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
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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
The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA). Synovial fluid (SF) from patients with esOA, non-OA knee injury, and inflammatory knee arthritis were analyzed for 35 potential markers using immunoassays. Partial least square discriminant analysis (PLS-DA) was used to derive a biomarker model for cohort classification. The ability of the biomarker model to diagnose esOA was validated by identical wide-spectrum SF analysis of a test cohort of ten patients with esOA.Aims
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