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Bone & Joint Research
Vol. 13, Issue 10 | Pages 546 - 558
4 Oct 2024
Li Y Wuermanbieke S Wang F Mu W Ji B Guo X Zou C Chen Y Zhang X Cao L

Aims

The optimum type of antibiotics and their administration route for treating Gram-negative (GN) periprosthetic joint infection (PJI) remain controversial. This study aimed to determine the GN bacterial species and antibacterial resistance rates related to clinical GN-PJI, and to determine the efficacy and safety of intra-articular (IA) antibiotic injection after one-stage revision in a GN pathogen-induced PJI rat model of total knee arthroplasty.

Methods

A total of 36 consecutive PJI patients who had been infected with GN bacteria between February 2015 and December 2021 were retrospectively recruited in order to analyze the GN bacterial species involvement and antibacterial resistance rates. Antibiotic susceptibility assays of the GN bacterial species were performed to screen for the most sensitive antibiotic, which was then used to treat the most common GN pathogen-induced PJI rat model. The rats were randomized either to a PJI control group or to three meropenem groups (intraperitoneal (IP), IA, and IP + IA groups). After two weeks of treatment, infection control level, the side effects, and the volume of antibiotic use were evaluated.


Bone & Joint Research
Vol. 13, Issue 10 | Pages 525 - 534
1 Oct 2024
Mu W Xu B Wang F Maimaitiaimaier Y Zou C Cao L

Aims

This study aimed to assess the risk of acute kidney injury (AKI) associated with combined intravenous (IV) and topical antibiotic therapy in patients undergoing treatment for periprosthetic joint infections (PJIs) following total knee arthroplasty (TKA), utilizing the Kidney Disease: Improving Global Outcomes (KDIGO) criteria for classification.

Methods

We conducted a retrospective analysis of 162 knees (162 patients) that received treatment for PJI post-TKA with combined IV and topical antibiotic infusions at a single academic hospital from 1 January 2010 to 31 December 2022. The incidence of AKI was evaluated using the KDIGO criteria, focussing on the identification of significant predictors and the temporal pattern of AKI development.