Aims. Aseptic loosening is the most common cause of failure following cemented total knee arthroplasty (TKA), and has been linked to poor cementation technique. We aimed to develop a consensus on the optimal technique for component cementation in TKA. Methods. A UK-based, three-round, online modified Delphi Expert Consensus Study was completed focusing on cementation technique in TKA. Experts were identified as having a minimum of five years’ consultant experience in the NHS and fulfilling any one of the following criteria: a ‘high volume’ knee arthroplasty practice (> 150 TKAs per annum) as identified from the National joint Registry of England, Wales, Northern Ireland and the Isle of Man; a senior author of at least five peer reviewed articles related to TKA in the previous five years; a surgeon who is named trainer for a post-certificate of comletion of
Aims. Simultaneous bilateral total knee arthroplasty (TKA) has been used due to its financial advantages, overall resource usage, and convenience for the patient. The
To assess the cost-effectiveness of a two-layer compression bandage versus a standard wool and crepe bandage following total knee arthroplasty, using patient-level data from the Knee Replacement Bandage Study (KReBS). A cost-utility analysis was undertaken alongside KReBS, a pragmatic, two-arm, open label, parallel-group, randomized controlled trial, in terms of the cost per quality-adjusted life year (QALY). Overall, 2,330 participants scheduled for total knee arthroplasty (TKA) were randomized to either a two-layer compression bandage or a standard wool and crepe bandage. Costs were estimated over a 12-month period from the UK NHS perspective, and health outcomes were reported as QALYs based on participants’ EuroQol five-dimesion five-level questionnaire responses. Multiple imputation was used to deal with missing data and sensitivity analyses included a complete case analysis and testing of costing assumptions, with a secondary analysis exploring the inclusion of productivity losses.Aims
Methods
To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models.Aims
Methods
Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A DL workflow was created to measure the FMAA and validated against human measurements. To reflect potential intramedullary guide placement during manual TKA, two different FMAAs were calculated either using a line approximating the entire diaphyseal shaft, and a line connecting the apex of the femoral intercondylar sulcus to the centre of the diaphysis. The proportion of FMAAs outside a range of 5.0° (SD 2.0°) was calculated for both definitions, and FMAA was compared using univariate analyses across sex, BMI, knee alignment, and femur length.Aims
Methods
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. 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.Aims
Methods
Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net) workflow for angle calculation on the entire database. The radiological ankle centre was defined as the midpoint of the superior talus edge/tibial plafond. Knee alignment (hip-knee-ankle angle) was compared against 1) midpoint of the most prominent malleoli points, 2) midpoint of the soft-tissue overlying malleoli, and 3) midpoint of the soft-tissue sulcus above the malleoli.Aims
Methods
The purpose of this study was to compare the radiological outcomes of manual versus robotic-assisted medial unicompartmental knee arthroplasty (UKA). Postoperative radiological outcomes from 86 consecutive robotic-assisted UKAs (RAUKA group) from a single academic centre were retrospectively reviewed and compared to 253 manual UKAs (MUKA group) drawn from a prior study at our institution. Femoral coronal and sagittal angles (FCA, FSA), tibial coronal and sagittal angles (TCA, TSA), and implant overhang were radiologically measured to identify outliers.Aims
Methods
This study aims to evaluate a new home medical stretching device called the Self Treatment Assisted Knee (STAK) tool to treat knee arthrofibrosis. 35 patients post-major knee surgery with arthrofibrosis and mean range of movement (ROM) of 68° were recruited. Both the STAK intervention and control group received standard physiotherapy for eight weeks, with the intervention group additionally using the STAK at home. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Oxford Knee Scores (OKS) were collected at all timepoints. An acceptability and home exercise questionnaire capturing adherence was recorded after each of the interventions.Aims
Methods
