Predicting Implant Size in Total Hip Arthroplasty.

Area of Science:

• Orthopedic surgery
• Biostatistics
• Health systems engineering

Background:

• Rising demand for total hip arthroplasty (THA) necessitates efficient resource management.
• Accurate prediction of implant size is crucial for optimizing THA procedures.
• Patient demographic variables offer potential predictors for implant sizing.

Purpose of the Study:

• To evaluate the efficacy of linear regression and Bayesian statistics in predicting THA implant size.
• To determine the accuracy of these statistical models using patient demographic data.
• To assess the potential of predictive modeling for improving resource allocation in THA.

Main Methods:

• Retrospective review of a joint replacement registry (2005-2019).
• Inclusion of 11,730 acetabular and 8,536 femoral components.
• Development of multivariable regression and Bayesian models using demographic variables (height, weight, sex) on a training cohort (80%) and validation cohort (20%).

Main Results:

• Linear regression models including height, weight, and sex showed significant predictive power (Cup R ² = 0.57, Stem M/L R ² = 0.32).
• A parsimonious model excluding weight maintained high predictive accuracy.
• Bayesian modeling demonstrated high accuracy in predicting implant size ranges in the validation cohort (95.3% for cups, 90.4% for femoral stems).

Conclusions:

• Patient demographic variables, particularly height, weight, and sex, are strong predictors of total hip arthroplasty implant size.
• Bayesian modeling offers a highly accurate method for predicting implant size, outperforming traditional regression in this study.
• Accurate implant size prediction using these models can enhance operating room efficiency, optimize inventory management, and reduce overall costs associated with THA.