The aim of this study was to determine the effectiveness of home-based prehabilitation on pre- and postoperative outcomes in participants awaiting total knee (TKA) and hip arthroplasty (THA). A systematic review with meta-analysis of randomized controlled trials (RCTs) of prehabilitation interventions for TKA and THA. MEDLINE, CINAHL, ProQuest, PubMed, Cochrane Library, and Google Scholar databases were searched from inception to October 2022. Evidence was assessed by the PEDro scale and the Cochrane risk-of-bias (ROB2) tool.Aims
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Avascular femoral head necrosis in the context of gymnastics is a rare but serious complication, appearing similar to Perthes’ disease but occurring later during adolescence. Based on 3D CT animations, we propose repetitive impact between the main supplying vessels on the posterolateral femoral neck and the posterior acetabular wall in hyperextension and external rotation as a possible cause of direct vascular damage, and subsequent femoral head necrosis in three adolescent female gymnasts we are reporting on. Outcome of hip-preserving head reduction osteotomy combined with periacetabular osteotomy was good in one and moderate in the other up to three years after surgery; based on the pronounced hip destruction, the third received initially a total hip arthroplasty.Aims
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The use of journal clubs and, more recently,
case-based discussions in order to stimulate debate among orthopaedic
surgeons lies at the heart of orthopaedic
Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy.Aims
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Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article:
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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This study aimed to answer the following questions: do 3D-printed models lead to a more accurate recognition of the pattern of complex fractures of the elbow?; do 3D-printed models lead to a more reliable recognition of the pattern of these injuries?; and do junior surgeons benefit more from 3D-printed models than senior surgeons? A total of 15 orthopaedic trauma surgeons (seven juniors, eight seniors) evaluated 20 complex elbow fractures for their overall pattern (i.e. varus posterior medial rotational injury, terrible triad injury, radial head fracture with posterolateral dislocation, anterior (trans-)olecranon fracture-dislocation, posterior (trans-)olecranon fracture-dislocation) and their specific characteristics. First, fractures were assessed based on radiographs and 2D and 3D CT scans; and in a subsequent round, one month later, with additional 3D-printed models. Diagnostic accuracy (acc) and inter-surgeon reliability (κ) were determined for each assessment.Aims
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Reimers migration percentage (MP) is a key measure to inform decision-making around the management of hip displacement in cerebral palsy (CP). The aim of this study is to assess validity and inter- and intra-rater reliability of a novel method of measuring MP using a smart phone app (HipScreen (HS) app). A total of 20 pelvis radiographs (40 hips) were used to measure MP by using the HS app. Measurements were performed by five different members of the multidisciplinary team, with varying levels of expertise in MP measurement. The same measurements were repeated two weeks later. A senior orthopaedic surgeon measured the MP on picture archiving and communication system (PACS) as the gold standard and repeated the measurements using HS app. Pearson’s correlation coefficient (r) was used to compare PACS measurements and all HS app measurements and assess validity. Intraclass correlation coefficient (ICC) was used to assess intra- and inter-rater reliability.Aims
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Ankle fracture is one of the most common musculoskeletal injuries sustained in the UK. Many patients experience pain and physical impairment, with the consequences of the fracture and its management lasting for several months or even years. The broad aim of ankle fracture treatment is to maintain the alignment of the joint while the fracture heals, and to reduce the risks of problems, such as stiffness. More severe injuries to the ankle are routinely treated surgically. However, even with advances in surgery, there remains a risk of complications; for patients experiencing these, the associated loss of function and quality of life (Qol) is considerable. Non-surgical treatment is an alternative to surgery and involves applying a cast carefully shaped to the patient’s ankle to correct and maintain alignment of the joint with the key benefit being a reduction in the frequency of common complications of surgery. The main potential risk of non-surgical treatment is a loss of alignment with a consequent reduction in ankle function. This study aims to determine whether ankle function, four months after treatment, in patients with unstable ankle fractures treated with close contact casting is not worse than in those treated with surgical intervention, which is the current standard of care. This trial is a pragmatic, multicentre, randomized non-inferiority clinical trial with an embedded pilot, and with 12 months clinical follow-up and parallel economic analysis. A surveillance study using routinely collected data will be performed annually to five years post-treatment. Adult patients, aged 60 years and younger, with unstable ankle fractures will be identified in daily trauma meetings and fracture clinics and approached for recruitment prior to their treatment. Treatments will be performed in trauma units across the UK by a wide range of surgeons. Details of the surgical treatment, including how the operation is done, implant choice, and the recovery programme afterwards, will be at the discretion of the treating surgeon. The non-surgical treatment will be close-contact casting performed under anaesthetic, a technique which has gained in popularity since the publication of the Ankle Injury Management (AIM) trial. In all, 890 participants (445 per group) will be randomly allocated to surgical or non-surgical treatment. Data regarding ankle function, QoL, complications, and healthcare-related costs will be collected at eight weeks, four and 12 months, and then annually for five years following treatment. The primary outcome measure is patient-reported ankle function at four months from treatment.Aims
Methods
Periacetabular osteotomy (PAO) is the preferred treatment for symptomatic acetabular dysplasia in adolescents and young adults. There remains a lack of consensus regarding whether intra-articular procedures such as labral repair or improvement of femoral offset should be performed at the time of PAO or addressed subsequent to PAO if symptoms warrant. The purpose was to determine the rate of subsequent hip arthroscopy (HA) in a contemporary cohort of patients, who underwent PAO in isolation without any intra-articular procedures. From June 2012 to March 2022, 349 rectus-sparing PAOs were performed and followed for a minimum of one year (mean 6.2 years (1 to 11)). The mean age was 24 years (14 to 46) and 88.8% were female (n = 310). Patients were evaluated at final follow-up for patient-reported outcome measures (PROMs). Clinical records were reviewed for complications or subsequent surgery. Radiographs were reviewed for the following acetabular parameters: lateral centre-edge angle, anterior centre-edge angle, acetabular index, and the alpha-angle (AA). Patients were cross-referenced from the two largest hospital systems in our area to determine if subsequent HA was performed. Descriptive statistics were used to analyze risk factors for HA.Aims
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Scoliosis is a lateral curvature of the spine with associated rotation, often causing distress due to appearance. For some curves, there is good evidence to support the use of a spinal brace, worn for 20 to 24 hours a day to minimize the curve, making it as straight as possible during growth, preventing progression. Compliance can be poor due to appearance and comfort. A night-time brace, worn for eight to 12 hours, can achieve higher levels of curve correction while patients are supine, and could be preferable for patients, but evidence of efficacy is limited. This is the protocol for a randomized controlled trial of ‘full-time bracing’ versus ‘night-time bracing’ in adolescent idiopathic scoliosis (AIS). UK paediatric spine clinics will recruit 780 participants aged ten to 15 years-old with AIS, Risser stage 0, 1, or 2, and curve size (Cobb angle) 20° to 40° with apex at or below T7. Patients are randomly allocated 1:1, to either full-time or night-time bracing. A qualitative sub-study will explore communication and experiences of families in terms of bracing and research. Patient and Public Involvement & Engagement informed study design and will assist with aspects of trial delivery and dissemination.Aims
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Olecranon fractures are usually caused by falling directly on to the olecranon or following a fall on to an outstretched arm. Displaced fractures of the olecranon with a stable ulnohumeral joint are commonly managed by open reduction and internal fixation. The current predominant method of management of simple displaced fractures with ulnohumeral stability (Mayo grade IIA) in the UK and internationally is a low-cost technique using tension band wiring. Suture or suture anchor techniques have been described with the aim of reducing the hardware related complications and reoperation. An all-suture technique has been developed to fix the fracture using strong synthetic sutures alone. The aim of this trial is to investigate the clinical and cost-effectiveness of tension suture repair versus traditional tension band wiring for the surgical fixation of Mayo grade IIA fractures of the olecranon. SOFFT is a multicentre, pragmatic, two-arm parallel-group, non-inferiority, randomized controlled trial. Participants will be assigned 1:1 to receive either tension suture fixation or tension band wiring. 280 adult participants will be recruited. The primary outcome will be the Disabilities of the Arm, Shoulder and Hand (DASH) score at four months post-randomization. Secondary outcome measures include DASH (at 12, 18, and 24 months), pain, Net Promotor Score (patient satisfaction), EuroQol five-dimension five-level score (EQ-5D-5L), radiological union, complications, elbow range of motion, and re-operations related to the injury or to remove metalwork. An economic evaluation will assess the cost-effectiveness of treatments.Aims
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Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the ‘why’), the current applications (the ‘what’), and the approach to unlocking its full potential (the ‘how’). Cite this article:
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 was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model. The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate.Aims
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