Periprosthetic hip-joint infection is a multifaceted and highly detrimental outcome for patients and clinicians. The incidence of prosthetic joint infection reported within two years of primary hip arthroplasty ranges from 0.8% to 2.1%. Costs of treatment are over five-times greater in people with periprosthetic hip joint infection than in those with no infection. Currently, there are no national evidence-based guidelines for treatment and management of this condition to guide clinical practice or to inform clinical study design. The aim of this study is to develop guidelines based on evidence from the six-year INFection and ORthopaedic Management (INFORM) research programme. We used a consensus process consisting of an evidence review to generate items for the guidelines and online consensus questionnaire and virtual face-to-face consensus meeting to draft the guidelines.Aims
Methods
This study aims to assess the feasibility of conducting a pragmatic, multicentre randomized controlled trial (RCT) to test the clinical and cost-effectiveness of an informal caregiver training programme to support the recovery of people following hip fracture surgery. This will be a mixed-methods feasibility RCT, recruiting 60 patients following hip fracture surgery and their informal caregivers. Patients will be randomized to usual NHS care, versus usual NHS care plus a caregiver-patient dyad training programme (HIP HELPER). This programme will comprise of three, one-hour, one-to-one training sessions for the patient and caregiver, delivered by a nurse, physiotherapist, or occupational therapist. Training will be delivered in the hospital setting pre-patient discharge. It will include practical skills for rehabilitation such as: transfers and walking; recovery goal setting and expectations; pacing and stress management techniques; and introduction to the HIP HELPER Caregiver Workbook, which provides information on recovery, exercises, worksheets, and goal-setting plans to facilitate a ‘good’ recovery. After discharge, patients and caregivers will be supported in delivering rehabilitation through three telephone coaching sessions. Data, collected at baseline and four months post-randomization, will include: screening logs, intervention logs, fidelity checklists, quality assurance monitoring visit data, and clinical outcomes assessing quality of life, physical, emotional, adverse events, and resource use outcomes. The acceptability of the study intervention and RCT design will be explored through qualitative methods with 20 participants (patients and informal caregivers) and 12 health professionals.Aims
Methods
This study aimed to assess the carbon footprint associated with total hip arthroplasty (THA) in a UK hospital setting, considering various components within the operating theatre. The primary objective was to identify actionable areas for reducing carbon emissions and promoting sustainable orthopaedic practices. Using a life-cycle assessment approach, we conducted a prospective study on ten cemented and ten hybrid THA cases, evaluating carbon emissions from anaesthetic room to recovery. Scope 1 and scope 2 emissions were considered, focusing on direct emissions and energy consumption. Data included detailed assessments of consumables, waste generation, and energy use during surgeries.Aims
Methods
Total hip arthroplasty (THA) is a common procedure to address pain and enhance function in hip disorders such as osteoarthritis. Despite its success, postoperative patient recovery exhibits considerable heterogeneity. This study aimed to investigate whether patients follow distinct pain trajectories following THA and identify the patient characteristics linked to suboptimal trajectories. This retrospective cohort study analyzed THA patients at a large academic centre (NYU Langone Orthopedic Hospital, New York, USA) from January 2018 to January 2023, who completed the Patient-Reported Outcomes Measurement Information System (PROMIS) pain intensity questionnaires, collected preoperatively at one-, three-, six-, 12-, and 24-month follow-up times. Growth mixture modelling (GMM) was used to model the trajectories. Optimal model fit was determined by Bayesian information criterion (BIC), Vuong-Lo-Mendell-Rubin likelihood ratio test (VLMR-LRT), posterior probabilities, and entropy values. Association between trajectory groups and patient characteristics were measured by multinomial logistic regression using the three-step approach.Aims
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