Aims

Day-case knee and hip replacement, in which patients are discharged on the day of surgery, has been gaining popularity during the last two decades, and particularly since the COVID-19 pandemic. This systematic review presents the evidence comparing day-case to inpatient-stay surgery.

Introduction. Orthopedics is experiencing a significant transformation with the introduction of technologies such as robotics and apps. These, integrated into the post-operative rehabilitation process, promise to improve clinical outcomes, patient satisfaction, and the overall efficiency of the healthcare system. This study examines the impact of an app called Mymobility and intra-operative data collected via the ROSA® robotic system on the functional recovery of patients undergoing robot-assisted knee arthroplasty. Method. The study was conducted at a single center from 2020 to 2023. Data from 436 patients were included, divided into “active” patients (active users of Mymobility) and “non-active” patients. Clinical analyses and satisfaction surveys were carried out on active patients. The intra-operative parameters recorded by ROSA® were correlated with the Patient-Reported Outcome Measures (PROMs) collected via Mymobility. Result. Intra-operative data showed significant correlations with PROMs for the 48 active patients, highlighting the importance of parameters such as medial joint space and ligament laxity. No significant differences were observed between the sexes, but a positive correlation was detected between age and PROMs. The data analysis indicated that an increased medial joint space and reduced ligament laxity are associated with better PROMs. The adoption of Mymobility remained limited, with only 10% of patients fully utilizing the app. Critical factors have been identified to improve recruitment, engagement, and overall experience with the platform. Conclusion. The integration of technologies such as Mymobility and ROSA® in post-operative rehabilitation offers numerous advantages, including the objectification of data, active patient involvement, and personalized care. Challenges remain related to costs, patient compliance, and demographic limitations. Nevertheless, these technologies represent a milestone in modern peri-operative management, being able to improve clinical outcomes and the quality of care

Background. Mobile apps have shown promise in helping people to self-manage conditions like chronic low back pain (CLBP). However, it is unclear who benefits most, why, and under what circumstances. This limits our understanding of how to maximize the potential of mobile app technology. Aim. This study aimed to test and refine programme theories about how mobile apps support people to self-manage CLBP in a real-world setting. The theories were based on a previously published realist review. Methods. A realist evaluation was conducted using one-to-one realist interviews by telephone with participants who had used the Curable app to self-manage their CLBP for three months. The interviews were guided by programme theories from the realist review. Analysis of the interviews used abductive and retroductive logic to create chains of inferences, which were then developed into context-mechanism-outcome configurations (CMOCs). These CMOCs offered causal explanations about who might benefit from a mobile app to self-manage CLBP, why and in what circumstances. Results. Ten interviews were conducted (nine with people with CLBP who trialled the app and one with a founding member of Curable LLC). Twenty CMOCs were created that identified key mechanisms such as agency, control and reassurance that interact with contextual factors such as acceptance, internal capacity, and a biopsychosocial approach to pain management. These factors influence whether a person with CLBP will benefit from a mobile app for self-management. Conclusions. Twenty CMOCs were created to support three programme theories centering around concepts of empowerment, burden of care, and timing. Conflicts of interest. None. Sources of funding. R Hunter's work was supported by the Inverness and Highland City-Region Deal Studentship Award [2018]. Presented at: 13th Congress of the European Pain Federation (EFIC), September 2023, Budapest, Hungary

Purpose and Background. Clinical practice guidelines (CPGs) recommend self-management for low back pain (LBP). Our recent narrative review on self-management needs revealed a consensus with respect to the critical components of self-management interventions. With mobile health advancements, apps offer innovative support for LBP management. This study aims to identify current apps for the self-management of LBP, assessing them for their quality, intervention content, theoretical approaches, and risk management approaches. Methods and Results. We identified 69 apps for LBP self-management from a systematic search in the UK iTunes and Google Play stores. The most recommended interventions are muscle stretching (n=51, 73.9%), muscle strengthening (n=42, 60.9%), and core stability exercises (n=32, 46.4%). The average MARS (SD) overall score for the included apps was 2.4 (0.44) out of a possible 5 points, with the engagement and information dimension scoring the lowest at 2.1. In terms of theoretical and risk management approaches, no apps offered a theoretical care model and all failed to specify the age group targeted; only one (1.4%) provided a tailored care approach; 18 (26.1%) included intervention progression; and 11 (15.9%) reported management safety checks. Conclusion. This study shows that app developers generally select interventions endorsed by CPGs. However, the application of a biopsychosocial care model is not being considered. Most of them are of low quality, lacking theoretical approaches to care and consideration of associated risks. It is essential to involve clinicians and patients in developing LBP self-management apps to improve the quality and related approach. Conflicts of interest. None. Sources of funding. No funding obtained. This study has been published in JMIR mHealth and uHealth

The Revision Hip Complexity Classification (RHCC) was developed by modified Delphi system in 2022 to provide a comprehensive, reproducible framework for the multidisciplinary discussion of complex revision hip surgery. The aim of this study was to assess the validity, intra-relater and inter-relater reliability of the RHCC. Radiographs and clinical vignettes of 20 consecutive patients who had undergone revision of Total Hip Arthroplasty (THA) at our unit during the previous 12-month period were provided to observers. Five observers, comprising 3 revision hip consultants, 1 hip fellow and 1 ST3-8 registrar were familiarised with the RHCC. Each revision THA case was classified on two separate occasions by each observer, with a mean time between assessments of 42.6 days (24–57). Inter-observer reliability was assessed using the Fleiss™ Kappa statistic and percentage agreement. Intra-observer reliability was assessed using the Cohen Kappa statistic. Validity was assessed using percentage agreement and Cohen Kappa comparing observers to the RHCC web-based application result. All observers were blinded to patient notes, operation notes and post-operative radiographs throughout the process. Inter-observer reliability showed fair agreement in both rounds 1 and 2 of the survey (0.296 and 0.353 respectively), with a percentage agreement of 69% and 75%. Inter-observer reliability was highest in H3-type revisions with kappa values of 0.577 and 0.441. Mean intra-observer reliability showed moderate agreement with a kappa value of 0.446 (0.369 to 0.773). Validity percentage agreement was 44% and 39% respectively, with mean kappa values of 0.125 and 0.046 representing only slight agreement. This study demonstrates that classification using the RHCC without utilisation of the web-based application is unsatisfactory, showing low validity and reliability. Reliability was higher for more complex H3-type cases. The use of the RHCC web app is recommended to ensure the accurate and reliable classification of revision THA cases

Aims

Accurate diagnosis of chronic periprosthetic joint infection (PJI) presents a significant challenge for hip surgeons. Preoperative diagnosis is not always easy to establish, making the intraoperative decision-making process crucial in deciding between one- and two-stage revision total hip arthroplasty (THA). Calprotectin is a promising point-of-care novel biomarker that has displayed high accuracy in detecting PJI. We aimed to evaluate the utility of intraoperative calprotectin lateral flow immunoassay (LFI) in THA patients with suspected chronic PJI.

Aims

The aim of this study was to investigate whether anterior pelvic plane-pelvic tilt (APP-PT) is associated with distinct hip pathomorphologies. We asked: is there a difference in APP-PT between young symptomatic patients being evaluated for joint preservation surgery and an asymptomatic control group? Does APP-PT vary among distinct acetabular and femoral pathomorphologies? And does APP-PT differ in symptomatic hips based on demographic factors?

Introduction. A 6cm femoral gain requires 5-Y during normal growth, but only 6–8-W surgically (x30–40 faster). In lengthening surgery, losses of muscle force (MF) and circumferences (MC) are major. Recovery is long, preventing sports till bone fusion. Can we maintain MC and strength throughout the entire lengthening and how?. We monitored for over 30 years patients for muscle force (isokinetic), circumferences, activities (including sports) and food intake, and acted on the 5 principles of the Osteostasis. Materials & Methods. Over 750 femoral lengthening with Full WB Nails (FWBN) got Isokinetic testing (≧1991), circumferences measurements (≧2012; 20-15-10-5-0cm above patella, max-calf, mini/max-ankle), food intake (≧2012), using MyFitnessPal App (≧2016), gradually enforced. Preoperative training along with a daily post-operative training are supervised by our trainers. Recommendations for food intake and activities were provided. Patients noted on a specific App all parameters. Compliance was noted. Results. Instead of a traditional 7–9cm circumference loss for 8–10cm gains using Ex-fix or nails, with FWBN and our protocols, no MC loss could be noted in compliant pre-trained patients, intensive early post-op resistance training, high calory intake (M:>4200, F:>3000; 20–25% Proteins) and supplements (no fat pad increase). Bone fusion could be obtained at the end of lengthening or within short weeks (Healing Index down to 8D/cm). Non-compliant patients (or using morphine), lost weight and MC. Conclusions. Increasing by 8–10cm muscle length, even bilaterally, and maintaining MC during lengthening, is possible, with very fast bone fusion. It requires building up several hundred of grams of muscles. The ‘building up equation’ associating resistance bike from the early post-operative phase with sports, strong food intake with increased protein intake, and added supplements with no morphine medication, proved to boost circumferences and bone fusion. It induced fast recovery, walking and sports capacities

Aims

While internet search engines have been the primary information source for patients’ questions, artificial intelligence large language models like ChatGPT are trending towards becoming the new primary source. The purpose of this study was to determine if ChatGPT can answer patient questions about total hip (THA) and knee arthroplasty (TKA) with consistent accuracy, comprehensiveness, and easy readability.

Aims

The aim of this study is to evaluate whether acetabular retroversion (AR) represents a structural anatomical abnormality of the pelvis or is a functional phenomenon of pelvic positioning in the sagittal plane, and to what extent the changes that result from patient-specific functional position affect the extent of AR.

Introduction. With advances in mobile application, digital health is being increasingly used for remote and personalised care. Patient education, self-management and tele communication is a crucial factor in optimising outcomes. Aims. We explore the use of a smartphone app based orthopaedic care management system to deliver personalised surgical experience, monitor patient engagement and functional outcomes of patients undergoing knee arthroplasty. Results. Over a 12-month period, 124 patients listed for knee arthroplasty were offered access to the app. Average patient age was 65.4 years (range 49 to 86). 13(10.4%) patients were over 80 years. Compliance with app usage was 86.4%. Compliance with post-operative exercises increased following a message through the app. The mean Oxford knee score improved from a pre-op value of 17 to 35 at a mean follow-up of 6 months. Mean numeric rating scale pain score reduced from 7 pre-operatively to 3 at the latest follow-up. 58 patients (46.7%) used the communication feature on the app (text messages, photos, video consultations), reducing telephone calls and patient foot fall in the hospital. Patient satisfaction with the app was very high. Conclusion. We found the virtual care system to be effective in providing patient education, prehabilitation and post-operative rehabilitation along with being an effective channel of communication between patients and the hospital team. Patient satisfaction and compliance was very high

Aims. This study aimed to evaluate the clinical application of the PJI-TNM classification for periprosthetic joint infection (PJI) by determining intraobserver and interobserver reliability. To facilitate its use in clinical practice, an educational app was subsequently developed and evaluated. Methods. A total of ten orthopaedic surgeons classified 20 cases of PJI based on the PJI-TNM classification. Subsequently, the classification was re-evaluated using the PJI-TNM app. Classification accuracy was calculated separately for each subcategory (reinfection, tissue and implant condition, non-human cells, and morbidity of the patient). Fleiss’ kappa and Cohen’s kappa were calculated for interobserver and intraobserver reliability, respectively. Results. Overall, interobserver and intraobserver agreements were substantial across the 20 classified cases. Analyses for the variable ‘reinfection’ revealed an almost perfect interobserver and intraobserver agreement with a classification accuracy of 94.8%. The category 'tissue and implant conditions' showed moderate interobserver and substantial intraobserver reliability, while the classification accuracy was 70.8%. For 'non-human cells,' accuracy was 81.0% and interobserver agreement was moderate with an almost perfect intraobserver reliability. The classification accuracy of the variable 'morbidity of the patient' reached 73.5% with a moderate interobserver agreement, whereas the intraobserver agreement was substantial. The application of the app yielded comparable results across all subgroups. Conclusion. The PJI-TNM classification system captures the heterogeneity of PJI and can be applied with substantial inter- and intraobserver reliability. The PJI-TNM educational app aims to facilitate application in clinical practice. A major limitation was the correct assessment of the implant situation. To eliminate this, a re-evaluation according to intraoperative findings is strongly recommended. Cite this article: Bone Joint Res 2024;13(1):19–27

Artificial Intelligence (AI) is becoming more powerful but is barely used to counter the growth in health care burden. AI applications to increase efficiency in orthopedics are rare. We questioned if (1) we could train machine learning (ML) algorithms, based on answers from digitalized history taking questionnaires, to predict treatment of hip osteoartritis (either conservative or surgical); (2) such an algorithm could streamline clinical consultation. Multiple ML models were trained on 600 annotated (80% training, 20% test) digital history taking questionnaires, acquired before consultation. Best performing models, based on balanced accuracy and optimized automated hyperparameter tuning, were build into our daily clinical orthopedic practice. Fifty patients with hip complaints (>45 years) were prospectively predicted and planned (partly blinded, partly unblinded) for consultation with the physician assistant (conservative) or orthopedic surgeon (operative). Tailored patient information based on the prediction was automatically sent to a smartphone app. Level of evidence: IV. Random Forest and BernoulliNB were the most accurate ML models (0.75 balanced accuracy). Treatment prediction was correct in 45 out of 50 consultations (90%), p<0.0001 (sign and binomial test). Specialized consultations where conservatively predicted patients were seen by the physician assistant and surgical patients by the orthopedic surgeon were highly appreciated and effective. Treatment strategy of hip osteoartritis based on answers from digital history taking questionnaires was accurately predicted before patients entered the hospital. This can make outpatient consultation scheduling more efficient and tailor pre-consultation patient education

Abstract. OBJECTIVES. Application of deep learning approaches to marker trajectories and ground reaction forces (mocap data), is often hampered by small datasets. Enlarging dataset size is possible using some simple numerical approaches, although these may not be suited to preserving the physiological relevance of mocap data. We propose augmenting mocap data using a deep learning architecture called “generative adversarial networks” (GANs). We demonstrate appropriate use of GANs can capture variations of walking patterns due to subject- and task-specific conditions (mass, leg length, age, gender and walking speed), which significantly affect walking kinematics and kinetics, resulting in augmented datasets amenable to deep learning analysis approaches. METHODS. A publicly available (. https://www.nature.com/articles/s41597-019-0124-4. ) gait dataset (733 trials, 21 women and 25 men, 37.2 ± 13.0 years, 1.74 ± 0.09 m, 72.0 ± 11.4 kg, walking speeds ranging from 0.18 m/s to 2.04 m/s) was used as the experimental dataset. The GAN comprised three neural networks: an encoder, a decoder, and a discriminator. The encoder compressed experimental data into a fixed-length vector, while the decoder transformed the encoder's output vector and a condition vector (containing information about the subject and trial) into mocap data. The discriminator distinguished between the encoded experimental data from randomly sampled vectors of the same size. By training these networks jointly using the experimental dataset, the generator (decoder) could generate synthetic data respecting specified conditions from randomly sampled vectors. Synthetic mocap data and lower limb joint angles were generated and compared to the experimental data, by identifying the statistically significant differences across the gait cycle for a randomly selected subset of the experimental data from 5 female subjects (73 trials, aged 26–40, weighing 57–74 kg, with leg lengths between 868–931 mm, and walking speeds ranging from 0.81–1.68 m/s). By conducting these comparisons for this subset, we aimed to assess the synthetic data generated using multiple conditions. RESULTS. We visually inspected the synthetic trials to ensure that they appeared realistic. The statistical comparison revealed that, on average, only 2.5% of the gait cycle showed significantly differences in the joint angles of the two data groups. Additionally, the synthetic ground reaction forces deviated from the experimental data distribution for an average of 2.9% of the gait cycle. CONCLUSIONS. We introduced a novel approach for generating synthetic mocap data of human walking based on the conditions that influence walking patterns. The synthetic data closely followed the trends observed in the experimental data, also in the literature, suggesting that our approach can augment mocap datasets considering multiple conditions, an approach unfeasible in previous work. Creation of large, augmented datasets allows the application of other deep learning approaches, with the potential to generate realistic mocap data from limited and non-lab-based data. Our method could also enhance data sharing since synthetic data does not raise ethical concerns. You can generate and download virtual gait data using our GAN approach from . https://thisgaitdoesnotexist.streamlit.app/. . Declaration of Interest. (b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project

Aims

Hip disease is common in children with cerebral palsy (CP) and can decrease quality of life and function. Surveillance programmes exist to improve outcomes by treating hip disease at an early stage using radiological surveillance. However, studies and surveillance programmes report different radiological outcomes, making it difficult to compare. We aimed to identify the most important radiological measurements and develop a core measurement set (CMS) for clinical practice, research, and surveillance programmes.

Aims

Accurate skeletal age and final adult height prediction methods in paediatric orthopaedics are crucial for determining optimal timing of growth-guiding interventions and minimizing complications in treatments of various conditions. This study aimed to evaluate the accuracy of final adult height predictions using the central peak height (CPH) method with long leg X-rays and four different multiplier tables.

Aims. Prophylactic antibiotic regimens for elective primary total hip and knee arthroplasty vary widely across hospitals and trusts in the UK. This study aimed to identify antibiotic prophylaxis regimens currently in use for elective primary arthroplasty across the UK, establish variations in antibiotic prophylaxis regimens and their impact on the risk of periprosthetic joint infection (PJI) in the first-year post-index procedure, and evaluate adherence to current international consensus guidance. Methods. The guidelines for the primary and alternative recommended prophylactic antibiotic regimens in clean orthopaedic surgery (primary arthroplasty) for 109 hospitals and trusts across the UK were sought by searching each trust and hospital’s website (intranet webpages), and by using the MicroGuide app. The mean cost of each antibiotic regimen was calculated using price data from the British National Formulary (BNF). Regimens were then compared to the 2018 Philadelphia Consensus Guidance, to evaluate adherence to international guidance. Results. The primary choice and dosing of the prophylactic antimicrobial regimens varied widely. The two most used regimens were combined teicoplanin and gentamicin, and cefuroxime followed by two or three doses of cefuroxime eight-hourly, recommended by 24 centres (22.02%) each. The alternative choice and dosing of the prophylactic antimicrobial regimen also varied widely across the 83 centres with data available. Prophylaxis regimens across some centres fail to cover the likeliest causes of surgical site infection (SSI). Five centres (4.59%) recommend co-amoxiclav, which confers no Staphylococcus coverage, while 33 centres (30.28%) recommend cefuroxime, which confers no Enterococcus coverage. Limited adherence to 2018 Philadelphia Consensus Guidance was observed, with 67 centres (61.50%) not including a cephalosporin in their guidance. Conclusion. This analysis of guidance on antimicrobial prophylaxis in primary arthroplasty across 109 hospitals and trusts in the UK has identified widespread variation in primary and alternative antimicrobial regimens currently recommended. Cite this article: Bone Jt Open 2023;4(10):742–749

Abstract. Introduction. Transforming outpatient services is a key commitment set out in the NHS Long Term Plan, with particular emphasis on digital solutions to reduce outpatient follow-up (FU) by 25%. This study looks at the potential for removing knee arthroscopy FU by providing a bespoke multimedia report for each individual patient, generated using the Synergy™ Surgeon App (Arthrex). Methodology. Single District Hospital using a 3 Phase study. Phase 1 – Assessment of cost and environmental impact of outpatient follow up appointments. Phase 2 – Bench marking of existing pathways and patient experience. Phase 3 – Qualitative assessment of multimedia report feedback of 30 patients. Results. Phase 1 – Impact per year for Trust in released clinician time 135hrs. Cost avoidance £40-£60k. Reduction of the carbon footprint from reduced FU of 3132 KgCo2e2. Phase 2 – Deep dive on 2019 n. 353 procedures. 1206 outpatient appointments required. Average 1.2 post-operative appointments. Phase 3 – 87% of patients who received the e-op report needed no further FU. This compares to only 25% using a traditional post op discussion after surgery. 94% of patients felt the report aided their recovery. Conclusions. Reducing patient FU appointments is crucial to the future of the NHS. Achieving this whilst simultaneously improving the quality of patient communication is achievable as this study has demonstrated. The potential scalability of this project to be applied other arthroscopic procedures is enormous. The study has demonstrated patients are comfortable with modern technology and feel it enhances their understanding whilst decreasing the need for routine post-op FU