Department of Orthopaedics and Traumatology, Division of Joint Replacement Surgery, The University of Hong Kong, Queen Mary Hospital, Hong Kong SAR, China.
J Knee Surg. 2024 Jul;37(8):602-606. doi: 10.1055/a-2198-7983. Epub 2023 Oct 25.
Anticipating implant sizes before total knee arthroplasty (TKA) allows the surgical team to streamline operations and prepare for potential difficulties. This study aims to determine the correlation and derive a regression model for predicting TKA sizes using patient-specific demographics without using radiographs. We reviewed the demographics, including hand and foot sizes, of 1,339 primary TKAs. To allow for comparison across different TKA designs, we converted the femur and tibia sizes into their anteroposterior (AP) and mediolateral (ML) dimensions. Stepwise multivariate regressions were performed to analyze the data. Regarding the femur component, the patient's foot, gender, height, hand circumference, body mass index, and age was the significant demographic factors in the regression analysis (R-square 0.541, < 0.05). For the tibia component, the significant factors in the regression analysis were the patient's foot size, gender, height, hand circumference, and age (R-square 0.608, < 0.05). The patient's foot size had the highest correlation coefficient for both femur (0.670) and tibia (0.697) implant sizes ( < 0.05). We accurately predicted the femur component size exactly, within one and two sizes in 49.5, 94.2, and 99.9% of cases, respectively. Regarding the tibia, the prediction was exact, within one and two sizes in 53.0, 96.0, and 100% of cases, respectively. The regression model, utilizing patient-specific characteristics, such as foot size and hand circumference, accurately predicted TKA femur and tibia sizes within one component size. This provides a more efficient alternative for preoperative planning.
在全膝关节置换术 (TKA) 之前预估植入物大小可以让手术团队简化手术流程并为潜在困难做好准备。本研究旨在确定使用患者特定的人口统计学数据（无需使用 X 光片）预测 TKA 大小的相关性并推导出回归模型。我们回顾了 1339 例初次 TKA 的人口统计学数据，包括手和脚的大小。为了能够在不同的 TKA 设计之间进行比较，我们将股骨和胫骨的大小转换为前后 (AP) 和内外 (ML) 尺寸。我们进行了逐步多元回归分析来处理数据。对于股骨组件，患者的脚、性别、身高、手围、体重指数和年龄是回归分析中的显著人口统计学因素（R 平方 0.541， < 0.05）。对于胫骨组件，回归分析中的显著因素是患者的脚大小、性别、身高、手围和年龄（R 平方 0.608， < 0.05）。患者的脚大小与股骨（0.670）和胫骨（0.697）植入物大小的相关性系数最高（ < 0.05）。我们准确预测了股骨组件的大小，在 49.5%、94.2%和 99.9%的病例中，分别精确到一个和两个尺寸。对于胫骨，在 53.0%、96.0%和 100%的病例中，分别精确到一个和两个尺寸。该回归模型利用患者特定的特征，如脚大小和手围，准确预测了 TKA 股骨和胫骨的大小，误差在一个组件尺寸内。这为术前规划提供了更高效的替代方案。
