A scoping review of the current evidence on treatment and outcomes following synovial sepsis.

ABSTRACT

BACKGROUND: Synovial sepsis is a frequent cause of morbidity and mortality in horses. Despite advances in diagnostics and treatments, persistent infection or chronic lameness can occur. OBJECTIVES: To perform a scoping review to identify and evaluate the current evidence on the factors implicated in the success of treatment for synovial sepsis. STUDY DESIGN: Joanna Briggs Institute scoping review. METHODS: A protocol was registered, and a systematic literature search was performed on CAB abstracts, Medline, Scopus and Embase. Inclusion and exclusion criteria were developed and studies systematically reviewed against this. Studies relating to factors affecting treatment success following synovial sepsis were retained and data was extracted on study method, population characteristics and factors significantly associated with treatment outcome. RESULTS: In total, 2338 studies were identified, and 61 were included to full paper analysis. Eight papers reported significant factors, identifying 15 risk factors associated with two measurements of outcome, either survival and/or return to athletic function. The 15 factors were identified and categorised into pre-, intra- and post-operative factors. Risk factors that were identified included the number or type of synovial structures involved, the presence of pannus, tendon and bone pathology, and the use of systemic antimicrobials. There were many discrepancies in inclusion criteria of cases of synovial sepsis as well as measurement and description of outcome variables. MAIN LIMITATIONS: Non-English language studies or conference proceedings were not included. Only small numbers of papers had similar findings. CONCLUSIONS: Standardisation of inclusion criteria is essential to enable comparisons and analysis between studies on synovial sepsis. Future studies should use methodologies to reduce bias including multicentre and multinational studies, prospective study design and robust statistical modelling.