Indiana University School of Medicine, Indianapolis, IN.
Department of Orthopaedic Surgery, Indiana University School of Medicine, Indianapolis, IN.
J Arthroplasty. 2021 Jul;36(7S):S242-S249. doi: 10.1016/j.arth.2021.02.063. Epub 2021 Mar 2.
Advanced technologies, like robotics, provide enhanced precision for implanting total knee arthroplasty (TKA) components; however, the optimal targets for implant position specifically in the sagittal plane do not exist. This study identified sagittal implant position which may predict improved outcomes using machine learning algorithms.
A retrospective review of 1091 consecutive TKAs was performed. All TKAs were posterior cruciate ligament retaining or sacrificing with an anterior-lip (49.4%) or conforming bearing (50.6%) and performed with modern perioperative protocols. Preoperative and postoperative tibial slope and postoperative femoral component flexion were measured with standardized radiographic protocols. Analysis groups were categorized by satisfaction scores and the Knee Society Score question 'does this knee feel normal to you?' Machine learning algorithms were used to identify optimal sagittal alignment zones that predict superior satisfaction and knees "always feeling normal" scores.
Mean age and median body mass index were 66 years and 34 kg/m, respectively, with 67% being female. The machine learning model predicted an increased likelihood of being "satisfied or very satisfied" and a knee "always feeling normal" with a change in tibial slope closer to native (-2 to +2°) and femoral component flexion 0 to +7°. Worse outcomes were predicted with any femoral component extension, femoral component flexion beyond +10°, and adding or removing >5° of native tibial slope.
Superior patient-reported outcomes were predicted with approximating native tibial slope and incorporating some femoral component flexion. Deviation from native tibial slope and excessive femoral flexion or any femoral component extension were predictive of worse outcomes.
Therapeutic level III.
先进技术，如机器人技术，为全膝关节置换术（TKA）组件的植入提供了更高的精度；然而，在矢状面中，植入物位置的最佳目标并不存在。本研究通过机器学习算法确定了可能改善结果的矢状植入物位置。
对 1091 例连续 TKA 进行回顾性研究。所有 TKA 均为后交叉韧带保留或牺牲，采用前唇（49.4%）或顺应性轴承（50.6%），并采用现代围手术期方案进行。使用标准化的放射学方案测量术前和术后胫骨斜率以及术后股骨组件的弯曲度。分析组根据满意度评分和膝关节学会评分问题“这个膝盖对你来说正常吗？”进行分类。使用机器学习算法确定预测满意度更高和膝关节“始终感觉正常”评分的最佳矢状对准区域。
平均年龄和中位数体重指数分别为 66 岁和 34kg/m，女性占 67%。机器学习模型预测胫骨斜率更接近自然（-2 至+2°）和股骨组件弯曲度 0 至+7°时，患者更有可能“满意或非常满意”，并且膝关节“始终感觉正常”。任何股骨组件的延伸、股骨组件弯曲度超过+10°以及增加或减少>5°的自然胫骨斜率都预示着更差的结果。
接近自然胫骨斜率并结合一些股骨组件弯曲度可预测更好的患者报告结果。与自然胫骨斜率的偏差以及股骨弯曲过度或任何股骨组件的延伸是预测结果不佳的指标。
治疗学 3 级。
