Planning on CT-Based 3D Virtual Models Can Accurately Predict the Component Size for Total Knee Arthroplasty

Abstract

The ability to predict accurate sizing of the implant components for total knee arthroplasty surgery can have several benefits in the operating room, in terms of simplifying the workflow and reducing the number of required instrument trays. Planning on a three-dimensional (3D) virtual model can be used to predict size. The aim of this study was to quantify the accuracy of the surgeon-validated plan prediction on a computed tomography (CT)-based system. The clinical records of 336 cases (267 patients), operated using a CT-based patient-specific instrumentation, have been reviewed for the size of implanted components. Preoperative default planning (according to the preferences of the surgeon) and approved planning have been compared with the size of implanted components for both the femur and tibia. The prosthesis size, preplanned by the manufacturers, was modified by the surgeon during the validation process in 0.9% of cases for the femoral component and in 2.7% of cases for the tibial component. The prosthesis size, preplanned by the surgeon after the validation process, was used in 95.8% for the femur and 92.6% for the tibia. Concordance on the size of the surgeon-validated plan and the finally implanted size was perfect for both, the femoral (κ  =  0.951; 95% confidence interval [CI]: 0.92–0.98) and the tibial component (κ  =  0.902; 95% CI: 0.86–0.94). The most frequent change of size (51%) was an increase by one size of the planned tibial component. Planning of knee arthroplasty surgery on a 3D virtual, CT-based model is useful to surgeons to help predict the size of the implants to be used in surgery. The system we have used can accurately predict the component size for both the femur and tibia. This study reflects a study of level III evidence.

Keywords

patient-specific instruments - total knee arthroplasty - implant size - preoperative planning

Publication History

Received: 30 January 2019

Accepted: 05 May 2019

Article published online:
03 July 2019

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