Prosthetic joint infection diagnosis applying the three-level European Bone and Joint Infection Society (EBJIS) approach

Abstract

Sensitive and specific tests for the diagnosis of prosthetic joint infection (PJI) are lacking. The aim of this study was to report clinical and microbiological findings of consecutive patients diagnosed with PJI at the University Hospital of Perugia, Perugia, Italy, and to validate these diagnoses utilizing the European Bone and Joint Infection Society (EBJIS) three-level diagnostic approach from 2021. Patients with a PJI diagnosis were included in this study and examined retrospectively. Overall, 133 patients were diagnosed with PJI: mean age 72 years, 54.9% female, and 55.6% with more than one comorbidity. The most frequent involved joints were hip 47% and knee 42%. Aetiology was identified in 88/133 (66.2%): staphylococci resulted the most frequent microorganisms and over 80% (45/54) resulted rifampin susceptible. Applying the EBJIS approach, PJI diagnosis resulted: confirmed in 101 (75.9%), likely in 25 (18.8%), and unlikely in 7 (5.3%). Likely PJIs aetiology was Staphylococcus aureus 11/25, coagulase-negative staphylococci 8/25, Streptococcus agalactiae 3/25, viridans group streptococci 2/25, and Pseudomonas aeruginosa 1/25. No statistically significant differences were detected among the three diagnosis groups with regard to clinical characteristics with the exception of a higher number of confirmed PJIs occurring < 3 months after implantation. The logistic regression analysis did not disclose any independent predictor of confirmed PJIs. We recommend using all the diagnostic tests available to approach PJI diagnosis, and suggest caution before rejecting PJI diagnosis in the presence of highly virulent microorganisms from a single sample, in patients without sinus tract, and in those receiving antimicrobial at the time microbiologic samples are collected. Study approved by Umbrian Regional Ethical Committee, Perugia, Italy, Prot. N. 23,124/21/ON of 10.27.2021.

Keywords: Clinical; Diagnosis algorithm; EBJIS; Microbiology; Prosthetic joint infection.