A Patient-Specific Predictive Model Increases Preoperative Templating Accuracy in Hip Arthroplasty

Abstract

Application of digital radiography during preoperative templating has shown potential to reduce complications in total hip arthroplasty. In this study, we aimed to further improve digital templating by using a predictive model built on patients' specific data. The model was significant in improving the accuracy of templating within ±1 size of acetabular component (χ2(1, N=468)=19.314, P<0.0001, Φ=0.604, and odds-ratio: 7.750 (95% CI 2.740–30.220)). We successfully achieved a 99% accuracy within ±2 of templated size. Additionally, patient demographics, such as height and weight, have shown significant effects on the predictive model. The outcome of this study may help reducing the costs of health care in the long term by minimizing implant inventory costs.