Periprosthetic joint infection (PJI) is the most devastating complication following total joint arthroplasty (TJA) and is posing a global healthcare challenge as the demand for TJA mounts. Two-stage exchange arthroplasty with the placement of antibiotic-loaded spacers has been shown to be efficacious against chronic PJI. This study aimed to review the key concepts, types, and outcome evaluations of articulating spacers in the two-stage exchange for PJI. Previous studies indicated that articulating spacers have been widely used due to better functional improvement and a comparable infection control rate relative to static spacers. Several types of articulating spacers are reportedly available, including hand-made spacers, spacers fashioned from molds, commercially preformed spacers, spacers with additional metal or polyethylene elements, new or autoclaved prosthesis, custom-made articulating spacers, and 3D printing-assisted spacers. However, limited evidence suggested no significant difference in clinical outcomes among the different subtypes of articulating spacers. Surgeons should be familiar with different treatment strategies when using various spacers to know which is the most appropriate.

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Aims Periprosthetic joint infection (PJI) is one of the most serious complications after total joint arthroplasty (TJA) but the characterization of the periprosthetic environment microbiome after TJA remains unknown. Here, we performed a prospective study based on metagenomic next-generation sequencing to explore the periprosthetic microbiota in patients with suspected PJI. Methods We recruited 28 patients with culture-positive PJI, 14 patients with culture-negative PJI, and 35 patients without PJI, which was followed by joint aspiration, untargeted metagenomic next-generation sequencing (mNGS), and bioinformatics analysis. Our results showed that the periprosthetic environment microbiome was significantly different between the PJI group and the non-PJI group. Then, we built a “typing system” for the periprosthetic microbiota based on the RandomForest Model. After that, the ‘typing system’ was verified externally. Results We found the periprosthetic microbiota can be classified into four types generally: “Staphylococcus type,” “Pseudomonas type,” “Escherichia type,” and “Cutibacterium type.” Importantly, these four types of microbiotas had different clinical signatures, and the patients with the former two microbiota types showed obvious inflammatory responses compared to the latter ones. Based on the 2014 Musculoskeletal Infection Society (MSIS) criteria, clinical PJI was more likely to be confirmed when the former two types were encountered. In addition, the Staphylococcus spp. with compositional changes were correlated with C-reactive protein levels, the erythrocyte sedimentation rate, and the synovial fluid white blood cell count and granulocyte percentage. Conclusions Our study shed light on the characterization of the periprosthetic environment microbiome in patients after TJA. Based on the RandomForest model, we established a basic “typing system” for the microbiota in the periprosthetic environment. This work can provide a reference for future studies about the characterization of periprosthetic microbiota in periprosthetic joint infection patients.

Background Periprosthetic joint infection is a serious complication after total joint arthroplasty. Despite that alpha-defensin was used as diagnostic test in the 2018 ICM (international consensus meeting) criteria, its position in the PJI diagnostic pipeline was controversial. Therefore, we performed a retrospective pilot study to identify whether synovial fluid alpha-defensin test was necessary when corresponding synovial fluid analysis (WBC count, PMN% and LE tests) was performed. Methods Between May 2015 and October 2018, a total of 90 suspected PJI patients who underwent revisions after TJA were included in this study. Based on the 2018 ICM criteria, the interobserver agreements between preoperative diagnostic results and postoperative diagnostic results and the interobserver reliability between preoperative diagnostic results and postoperative diagnostic results with or without synovial fluid alpha-defensin tests were calculated. After that, the ROC analysis, and the direct cost-effectiveness of adding alpha-defensin was performed. Results There were 48,16 and 26 patients in the PJI group, inconclusive group and non-PJI group, respectively. Adding the alpha-defensin tests into 2018 ICM criteria can’t change the preoperative diagnostic results, postoperative diagnostic results, and the concordance between preoperative and postoperative diagnostic results. Moreover, the Risk–benefit Ratio is over 90 per changed decision and the direct cost-effectiveness of alpha-defensin was more than $8370($93*90) per case. Conclusions Alpha-defensin assay exhibit high sensitivity and specificity for PJI detection as a standalone test based on the 2018 ICM criteria. However, the additional order of Alpha-defensin can’t offer additional evidence for PJI diagnosis when corresponding synovial fluid analysis was performed (synovial fluid WBC count, PMN% and LE strip tests). Evidence level Level II, Diagnostic study.