The proportion of arthroplasties performed in the ambulatory setting has increased considerably. However, there are concerns whether same-day discharge may increase the risk of complications. The aim of this study was to compare 90-day outcomes between inpatient arthroplasties and outpatient arthroplasties performed at an ambulatory surgery centre (ASC), and determine whether there is a learning curve associated with performing athroplasties in an ASC. Among a single-surgeon cohort of 970 patients who underwent arthroplasty at an ASC, 854 (88.0%) were matched one-to-one with inpatients based on age, sex, American Society of Anesthesiologists (ASA) grade, BMI, and procedure (105 could not be adequately matched and 11 lacked 90-day follow-up). The cohort included 281 total hip arthroplasties (THAs) (32.9%), 267 unicompartmental knee arthroplasties (31.3%), 242 primary total knee arthroplasties (TKAs) (28.3%), 60 hip resurfacings (7.0%), two revision THAs (0.3%), and two revision TKAs (0.3%). Outcomes included readmissions, reoperations, visits to the emergency department, unplanned clinic visits, and complications.Aims
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Due to the opioid epidemic in the USA, our service progressively decreased the number of opioid tablets prescribed at discharge after primary hip (THA) and knee (TKA) arthroplasty. The goal of this study was to analyze the effect on total morphine milligram equivalents (MMEs) prescribed and post-discharge opioid repeat prescriptions. We retrospectively reviewed 19,428 patients undergoing a primary THA or TKA between 1 February 2016 and 31 December 2019. Two reductions in the number of opioid tablets prescribed at discharge were implemented over this time; as such, we analyzed three periods (P1, P2, and P3) with different routine discharge MME (750, 520, and 320 MMEs, respectively). We investigated 90-day refill rates, refill MMEs, and whether discharge MMEs were associated with represcribing in a multivariate model.Aims
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Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
This study aimed to identify the tibial component and femoral component coronal angles (TCCAs and FCCAs), which concomitantly are associated with the best outcomes and survivorship in a cohort of fixed-bearing, cemented, medial unicompartmental knee arthroplasties (UKAs). We also investigated the potential two-way interactions between the TCCA and FCCA. Prospectively collected registry data involving 264 UKAs from a single institution were analyzed. The TCCAs and FCCAs were measured on postoperative radiographs and absolute angles were analyzed. Clinical assessment at six months, two years, and ten years was undertaken using the Knee Society Knee score (KSKS) and Knee Society Function score (KSFS), the Oxford Knee Score (OKS), the 36-Item Short-Form Health Survey questionnaire (SF-36), and range of motion (ROM). Fulfilment of expectations and satisfaction was also recorded. Implant survivorship was reviewed at a mean follow-up of 14 years (12 to 16). Multivariate regression models included covariates, TCCA, FCCA, and two-way interactions between them. Partial residual graphs were generated to identify angles associated with the best outcomes. Kaplan-Meier analysis was used to compare implant survivorship between groups.Aims
Methods
Joint registries classify all further arthroplasty procedures to a knee with an existing partial arthroplasty as revision surgery, regardless of the actual procedure performed. Relatively minor procedures, including bearing exchanges, are classified in the same way as major operations requiring augments and stems. A new classification system is proposed to acknowledge and describe the detail of these procedures, which has implications for risk, recovery, and health economics. Classification categories were proposed by a surgical consensus group, then ranked by patients, according to perceived invasiveness and implications for recovery. In round one, 26 revision cases were classified by the consensus group. Results were tested for inter-rater reliability. In round two, four additional cases were added for clarity. Round three repeated the survey one month later, subject to inter- and intrarater reliability testing. In round four, five additional expert partial knee arthroplasty surgeons were asked to classify the 30 cases according to the proposed revision partial knee classification (RPKC) system.Aims
Methods
Many authors have recommended undercorrection of the deformity when carrying out unicompartmental knee arthroplasty (UKA). The isolated effect of alignment of the knee on the outcome of
Metal allergy in knee arthroplasty patients is a controversial topic. We aimed to conduct a scoping review to clarify the management of metal allergy in primary and revision total knee arthroplasty (TKA). Studies were identified by searching electronic databases: Cochrane Central Register of Controlled Trials, Ovid MEDLINE, and Embase, from their inception to November 2020, for studies evaluating TKA patients with metal hypersensitivity/allergy. All studies reporting on diagnosing or managing metal hypersensitivity in TKA were included. Data were extracted and summarized based on study design, study population, interventions and outcomes. A practical guide is then formulated based on the available evidence.Aims
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The aim of this study was to compare the clinical outcomes of robotic arm-assisted bi-unicompartmental knee arthroplasty (bi-UKA) with conventional mechanically aligned total knee arthroplasty (TKA) during the first six weeks and at one year postoperatively. A per protocol analysis of 76 patients, 43 of whom underwent TKA and 34 of whom underwent bi-UKA, was performed from a prospective, single-centre, randomized controlled trial. Diaries kept by the patients recorded pain, function, and the use of analgesics daily throughout the first week and weekly between the second and sixth weeks. Patient-reported outcome measures (PROMs) were compared preoperatively, and at three months and one year postoperatively. Data were also compared longitudinally and a subgroup analysis was conducted, stratified by preoperative PROM status.Aims
Methods
The aim of this study was to compare robotic arm-assisted bi-unicompartmental knee arthroplasty (bi-UKA) with conventional mechanically aligned total knee arthroplasty (TKA) in order to determine the changes in the anatomy of the knee and alignment of the lower limb following surgery. An analysis of 38 patients who underwent TKA and 32 who underwent bi-UKA was performed as a secondary study from a prospective, single-centre, randomized controlled trial. CT imaging was used to measure coronal, sagittal, and axial alignment of the knee preoperatively and at three months postoperatively to determine changes in anatomy that had occurred as a result of the surgery. The hip-knee-ankle angle (HKAA) was also measured to identify any differences between the two groups.Aims
Methods
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. 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.Aims
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The mobile bearing Oxford unicompartmental knee arthroplasty (OUKA) is recommended to be performed with the leg in the hanging leg (HL) position, and the thigh placed in a stirrup. This comparative cadaveric study assesses implant positioning and intraoperative kinematics of OUKA implanted either in the HL position or in the supine leg (SL) position. A total of 16 fresh-frozen knees in eight human cadavers, without macroscopic anatomical defects, were selected. The knees from each cadaver were randomized to have the OUKA implanted in the HL or SL position.Aims
Methods
The efficacy and safety of intrawound vancomycin for preventing surgical site infection in primary hip and knee arthroplasty is uncertain. A systematic review of the literature was conducted, indexed from inception to March 2020 in PubMed, Web of Science, Cochrane Library, Embase, and Google Scholar databases. All studies evaluating the efficacy and/or safety of intrawound vancomycin in patients who underwent primary hip and knee arthroplasty were included. Incidence of periprosthetic joint infection (PJI), superficial infection, aseptic wound complications, acute kidney injury, anaphylactic reaction, and ototoxicity were meta-analyzed. Results were reported as odds ratios (ORs) and 95% confidence intervals (CIs). The quality of included studies was assessed using the risk of bias in non-randomized studies of interventions (ROBINS-I) assessment tool.Aims
Methods