Aims. We aimed to assess the reliability and validity of OpenPose, a posture estimation algorithm, for measurement of knee range of motion after total knee arthroplasty (TKA), in comparison to radiography and goniometry. Methods. In this prospective observational study, we analyzed 35 primary TKAs (24 patients) for knee osteoarthritis. We measured the knee angles in flexion and extension using OpenPose, radiography, and goniometry. We assessed the test-retest reliability of each method using intraclass correlation coefficient (1,1). We evaluated the ability to estimate other measurement values from the OpenPose value using linear regression analysis. We used intraclass correlation coefficients (2,1) and Bland–Altman analyses to evaluate the agreement and error between radiography and the other measurements. Results. OpenPose had excellent test-retest reliability (intraclass correlation coefficient (1,1) = 1.000). The R. 2. of all regression models indicated large correlations (0.747 to 0.927). In the flexion position, the intraclass correlation coefficients (2,1) of OpenPose indicated excellent agreement (0.953) with radiography. In the extension position, the intraclass correlation coefficients (2,1) indicated good agreement of OpenPose and radiography (0.815) and moderate agreement of goniometry with radiography (0.593). OpenPose had no systematic error in the flexion position, and a 2.3° fixed error in the extension position, compared to radiography. Conclusion. OpenPose is a reliable and valid tool for measuring flexion and extension positions after TKA. It has better accuracy than goniometry, especially in the extension position. Accurate measurement values can be obtained with low error, high reproducibility, and no contact, independent of the examiner’s skills. Cite this article: Bone Joint Res 2023;12(5):313–320

Objectives. Preservation of posterior condylar offset (PCO) has been shown to correlate with improved functional results after primary total knee arthroplasty (TKA). Whether this is also the case for revision TKA, remains unknown. The aim of this study was to assess the independent effect of PCO on early functional outcome after revision TKA. Methods. A total of 107 consecutive aseptic revision TKAs were performed by a single surgeon during an eight-year period. The mean age was 69.4 years (39 to 85) and there were 59 female patients and 48 male patients. The Oxford Knee Score (OKS) and Short-form (SF)-12 score were assessed pre-operatively and one year post-operatively. Patient satisfaction was also assessed at one year. Joint line and PCO were assessed radiographically at one year. Results. There was a significant improvement in the OKS (10.6 points, 95% confidence interval (CI) 8.8 to 12.3) and the SF-12 physical component score (5.9, 95% CI 4.1 to 7.8). PCO directly correlated with change in OKS (p < 0.001). Linear regression analysis confirmed the independent effect of PCO on the OKS (p < 0.001) and the SF-12 physical score (p = 0.02). The overall rate of satisfaction was 85% and on logistic regression analysis improvement in the OKS (p = 0.002) was a significant predictor of patient satisfaction, which is related to PCO; although this was not independently associated with satisfaction. Conclusion. Preservation of PCO should be a major consideration when undertaking revision TKA. The option of increasing PCO to balance the flexion gap while maintaining the joint line should be assessed intra-operatively. Cite this article: N. D. Clement, D. J. MacDonald, D. F. Hamilton, R. Burnett. Posterior condylar offset is an independent predictor of functional outcome after revision total knee arthroplasty. Bone Joint Res 2017;6:172–178. DOI: 10.1302/2046-3758.63.BJR-2015-0021.R1

Objectives

Up to 40% of unicompartmental knee arthroplasty (UKA) revisions are performed for unexplained pain which may be caused by elevated proximal tibial bone strain. This study investigates the effect of tibial component metal backing and polyethylene thickness on bone strain in a cemented fixed-bearing medial UKA using a finite element model (FEM) validated experimentally by digital image correlation (DIC) and acoustic emission (AE).