Accurately Predicting Total Knee Component Size without Preoperative Radiographs.

Area of Science:

• Orthopedic Surgery
• Biomedical Engineering
• Medical Data Science

Background:

• Preoperative templating for total knee arthroplasty (TKA) is crucial for selecting appropriate implant sizes.
• While long limb radiographs aid in assessing alignment and disease, their accuracy for TKA component size prediction remains unproven.
• Surgeons seek improved predictive models for TKA component sizing using patient characteristics.

Purpose of the Study:

• Identify patient characteristics influencing tibial and femoral component sizes in TKA.
• Develop predictive models for TKA component size determination.
• Validate these models across multiple centers and compare outcomes based on prediction accuracy.

Main Methods:

• Collected demographic data from 741 patients (845 knees) in a multicenter clinical trial.
• Utilized Pearson coefficients and multivariate linear regression to correlate demographics with component size.
• Constructed and tested predictive equations for femoral and tibial components at five centers.

Main Results:

• Height, weight, and gender were the strongest predictors of TKA component size (p<0.01), followed by ethnicity and age.
• The developed model predicted component fit within one size for 94% of femoral and 96% of tibial components.
• No significant adverse events were noted in cases outside ±1 size prediction, with 100% implant survivorship at five years.

Conclusions:

• Demographic-based models effectively predict TKA component size prior to surgery.
• These models can reduce reliance on preoperative radiographic templating, enhancing surgeon efficiency.
• The findings suggest potential for reduced hospital implant inventory, particularly beneficial for outpatient surgery centers.