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. This was an international, multicentre retrospective study of 1,174 revision total hip (THA) and knee arthroplasties (TKA) undergoing I&D for PJI between January 2005 and December 2017. PJI was defined using the Musculoskeletal Infection Society (MSIS) criteria. A total of 52 variables including demographics, comorbidities, and clinical and laboratory findings were evaluated using random forest machine learning analysis. The algorithm was then verified through cross-validation.Aims
Methods
Next-generation sequencing (NGS) is a well-established technique for amplification and sequencing of DNA and has recently gained much attention in many fields of medicine. Our aim was to evaluate the ability of NGS in identifying the causative organism(s) in patients with periprosthetic joint infection (PJI). In this prospective study samples were collected from 78 revision total hip arthroplasties. Synovial fluid, deep tissue and swabs were obtained at the time of surgery and shipped to the laboratory for NGS analysis. Deep tissue specimens were also sent to the institutional lab for culture. PJI was diagnosed using the Musculoskeletal infection society (MSIS) definition of PJI. Thirty-four revisions were considered infected; culture was positive in 25 of these (25/34, 73.5%), while NGS was positive in 26 (26/34, 76.4%). Among the positive cultures, complete concordance between NGS and culture in 21 cases (21/25, 84.0%). 4 cases were discordant. Among these cases, 3 cases were culture-positive and NGS-negative, while 1 was both positive on NGS and culture for disparate organisms. Among the 9 cases of culture-negative PJI(CN-PJI), NGS was able to identify an organism in 4 cases (4/9, 44.4%). The remaining 5 cases were negative on both NGS and culture (5/9, 55.6%). Forty-four revisions were considered to be aseptic; NGS exclusively identified microbes in 7 of 44 “aseptic” revisions (15.9%) and culture exclusively isolated an organism in 3 of 44 cases (6.8%). Both NGS and culture were positive in 1 of case however the result was discordant. The remaining cases (33/44, 75.0%) were both NGS and culture negative. NGS detected several organisms in most positive samples, with a greater number of organisms detected in aseptic compared to septic cases (7 vs. 3.7, respectively). NGS may be a promising technique for identifying the infecting organism in PJI. Our findings suggest that some cases of PJI may be polymicrobial that escape detection using conventional culture.
The World Health Organization (WHO) and the Centre
for Disease Control and Prevention (CDC) recently published guidelines
for the prevention of surgical site infection. The WHO guidelines,
if implemented worldwide, could have an immense impact on our practices
and those of the CDC have implications for healthcare policy in
the United States. Our aim was to review the strategies for prevention of periprosthetic
joint infection in light of these and other recent guidelines. Cite this article:
