Aims. The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population. Methods. We used an active appearance model-based approach to segment the preoperative CT of 21 osteoarthritic patients, which identified the osteophyte-free surfaces and estimated cartilage from the segmented bones; these geometries were used to construct patient-specific musculoskeletal models of the pre-diseased knee. Subsequently, implantations were simulated using the MA method, and a previously developed optimization technique was employed to find the optimal implant position that minimized the root mean square deviation between pre-diseased and postoperative ligament strains and kinematics. Results. There were evident biomechanical differences between the simulated patient models, but also trends that appeared reproducible at the population level. Optimizing the implant position significantly reduced the maximum observed strain root mean square deviations within the cohort from 36.5% to below 5.3% for all but the anterolateral ligament; and concomitantly reduced the kinematic deviations from 3.8 mm (SD 1.7) and 4.7° (SD 1.9°) with MA to 2.7 mm (SD 1.4) and 3.7° (SD 1.9°) relative to the pre-diseased state. To achieve this, the femoral component consistently required translational adjustments in the anterior, lateral, and proximal directions, while the tibial component required a more posterior slope and varus rotation in most cases. Conclusion. These findings confirm that MA-induced biomechanical alterations relative to the pre-diseased state can be reduced by optimizing the implant position, and may have implications to further advance pre-planning in robotic-assisted surgery in order to restore pre-diseased knee function. Cite this article: Bone Joint J 2024;106-B(11):1231–1239

Aims. The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Methods. Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC. Results. Within the imputed datasets, the LOS (RMSE 1.161) and PROMs models (RMSE 15.775, 11.056, 21.680 for KOOS pain, function, and QOL, respectively) demonstrated good accuracy. For all models, the accuracy of predicting outcomes in a new set of patients were consistent with the cross-validation accuracy overall. Upon validation with a new patient dataset, the LOS and readmission models demonstrated high accuracy (71.5% and 65.0%, respectively). Similarly, the one-year PROMs improvement models demonstrated high accuracy in predicting ten-point improvements in KOOS pain (72.1%), function (72.9%), and QOL (70.8%) scores. Conclusion. The data-driven models developed in this study offer scalable predictive tools that can accurately estimate the likelihood of improved pain, function, and quality of life one year after knee arthroplasty as well as LOS and 90 day readmission. Cite this article: Bone Joint J 2020;102-B(9):1183–1193

Aims

The primary aim of this study was to compare the postoperative systemic inflammatory response in conventional jig-based total knee arthroplasty (conventional TKA) versus robotic-arm assisted total knee arthroplasty (robotic TKA). Secondary aims were to compare the macroscopic soft tissue injury, femoral and tibial bone trauma, localized thermal response, and the accuracy of component positioning between the two treatment groups.

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The objective of this study is to assess the use of ultrasound (US) as a radiation-free imaging modality to reconstruct 3D anatomy of the knee for use in preoperative templating in knee arthroplasty.

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Robotic-assisted unicompartmental knee arthroplasty (UKA) promises accurate implant placement with the potential of improved survival and functional outcomes. The aim of this study was to present the current evidence for robotic-assisted UKA and describe the outcome in terms of implant positioning, range of movement (ROM), function and survival, and the types of robot and implants that are currently used.

Aims

In Asia and the Middle-East, people often flex their knees deeply in order to perform activities of daily living. The purpose of this study was to investigate the 3D kinematics of normal knees during high-flexion activities. Our hypothesis was that the femorotibial rotation, varus-valgus angle, translations, and kinematic pathway of normal knees during high-flexion activities, varied according to activity.

Aims

The primary aim of this study was to determine the surgical team’s learning curve for introducing robotic-arm assisted unicompartmental knee arthroplasty (UKA) into routine surgical practice. The secondary objective was to compare accuracy of implant positioning in conventional jig-based UKA versus robotic-arm assisted UKA.

Pre-operative variables are increasingly being used to determine eligibility for total knee replacement (TKR). This study was undertaken to evaluate the relationships, interactions and predictive capacity of variables available pre- and post-operatively on patient satisfaction following TKR. Using nationally collected patient reported outcome measures and data from the National Joint Registry for England and Wales, we identified 22 798 patients who underwent TKR for osteoarthritis between August 2008 and September 2010. The ability of specific covariates to predict satisfaction was assessed using ordinal logistic regression and structural equational modelling. Only 4959 (22%) of 22 278 patients rated the results of their TKR as ‘excellent’, despite the majority (71%, n = 15 882) perceiving their knee symptoms to be much improved. The strongest predictors of satisfaction were post-operative variables. Satisfaction was significantly and positively related to the perception of symptom improvement (operative success) and the post-operative EuroQol-5D score. While also significant within the models pre-operative variables were less important and had a minimal influence upon post-operative satisfaction. The most robust predictions of satisfaction occurred only when both pre- and post-operative variables were considered together. These findings question the appropriateness of restricting access to care based on arbitrary pre-operative thresholds as these factors have little bearing on post-operative satisfaction.

Cite this article: Bone Joint J 2013;95-B:1359–65.

The primary aim of this study was to develop a patient-reported Activity & Participation Questionnaire (the OKS-APQ) to supplement the Oxford knee score, in order to assess higher levels of activity and participation. The generation of items for the questionnaire involved interviews with 26 patients. Psychometric analysis (exploratory and confirmatory factor analysis and Rasch analysis) guided the reduction of items and the generation of a scale within a prospective study of 122 relatively young patients (mean age 61.5 years (42 to 71)) prior to knee replacement. A total of 99, completed pre-operative and six month post-operative assessments (new items, OKS, Short-Form 36 and American Knee Society Score).

The eight-item OKS-APQ scale is unidimensional, reliable (Cronbach’s alpha 0.85; intraclass correlation coefficient (ICC) 0.79; or 0.92 when one outlier was excluded), valid (r >  0.5 with related scales) and responsive (effect size 4.16).

We recommend that it is used with the OKS with adults of all ages when further detail regarding the levels of activity and participation of a patient is required.

Cite this article: Bone Joint J 2014;96-B:332–8.