The recently published Prophylactic
Periprosthetic joint infection (PJI) is a difficult complication requiring a comprehensive eradication protocol. Cure rates have essentially stalled in the last two decades, using methods of antimicrobial cement joint spacers and parenteral antimicrobial agents. Functional spacers with higher-dose antimicrobial-loaded cement and antimicrobial-loaded calcium sulphate beads have emphasized local antimicrobial delivery on the premise that high-dose local antimicrobial delivery will enhance eradication. However, with increasing antimicrobial pressures, microbiota have responded with adaptive mechanisms beyond traditional antimicrobial resistance genes. In this review we describe adaptive resistance mechanisms that are relevant to the treatment of PJI. Some mechanisms are well known, but others are new. The objective of this review is to inform clinicians of the known adaptive resistance mechanisms of microbes relevant to PJI. We also discuss the implications of these adaptive mechanisms in the future treatment of PJI. Cite this article:
Aims. The diagnosis of periprosthetic joint infection can be difficult
due to the high rate of culture-negative infections. The aim of
this study was to assess the use of next-generation sequencing for
detecting organisms in synovial fluid. Materials and Methods. In this prospective, single-blinded study, 86 anonymized samples
of synovial fluid were obtained from patients undergoing aspiration
of the hip or knee as part of the investigation of a periprosthetic
infection. A panel of synovial fluid tests, including levels of
C-reactive protein, human neutrophil elastase, total neutrophil
count, alpha-defensin, and culture were performed prior to next-generation
sequencing. Results. Of these 86 samples, 30 were alpha-defensin-positive and culture-positive
(Group I), 24 were alpha-defensin-positive and culture-negative
(Group II) and 32 were alpha-defensin-negative and culture-negative
(Group III). Next-generation sequencing was concordant with 25 results
for Group I. In four of these, it detected
In 2013, we introduced a specialized, centralized, and interdisciplinary team in our institution that applied a standardized diagnostic and treatment algorithm for the management of prosthetic joint infections (PJIs). The hypothesis for this study was that the outcome of treatment would be improved using this approach. In a retrospective analysis with a standard postoperative follow-up, 95 patients with a PJI of the hip and knee who were treated with a two-stage exchange between 2013 and 2017 formed the study group. A historical cohort of 86 patients treated between 2009 and 2011 not according to the standardized protocol served as a control group. The success of treatment was defined according to the Delphi criteria in a two-year follow-up.Aims
Patients and Methods
‘Big data’ is a term for data sets that are so
large or complex that traditional data processing applications are
inadequate. Billions of dollars have been spent on attempts to build predictive
tools from large sets of poorly controlled healthcare metadata.
Companies often sell reports at a physician or facility level based
on various flawed data sources, and comparative websites of ‘publicly
reported data’ purport to educate the public. Physicians should
be aware of concerns and pitfalls seen in such data definitions,
data clarity, data relevance, data sources and data cleaning when
evaluating analytic reports from metadata in health care. Cite this article:
Periprosthetic joint infection (PJI) is one of
the most feared and challenging complications following total knee arthroplasty.
We provide a detailed description of our current understanding regarding
the management of PJI of the knee, including diagnostic aids,
pre-operative planning, surgical treatment, and outcome. Cite this article:
The surgical community is plagued with a reputation
for both failing to engage and to deliver on clinical research.
This is in part due to the absence of a strong research culture, however
it is also due to a multitude of barriers encountered in clinical
research; particularly those involving surgical interventions. ‘Trauma’
amplifies these barriers, owing to the unplanned nature of care,
unpredictable work patterns, the emergent nature of treatment and
complexities in the consent process. This review discusses the barriers
to clinical research in surgery, with a particular emphasis on trauma.
It considers how barriers may be overcome, with the aim to facilitate
future successful clinical research. Cite this article: