Mayo Clinic Alix School of Medicine, Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA.
Department of Diagnostic Radiology, Mayo Clinic, Rochester, MN, USA.
Knee Surg Sports Traumatol Arthrosc. 2023 Oct;31(10):4099-4108. doi: 10.1007/s00167-023-07497-7. Epub 2023 Jul 6.
Identifying predictive factors for all-cause reoperation after anterior cruciate ligament reconstruction could inform clinical decision making and improve risk mitigation. The primary purposes of this study are to (1) determine the incidence of all-cause reoperation after anterior cruciate ligament reconstruction, (2) identify predictors of reoperation after anterior cruciate ligament reconstruction using machine learning methodology, and (3) compare the predictive capacity of the machine learning methods to that of traditional logistic regression.
A longitudinal geographical database was utilized to identify patients with a diagnosis of new anterior cruciate ligament injury. Eight machine learning models were appraised on their ability to predict all-cause reoperation after anterior cruciate ligament reconstruction. Model performance was evaluated via area under the receiver operating characteristics curve. To explore modeling interpretability and radiomic feature influence on the predictions, we utilized a game-theory-based method through SHapley Additive exPlanations.
A total of 1400 patients underwent anterior cruciate ligament reconstruction with a mean postoperative follow-up of 9 years. Two-hundred and eighteen (16%) patients experienced a reoperation after anterior cruciate ligament reconstruction, of which 6% of these were revision ACL reconstruction. SHapley Additive exPlanations plots identified the following risk factors as predictive for all-cause reoperation: diagnosis of systemic inflammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial fixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. XGBoost was the best-performing model (area under the receiver operating characteristics curve of 0.77) and outperformed logistic regression in this regard.
All-cause reoperation after anterior cruciate ligament reconstruction occurred at a rate of 16%. Machine learning models outperformed traditional statistics and identified diagnosis of systemic inflammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial fixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair as predictive risk factors for reoperation. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. These models will allow surgeons to tabulate individualized risk for future reoperation for patients undergoing anterior cruciate ligament reconstruction.
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确定前交叉韧带重建后全因再次手术的预测因素，可以为临床决策提供信息并改善风险缓解。本研究的主要目的是：(1)确定前交叉韧带重建后全因再次手术的发生率；(2)使用机器学习方法确定前交叉韧带重建后再次手术的预测因素；(3)比较机器学习方法与传统逻辑回归的预测能力。
利用纵向地理数据库确定新诊断的前交叉韧带损伤患者。评估了 8 种机器学习模型预测前交叉韧带重建后全因再次手术的能力。通过接收者操作特征曲线下的面积来评估模型性能。为了探索建模可解释性和放射组学特征对预测的影响，我们通过博弈论的 SHapley Additive exPlanations 方法进行了分析。
共 1400 例患者接受了前交叉韧带重建，平均术后随访 9 年。218 例(16%)患者在前交叉韧带重建后再次手术，其中 6%为 ACL 翻修重建。SHapley Additive exPlanations 图确定了以下预测全因再次手术的危险因素：系统性炎症性疾病诊断、撕裂位置远侧、合并内侧副韧带修复、术前视觉模拟评分疼痛较高、腘绳肌腱自体移植物、胫骨通过放射状扩张装置固定、初次损伤时年龄较小、合并半月板修复。与以往研究相比，相关阴性因素包括性别和手术时机。XGBoost 是表现最佳的模型(接收者操作特征曲线下面积为 0.77)，在这方面优于逻辑回归。
前交叉韧带重建后全因再次手术的发生率为 16%。机器学习模型优于传统统计学方法，确定了系统性炎症性疾病诊断、撕裂位置远侧、合并内侧副韧带修复、术前视觉模拟评分疼痛较高、腘绳肌腱自体移植物、胫骨通过放射状扩张装置固定、初次损伤时年龄较小、合并半月板修复是再次手术的预测危险因素。与以往研究相比，相关阴性因素包括性别和手术时机。这些模型将使外科医生能够为接受前交叉韧带重建的患者计算未来再次手术的个体化风险。
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