Predicting robotic-assisted total knee arthroplasty operating time

Abstract

Aims No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model. Methods A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and tibial mechanical axis and the osteophyte volume were calculated from CT scans. These inputs were used to develop a predictive model aimed to predict operating time based on demographic data only, and demographic and 3D patient anatomy data. Results The key factors for predicting operating time were the surgeon and patient weight, followed by 12 anatomical parameters derived from CT scans. The predictive model based only on demographic data showed that 90% of predictions were within 15 minutes of actual operating time, with 73% within ten minutes. The predictive model including demographic data and CT scans showed that 94% of predictions were within 15 minutes of actual operating time and 88% within ten minutes. Conclusion The primary factors for predicting robotic-assisted TKA operating time were surgeon, patient weight, and osteophyte volume. This study demonstrates that incorporating 3D patient-specific data can improve operating time predictions models, which may lead to improved operating room planning and efficiency. Cite this article: Bone Jt Open 2022;3(5):383–389.

Prediction of early functional outcomes in patients after robotic-assisted total knee arthroplasty: a nomogram prediction model

“…Muertizha et al 21 used a nomogram to predict factors contributing to 1-year dissatisfaction after TKA. Motesharei et al 31 developed a model to predict the operation time of RA-TKA and obtained satisfactory results. In contrast to previous studies, a nomogram model was hereby developed to predict functional outcomes for early patient benefit following RA-TKA to direct early clinical intervention and address patient and physician concerns regarding RA-TKA surgery.…”

Initial Learning Curve for Robot-Assisted Total Knee Arthroplasty in a Dedicated Orthopedics Center

“…Robotic systems may help improve the skills of orthopedic surgeons and the workflow of TKAs [55][56][57]. However, there are some concerns that robotic-assisted surgery may have unsatisfactory results regarding mechanical axis alignments, leading to potential deficiencies in surgical outcomes [58].…”

Numerical study of osteophyte effects on preoperative knee functionality in patients undergoing total knee arthroplasty

“…The CT images of the individual patients were segmented to render the three‐dimensional bone geometries of the hip, knee, and ankle joints. The bony structures of the knee were segmented using two different active appearance models (AAM), provided by Imorphics 11 (Stryker), that were trained on manual segmentations of CT images to identify the osteophytic femoral and tibial bone surfaces, as well as the bone surfaces beneath osteophytes, 10,11 referred to as the preosteophytic bones (Figure 1). The osteophytic AAM was trained on a set of 670 cadaveric specimens with different stages of knee OA to detect osteophytes, which are typically visible along the periphery of the knee bones in OA subjects; the preosteophytic AAM was trained on manual segmentations of the osteophyte‐free bone surfaces in a training set of 124 patient CT images.…”