Fast and accurate identification of pathogens causing periprosthetic joint infections (PJI) is essential to initiate effective antimicrobial treatment. Culture-based approaches frequently yield false negative results, despite clear signs of infection. This may be due to the use of general growth media, which do not mimic the conditions at site of infection. Possible alternative approaches include DNA-based techniques, the use of In this study, 120 synovial fluid samples were included, aspirated from patients with clinical signs of PJI. For these samples microbiology data (obtained in the clinical microbiology lab using standard procedures) and next generation sequencing (NGS) data, were available. The samples were incubated in the SSF medium at different oxygen levels (21% O2, 3% O2 and 0% O2) for 10 days. Every 24h, the presence of growth was checked. From positive samples, cultures were purified on Columbia blood agar and identified using MALDI-TOF. In parallel, heat produced by metabolically active microorganisms present in the samples was measured using ITC (calScreener, Symcel), (96h at 37°C, in SSF, BHI and thioglycolate). From the resulting thermograms the ‘time to activity’ could be derived. The accuracy and time to detection were compared between the different detection methods.Aim
Methods
There is growing evidence that bacteria encountered in periprosthetic joint infections (PJI) form surface-attached biofilms on prostheses, as well as biofilm aggregates embedded in synovial fluid and tissues. However, models allowing the investigation of these biofilms and the assessment of their antimicrobial susceptibility in physiologically relevant conditions are currently lacking. To address this, we developed a synthetic synovial fluid (SSF) model and we validated this model in terms of growth, aggregate formation and antimicrobial susceptibility testing, using multiple PJI isolates. 17 PJI isolates were included, belonging to Aim
Methods
