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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The annual incidence of hip fracture is 620 000 in the European Union. The cost of this clinical problem has been estimated at 1.75 million disability-adjusted life years lost, equating to 1.4% of the total healthcare burden in established market economies. Recent guidance from The National Institute for Health and Clinical Excellence (NICE) states that research into the clinical and cost effectiveness of total hip arthroplasty (THA) as a treatment for hip fracture is a priority. We asked the question: can a trial investigating THA for hip fracture currently be delivered in the NHS? We performed a contemporaneous process evaluation that provides a context for the interpretation of the findings of WHiTE Two – a randomised study of THA for hip fracture. We developed a mixed methods approach to situate the trial centre within the context of wider United Kingdom clinical practice. We focused on fidelity, implementation, acceptability and feasibility of both the trial processes and interventions to stakeholder groups, such as healthcare providers and patients.Objectives
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