Arthritis Clinical and Research Center, Peking University People's Hospital, Peking University, Beijing, China.
Orthopedic Department, Beijing Jishuitan Hospital, Beijing, China.
Orthop Surg. 2024 Jun;16(6):1381-1389. doi: 10.1111/os.14076. Epub 2024 May 1.
Predicting whether the posterior cruciate ligament (PCL) should be preserved during total knee arthroplasty (TKA) procedures is a complex task in the preoperative phase. The choice to either retain or excise the PCL has a substantial effect on the surgical outcomes and biomechanical integrity of the knee joint after the operation. To enhance surgeons' ability to predict the removal and retention of the PCL in patients before TKA, we developed machine learning models. We also identified significant feature factors that contribute to accurate predictions during this process.
Patients' data on TKA continuously performed by a single surgeon who had intended initially to undergo implantation of cruciate-retaining (CR) prostheses was collected. During the sacrifice of PCL, we utilized anterior-stabilized (AS) tibial bearings. The dataset was split into CR and AS categories to form distinct groups. Relevant information regarding age, gender, body mass index (BMI), the affected side, and preoperative diagnosis was extracted by reviewing the medical records of the patients. To ensure the authenticity of the research, an initial step involved capturing X-ray images before the surgery. These images were then analyzed to determine the height of the medial condyle (MMH) and lateral condyle (LMH), as well as the ratios between MLW and MMH and MLW and LMH. Additionally, the insall-salvati index (ISI) was calculated, and the severity of any varus or valgus deformities was assessed. Eight machine-learning methods were developed to predict the retention of PCL in TKA. Risk factor analysis was performed using the SHApley Additive exPlanations method.
A total of 307 knee joints from 266 patients were included, among which there were 254 females and 53 males. A stratified random sampling technique was used to split patients in a 70:30 ratio into a training dataset and a testing dataset. Eight machine-learning models were trained using data feeding. Except for the AUC of the LGBM Classifier, which is 0.70, the AUCs of other machine learning models are all lower than 0.70. In importance-based analysis, ISI, MMH, LMH, deformity, and age were confirmed as important predictive factors for PCL retention in operations.
The LGBM Classifier model achieved the best performance in predicting PCL retention in TKA. Among the potential risk factors, ISI, MMH, LMH, and deformity played essential roles in the prediction of PCL retention.
在全膝关节置换术（TKA）术前阶段，预测后交叉韧带（PCL）是否应保留是一项复杂的任务。选择保留或切除 PCL 对术后膝关节的手术结果和生物力学完整性有重大影响。为了提高外科医生在 TKA 前预测患者 PCL 切除和保留的能力，我们开发了机器学习模型。我们还确定了在这个过程中有助于准确预测的重要特征因素。
收集了一位外科医生连续进行的 TKA 患者的数据，该外科医生最初打算植入交叉韧带保留（CR）假体。在 PCL 牺牲过程中，我们使用了前稳定（AS）胫骨轴承。将数据集分为 CR 和 AS 两类，形成不同的组。通过查阅患者的病历，提取了与年龄、性别、体重指数（BMI）、患侧和术前诊断相关的信息。为了确保研究的真实性，首先要在手术前拍摄 X 光片。然后对这些图像进行分析，以确定内侧髁（MMH）和外侧髁（LMH）的高度，以及 MLW 和 MMH 之间以及 MLW 和 LMH 之间的比值。此外，还计算了 Insall-Salvati 指数（ISI），并评估了任何内翻或外翻畸形的严重程度。开发了 8 种机器学习方法来预测 TKA 中 PCL 的保留。使用 Shapley Additive exPlanations 方法进行风险因素分析。
共纳入 266 例患者的 307 个膝关节，其中女性 254 例，男性 53 例。采用分层随机抽样技术将患者按 70:30 的比例分为训练数据集和测试数据集。使用数据馈送训练了 8 种机器学习模型。除 LGBM 分类器的 AUC 为 0.70 外，其他机器学习模型的 AUC 均低于 0.70。在基于重要性的分析中，ISI、MMH、LMH、畸形和年龄被确认为操作中 PCL 保留的重要预测因素。
LGBM 分类器模型在预测 TKA 中 PCL 保留方面表现最佳。在潜在的风险因素中，ISI、MMH、LMH 和畸形在预测 PCL 保留方面起着重要作用。
