The SOLARIO trial is a randomised controlled non-inferiority trial of antibiotic strategy for bone and joint infection. SOLARIO compares short or long post-operative systemic antibiotic duration, for patients with confirmed infections, who had local antibiotics implanted and no infected metalwork retained when undergoing surgery. This analysis compared systemic antibiotic use in the short (intervention) and long (standard of care) arms of the trial, in the 12 months after index surgery. Data was collected prospectively from study randomisation, within 7 days of index surgery. All systemic antibiotics prescribed for the index infection were recorded, from health records and patient recall, at randomisation, 6 weeks, 3-6 months and 12 months after study entry. Start and end dates for each antibiotic were recorded.Aim
Method
People awaiting surgery for bone and joint infection may be recommended to stop smoking to improve anaesthetic and surgical outcomes. However, restricting curative surgical treatment to non-smokers on the basis of potentially worse surgical outcomes is not validated for functional outcomes or quality of life differences between patients who do and do not smoke. This study used secondary analysis of trial data to ask: do peri-operative non-smokers have a greater improvement in their quality of life 12 months after surgery for bone and joint infection, compared with non-smokers? Participants in the SOLARIO and OVIVA clinical trials who had complete baseline and 12 month EQ-5D-5L or EQ-5D-3L scores were included. Smoking status was ascertained at baseline study enrolment from participant self-report. Normalised quality of life scores were calculated for participants at baseline and 12 months, based on contemporaneous health state scores for England. Baseline and 12 month scores were compared to calculate a post-operative increment in quality of life.Aim
Method
Smoking is known to impair wound healing and to increase the risk of peri-operative adverse events and is associated with orthopaedic infection and fracture non-union. Understanding the magnitude of the causal effect on orthopaedic infection recurrence may improve pre-operative patient counselling. Four prospectively-collected datasets including 1173 participants treated in European centres between 2003 and 2021, followed up to 12 months after surgery for clinically diagnosed orthopaedic infections, were included in logistic regression modelling with Inverse Probability of Treatment Weighting for current smoking status [1–3]. Host factors including age, gender and ASA score were included as potential confounding variables, interacting through surgical treatment as a collider variable in a pre-specified structural causal model informed by clinical experience. The definition of infection recurrence was identical and ascertained separately from baseline factors in three contributing cohorts. A subset of 669 participants with positive histology, microbiology or a sinus at the time of surgery, were analysed separately.Aim
Methods
Recurrence of bone and joint infection, despite appropriate therapy, is well recognised and stimulates ongoing interest in identifying host factors that predict infection recurrence. Clinical prediction models exist for those treated with DAIR, but to date no models with a low risk of bias predict orthopaedic infection recurrence for people with surgically excised infection and removed metalwork. The aims of this study were to construct and internally validate a risk prediction model for infection recurrence at 12 months, and to identify factors that predict recurrence. Predictive factors must be easy to check in pre-operative assessment and relevant across patient groups. Four prospectively collected datasets including 1173 participants treated in European centres between 2003 and 2021, followed up to 12 months after surgery for orthopaedic infections, were included in logistic regression modelling [1–3]. The definition of infection recurrence was identical and ascertained separately from baseline factors in three contributing cohorts. Eight predictive factors were investigated following Aim
Methods
