Demographic Data Reliably Predicts Total Hip Arthroplasty Component Size

• Published in: The Journal of arthroplasty 2022-08, Vol.37 (8), p.S890-S894
• Main Authors: Murphy, Michael P., Boubekri, Amir M., Myall, James J., Ralles, Steven J., Brown, Nicholas M.
• Format: Article
• Language: English
• Subjects: cementless stem; demographic data; metaphyseal-fitting stem; multivariate regression model; predicting implant size; total hip arthroplasty
• ISSN: 0883-5403; 1532-8406
• DOI: 10.1016/j.arth.2022.01.051

Preoperative radiographic templating for total hip arthroplasty (THA) has been shown to be inaccurate, although essential for streamlining operating room efficiency. Although demographic data have shown to predict total knee arthroplasty component sizes, the unique contour and design among femoral stem implants have limited a similar application for hip arthroplasty. The purpose of this study was to determine whether demographic data may predict cementless THA size independent of the stem design. A consecutive series of 1,653 index cementless metaphyseal-fitting THAs were reviewed between 2007 and 2019. This included 12 unique femoral component designs, 6 acetabular component designs, 60 femur size-design combinations, and 23 acetabular size-design combinations. Implanted component sizes and patient demographic data were collected, including gender, height, weight, laterality, age, race, and ethnicity. Multivariate linear regressions were formulated to predict implanted femur and acetabular component sizes from the demographic data. There was a significant linear correlation between gender, implant model, age, height, and weight for femur (R2 = 0.778; P < .001) and acetabular (R2 = 0.491; P < .001) sizes. Calculated femur and acetabular component sizes averaged within 0.97 and 0.95 sizes of those implants, respectively. Femur and acetabular sizes were predicted within 1 size 79.1% and 78.2% and within 2 sizes 94.3% and 94.6% of the time, respectively. Multivariate regression models were created based on specific demographics data to predict femur and acetabular component sizes. The model allows for simplified preoperative planning and potential cost savings implementation. A free phone application named EasyTJA was constructed for ease of implementation.