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. 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.Aims
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Patient dissatisfaction following primary total knee arthroplasty (TKA) with manual jig-based instruments has been reported to be as high as 30%. Robotic-assisted total knee arthroplasty (RA-TKA) has been increasingly used in an effort to improve patient outcomes, however there is a paucity of literature examining patient satisfaction after RA-TKA. This study aims to identify the incidence of patients who were not satisfied following RA-TKA and to determine factors associated with higher levels of dissatisfaction. This was a retrospective review of 674 patients who underwent primary TKA between October 2016 and September 2020 with a minimum two-year follow-up. A five-point Likert satisfaction score was used to place patients into two groups: Group A were those who were very dissatisfied, dissatisfied, or neutral (Likert score 1 to 3) and Group B were those who were satisfied or very satisfied (Likert score 4 to 5). Patient demographic data, as well as preoperative and postoperative patient-reported outcome measures, were compared between groups.Aims
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To investigate the impact of consecutive perioperative care transitions on in-hospital recovery of patients who had primary total knee arthroplasty (TKA) over an 11-year period. This observational cohort study used electronic health record data from all patients undergoing preoperative screening for primary TKA at a Northern Netherlands hospital between 2009 and 2020. In this timeframe, three perioperative care transitions were divided into four periods: Baseline care (Joint Care, n = 171; May 2009 to August 2010), Function-tailored (n = 404; September 2010 to October 2013), Fast-track (n = 721; November 2013 to May 2018), and Prehabilitation (n = 601; June 2018 to December 2020). In-hospital recovery was measured using inpatient recovery of activities (IROA), length of stay (LOS), and discharge to preoperative living situation (PLS). Multivariable regression models were used to analyze the impact of each perioperative care transition on in-hospital recovery.Aims
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Micromotion of the polyethylene (PE) inlay may contribute to backside PE wear in addition to articulate wear of total knee arthroplasty (TKA). Using radiostereometric analysis (RSA) with tantalum beads in the PE inlay, we evaluated PE micromotion and its relationship to PE wear. A total of 23 patients with a mean age of 83 years (77 to 91), were available from a RSA study on cemented TKA with Maxim tibial components (Zimmer Biomet). PE inlay migration, PE wear, tibial component migration, and the anatomical knee axis were evaluated on weightbearing stereoradiographs. PE inlay wear was measured as the deepest penetration of the femoral component into the PE inlay.Aims
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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
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This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization.Aims
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Periprosthetic fractures (PPFs) around the knee are challenging injuries. This study aims to describe the characteristics of knee PPFs and the impact of patient demographics, fracture types, and management modalities on in-hospital mortality. Using a multicentre study design, independent of registry data, we included adult patients sustaining a PPF around a knee arthroplasty between 1 January 2010 and 31 December 2019. Univariate, then multivariable, logistic regression analyses were performed to study the impact of patient, fracture, and treatment on mortality.Aims
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To perform an incremental cost-utility analysis and assess the impact of differential costs and case volume on the cost-effectiveness of robotic arm-assisted unicompartmental knee arthroplasty (rUKA) compared to manual (mUKA). This was a five-year follow-up study of patients who were randomized to rUKA (n = 64) or mUKA (n = 65). Patients completed the EuroQol five-dimension questionnaire (EQ-5D) preoperatively, and at three months and one, two, and five years postoperatively, which was used to calculate quality-adjusted life years (QALYs) gained. Costs for the primary and additional surgery and healthcare costs were calculated.Aims
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To map the Oxford Knee Score (OKS) and High Activity Arthroplasty Score (HAAS) items to a common scale, and to investigate the psychometric properties of this new scale for the measurement of knee health. Patient-reported outcome measure (PROM) data measuring knee health were obtained from the NHS PROMs dataset and Total or Partial Knee Arthroplasty Trial (TOPKAT). Assumptions for common scale modelling were tested. A graded response model (fitted to OKS item responses in the NHS PROMs dataset) was used as an anchor to calibrate paired HAAS items from the TOPKAT dataset. Information curves for the combined OKS-HAAS model were plotted. Bland-Altman analysis was used to compare common scale scores derived from OKS and HAAS items. A conversion table was developed to map between HAAS, OKS, and the common scale.Aims
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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
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To map literature on prognostic factors related to outcomes of revision total knee arthroplasty (rTKA), to identify extensively studied factors and to guide future research into what domains need further exploration. We performed a systematic literature search in MEDLINE, Embase, and Web of Science. The search string included multiple synonyms of the following keywords: "revision TKA", "outcome" and "prognostic factor". We searched for studies assessing the association between at least one prognostic factor and at least one outcome measure after rTKA surgery. Data on sample size, study design, prognostic factors, outcomes, and the direction of the association was extracted and included in an evidence map.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
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The aim of this study was to report the meaningful values of the EuroQol five-dimension three-level questionnaire (EQ-5D-3L) and EuroQol visual analogue scale (EQ-VAS) in patients undergoing primary knee arthroplasty (KA). This is a retrospective study of patients undergoing primary KA for osteoarthritis in a university teaching hospital (Royal Infirmary of Edinburgh) (1 January 2013 to 31 December 2019). Pre- and postoperative (one-year) data were prospectively collected for 3,181 patients (median age 69.9 years (interquartile range (IQR) 64.2 to 76.1); females, n = 1,745 (54.9%); median BMI 30.1 kg/m2 (IQR 26.6 to 34.2)). The reliability of the EQ-5D-3L was measured using Cronbach’s alpha. Responsiveness was determined by calculating the anchor-based minimal clinically important difference (MCID), the minimal important change (MIC) (cohort and individual), the patient-acceptable symptom state (PASS) predictive of satisfaction, and the minimal detectable change at 90% confidence intervals (MDC-90).Aims
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Aims. The aims of this study were to assess mapping models to predict the three-level version of EuroQoL five-dimension utility index (EQ-5D-3L) from the Oxford Knee Score (OKS) and validate these before and after total knee arthroplasty (TKA). Methods. A retrospective cohort of 5,857 patients was used to create the
The purpose of this study was to compare the clinical outcomes, mortalities, implant survival rates, and complications of total knee arthroplasty (TKA) in patients with or without hepatitis B virus (HBV) infection over at least ten years of follow-up. From January 2008 to December 2010, 266 TKAs were performed in 169 patients with HBV (HBV group). A total of 169 propensity score–matched patients without HBV were chosen for the control group in a one-to-one ratio. Then, the clinical outcomes, mortalities, implant survival rates, and complications of TKA in the two groups were compared. The mean follow-up periods were 11.7 years (10.5 to 13.4) in the HBV group and 11.8 years (11.5 to 12.4) in the control group.Aims
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Unicompartmental knee arthroplasty (UKA) has a higher risk of revision than total knee arthroplasty (TKA), particularly for younger patients. The outcome of knee arthroplasty is typically defined as implant survival or revision incidence after a defined number of years. This can be difficult for patients to conceptualize. We aimed to calculate the ‘lifetime risk’ of revision for UKA as a more meaningful estimate of risk projection over a patient’s remaining lifetime, and to compare this to TKA. Incidence of revision and mortality for all primary UKAs performed from 1999 to 2019 (n = 13,481) was obtained from the New Zealand Joint Registry (NZJR). Lifetime risk of revision was calculated for patients and stratified by age, sex, and American Society of Anesthesiologists (ASA) grade.Aims
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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
Aims. The aim of this study was to compare any differences in the primary outcome (biphasic flexion knee moment during gait) of robotic arm-assisted bi-unicompartmental knee arthroplasty (bi-UKA) with conventional mechanically aligned total knee arthroplasty (TKA) at one year post-surgery. Methods. A total of 76 patients (34 bi-UKA and 42 TKA patients) were analyzed in a prospective, single-centre, randomized controlled trial. Flat ground shod gait analysis was performed preoperatively and one year postoperatively. Knee flexion moment was calculated from motion capture markers and force plates. The same setup determined proprioception outcomes during a joint position sense test and one-leg standing. Surgery allocation, surgeon, and secondary outcomes were analyzed for
Wear of the polyethylene (PE) tibial insert of total knee arthroplasty (TKA) increases the risk of revision surgery with a significant cost burden on the healthcare system. This study quantifies wear performance of tibial inserts in a large and diverse series of retrieved TKAs to evaluate the effect of factors related to the patient, knee design, and bearing material on tibial insert wear performance. An institutional review board-approved retrieval archive was surveyed for modular PE tibial inserts over a range of in vivo duration (mean 58 months (0 to 290)). Five knee designs, totalling 1,585 devices, were studied. Insert wear was estimated from measured thickness change using a previously published method. Linear regression statistical analyses were used to test association of 12 patient and implant design variables with calculated wear rate.Aims
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This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay.Aims
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