Using Patient Demographics and Statistical Modeling to Predict Knee Tibia Component Sizing in Total Knee Arthroplasty

Area of Science:

• Orthopedic Surgery
• Biostatistics
• Medical Imaging

Background:

• Accurate preoperative planning is crucial for successful primary total knee arthroplasty (TKA) implantation.
• Traditional TKA templating methods lack the precision needed for exact component size prediction.

Purpose of the Study:

• To develop a general predictive statistical model for estimating tibia component size in TKA.
• To enhance the accuracy and efficiency of preoperative templating using patient demographic data.

Main Methods:

• Ordinal logistic regression was employed to construct a proportional odds model.
• Retrospective data from 1992 primary Persona Knee System TKA procedures were analyzed.
• Data were partitioned into training, validation, and testing sets, with a prospective cadaver dataset also used for evaluation.

Main Results:

• The final model, incorporating gender, age, weight, and height, predicted tibia size within one size difference 96% of the time on validation data.
• Accuracy was 94% on testing data and 92% on prospective cadaver data.
• The model demonstrated high predictive accuracy for tibia component sizing.

Conclusions:

• Ordinal logistic regression-based statistical modeling significantly enhances the accuracy of tibia sizing for Persona Knee preoperative templating.
• This methodology offers a substantial improvement in the accuracy, efficiency, and quality of TKA templating.
• The approach is adaptable to other TKA products with applicable data.