A growing number of recent investigations on the human genome, gut microbiome, and proteomics suggests that the loss of mucosal barrier function, particularly in the gastrointestinal tract, may substantially affect antigen trafficking, ultimately influencing the close bidirectional interaction between the gut microbiome and the immune system. This cross-talk is highly influential in shaping the host immune system and ultimately clinical infections. The hypothesis of the current study was that a change in microbiome and/or breach in GI epithelial barrier could be partially responsible for development of periprosthetic joint infections (PJI).

Multiple biomarkers of gut barrier disruption were tested in parallel in plasma samples collected as part of a prospective cohort study of patients undergoing revision arthroplasty for aseptic failures or PJI (As defined by the 2018 ICM criteria). All blood samples were collected before any antibiotic was administered. Samples were tested for Zonulin, soluble CD14 (sCD14), and lipopolysaccharide (LPS) using commercially available enzyme-linked immunosorbent assays. Statistical analysis consisted of descriptive statistics, Mann-Whitney t-test, and Kruskal-Wallis test.

A total of 134 patients were consented and included in the study. 44 were classified as PJI (30 chronic and 14 acute), and 90 as aseptic failures (26 primaries and 64 aseptic revisions). Both Zonulin and sCD14, but not LPS, were found to be significantly increased in the PJI group compared to non-infected cases (p<0.001; p=0.003). Higher levels of Zonulin were found in acute infections compared to chronic PJI (p=0.005

This prospective ongoing study reveals a possible link between gut permeability and the ‘gut-immune-joint axis’ in PJI. If this association continues to be born out with larger cohort recruitment and more in-depth analysis, it would have an immense implication in managing patients with PJI. In addition to administering antimicrobials, patients with PJI and other orthopedic infections may require gastrointestinal modulators such as pro and prebiotics.

Aims

Failure of irrigation and debridement (I&D) for prosthetic joint infection (PJI) is influenced by numerous host, surgical, and pathogen-related factors. We aimed to develop and validate a practical, easy-to-use tool based on machine learning that may accurately predict outcome following I&D surgery taking into account the influence of numerous factors.