 different surgeons were included in a test set. Machine learning models and a newly adjusted scoring system were developed using the training set. The performance of the models including the adjusted scoring system and 2 previous scoring systems were compared using the test set. The performance was assessed using metrics such as the area under the receiver operating characteristic curve (AUROC) and compared using the net reclassification improvement based on the adjusted scoring system.
A total of 429 patients were included for the training and internal validation set, and 112 patients were included for the test set. An elastic-net logistic regression demonstrated the best performance, with an AUROC of 0.847 and net reclassification improvement of 0.071, compared with the adjusted scoring system in the test set. The AUROC of the adjusted scoring system was 0.786, and the AUROCs of the previous scoring systems were 0.757 and 0.687. The elastic-net logistic regression was transformed into an accessible online model.
The performance of the machine learning model, which provides a probability estimation for rotator cuff reparability, is comparable with that of the adjusted scoring system. Nevertheless, when deploying prediction models beyond the original cohort, regardless of whether they rely on machine learning or scoring systems, clinicians should exercise caution and not rely solely on the output of the model.
肩袖撕裂的修复并非总是可行，这取决于撕裂的严重程度。尽管已有多项研究探讨了与可修复性相关的因素和各种预测方法，但由于评分方法不一致和缺乏验证，这些方法的实用性受到了阻碍。
开发机器学习模型来预测肩袖撕裂的可修复性，将其与之前的评分系统进行比较，并提供一个易于访问的在线模型。
队列研究；证据等级，3 级。
纳入术前磁共振成像显示前后径和内外径均>1cm 的肩袖撕裂的关节镜下修复，并分为训练集（70%）和内部验证集（30%）。为了外部验证，纳入了由 2 位不同外科医生进行的肩袖修复作为测试集。使用训练集开发了机器学习模型和新调整的评分系统。使用测试集比较了包括调整后的评分系统和 2 个先前评分系统的模型性能。使用接收者操作特征曲线下面积（AUROC）等指标评估性能，并根据调整后的评分系统进行净重新分类改进进行比较。
共有 429 例患者纳入训练和内部验证集，112 例患者纳入测试集。弹性网络逻辑回归显示出最佳性能，在测试集中的 AUROC 为 0.847，净重新分类改善为 0.071，优于调整后的评分系统。调整后的评分系统的 AUROC 为 0.786，先前评分系统的 AUROC 分别为 0.757 和 0.687。弹性网络逻辑回归被转化为一个易于访问的在线模型。
提供肩袖可修复性概率估计的机器学习模型的性能与调整后的评分系统相当。然而，在部署预测模型超出原始队列时，无论它们是依赖于机器学习还是评分系统，临床医生都应谨慎行事，不要仅依赖模型的输出。
