Department of Orthopaedics, Adult Reconstruction and Joint Replacement, Hospital for Special Surgery, New York, New York, USA.
Department of Biostatistics, Hospital for Special Surgery, New York, New York, USA.
Bone Joint J. 2020 Jun;102-B(6_Supple_A):85-90. doi: 10.1302/0301-620X.102B6.BJJ-2019-1620.R1.
The purpose of this investigation was to determine the relationship between height, weight, and sex with implant size in total knee arthroplasty (TKA) using a multivariate linear regression model and a Bayesian model.
A retrospective review of an institutional registry was performed of primary TKAs performed between January 2005 and December 2016. Patient demographics including patient age, sex, height, weight, and body mass index (BMI) were obtained from registry and medical record review. In total, 8,100 primary TKAs were included. The mean age was 67.3 years (SD 9.5) with a mean BMI of 30.4 kg/m (SD 6.3). The TKAs were randomly split into a training cohort (n = 4,022) and a testing cohort (n = 4,078). A multivariate linear regression model was created on the training cohort and then applied to the testing cohort . A Bayesian model was created based on the frequencies of implant sizes in the training cohort. The model was then applied to the testing cohort to determine the accuracy of the model at 1%, 5%, and 10% tolerance of inaccuracy.
Height had a relatively strong correlation with implant size (femoral component anteroposterior (AP) Pearson correlation coefficient (ρ) = 0.73, p < 0.001; tibial component mediolateral (ML) ρ = 0.77, p < 0.001). Weight had a moderately strong correlation with implant size, (femoral component AP ρ = 0.46, p < 0.001; tibial ML ρ = 0.48, p < 0.001). There was a significant linear correlation with height, weight, and sex with implant size (femoral component R = 0.607, p < 0.001; tibial R = 0.695, p < 0.001). The Bayesian model showed high accuracy in predicting the range of required implant sizes (94.4% for the femur and 96.6% for the tibia) accepting a 5% risk of inaccuracy.
Implant size was correlated with basic demographic variables including height, weight, and sex. The linear regression and Bayesian models accurately predicted required implant sizes across multiple manufacturers based on height, weight, and sex alone. These types of predictive models may help improve operating room and implant supply chain efficiency. Level of Evidence: Level IV Cite this article: 2020;102-B(6 Supple A):85-90.
本研究旨在使用多元线性回归模型和贝叶斯模型确定全膝关节置换术（TKA）中身高、体重和性别与植入物大小的关系。
对 2005 年 1 月至 2016 年 12 月期间进行的原发性 TKA 进行机构注册的回顾性研究。从注册处和病历回顾中获得患者人口统计学数据，包括患者年龄、性别、身高、体重和体重指数（BMI）。共纳入 8100 例原发性 TKA。平均年龄为 67.3 岁（标准差 9.5），平均 BMI 为 30.4kg/m（标准差 6.3）。TKA 随机分为训练队列（n=4022）和测试队列（n=4078）。在训练队列上创建多元线性回归模型，然后将其应用于测试队列。基于训练队列中植入物大小的频率创建了贝叶斯模型。然后将该模型应用于测试队列，以确定模型在 1%、5%和 10%容差下的准确性。
身高与植入物大小具有较强的相关性（股骨组件前后向（AP）皮尔逊相关系数（ρ）=0.73，p<0.001；胫骨组件内外向（ML）ρ=0.77，p<0.001）。体重与植入物大小呈中度强相关性（股骨组件 APρ=0.46，p<0.001；胫骨 MLρ=0.48，p<0.001）。身高、体重和性别与植入物大小有显著的线性相关性（股骨组件 R=0.607，p<0.001；胫骨 R=0.695，p<0.001）。贝叶斯模型在预测所需植入物尺寸范围方面具有较高的准确性（股骨为 94.4%，胫骨为 96.6%），接受 5%的误差风险。
植入物大小与身高、体重和性别等基本人口统计学变量相关。线性回归和贝叶斯模型可以根据身高、体重和性别准确预测来自多个制造商的所需植入物尺寸。这些预测模型可能有助于提高手术室和植入物供应链的效率。
IV 级
2020；102-B（6 增刊 A）：85-90.
