Can Demographic Variables Accurately Predict Component Sizing in Primary Total Knee Arthroplasty?

Area of Science:

• Orthopedic surgery
• Biomedical engineering
• Medical imaging

Background:

• Healthcare reform necessitates cost reduction and efficiency improvements in patient care.
• Accurate preoperative planning is crucial for optimizing outcomes in total knee arthroplasty (TKA).

Purpose of the Study:

• To evaluate the predictive accuracy of patient demographics (height, weight, gender) for TKA sizing.
• To develop a novel templating model for TKA to enhance preoperative planning.

Main Methods:

• A retrospective review of 3491 primary TKAs was conducted.
• Multivariate linear regression analysis was used to correlate demographics and preoperative templating with final implant sizes.
• Model accuracy was assessed for femoral and tibial components using various implants.

Main Results:

• Height, weight, and gender showed significant linear correlations with femoral and tibial sizes (R² = 0.504-0.610).
• Incorporating preoperative templating significantly improved model fit (R² = 0.756-0.780).
• Demographics alone predicted sizes within 1 unit 71%-97%, while combined with templating, accuracy reached 85%-99%.

Conclusions:

• A novel TKA templating model accurately predicts final implant size within one size.
• This model simplifies preoperative planning and can support cost-saving initiatives by optimizing inventory.
• The findings support the integration of demographic data into TKA sizing algorithms for improved efficiency.