Single-stage revision for chronic periprosthetic joint infection after knee and hip arthroplasties: indications and treatments

Editorial: Management of PJI/SSI after joint arthroplasty

The management of periprosthetic joint infection (PJI) and surgical site infection (SSI) after joint arthroplasty poses a major challenge in orthopedic surgery. This Editorial provides an overview of the studies published in the special issue "Management of PJI/SSI after Joint Arthroplasty", summarizing the key findings from these studies, which cover a wide range of topics, including stringent preventive strategies, comprehensive diagnostic methods, and personalized treatment modalities. The authors concluded the editorial with their perspectives regarding the status quo of research in this field and future directions for research, such as the development of novel antibiotics, biofilm research, patient-specific risk factors, and the integration of technological advancements (such as machine learning and artificial intelligence) into clinical practice. The authors emphasized the need for continued research, interdisciplinary collaboration, and the application of innovative technologies to enhance patient outcomes and mitigate the burden of these infections on healthcare systems.

Impact of fixation method on femoral bone loss: a retrospective evaluation of stem loosening in first-time revision total hip arthroplasty among two hundred and fifty five patients

PURPOSE

Implant loosening represent the most common indication for stem revision in hip revision arthroplasty. This study compares femoral bone loss and the risk of initial revisions between cemented and uncemented loosened primary stems, investigating the impact of fixation method at primary implantation on femoral bone defects.

METHODS

This retrospective study reviewed 255 patients who underwent their first revision for stem loosening from 2010 to 2022, receiving either cemented or uncemented stem implants. Femoral bone loss was preoperatively measured using the Paprosky classification through radiographic evaluations. Kaplan-Meier analysis estimated the survival probability of the original stem, and the hazard ratio assessed the relative risk of revision for uncemented versus cemented stems in the first postoperative year and the following two to ten years.

RESULTS

Cemented stems showed a higher prevalence of significant bone loss (type 3b and 4 defects: 32.39% vs. 2.72%, p < .001) compared to uncemented stems, which more commonly had type 1 and 2 defects (82.07% vs. 47.89%, p < .001). In our analysis of revision cases, primary uncemented stems demonstrated a 20% lower incidence of stem loosening in the first year post-implantation compared to cemented stems (HR 0.8; 95%-CI 0.3-2.0). However, the incidence in uncemented stems increased by 20% during the subsequent years two to ten (HR 1.2; 95%-CI 0.7-1.8). Septic loosening was more common in cemented stems (28.17% vs. 10.87% in uncemented stems, p = .001). Kaplan-Meier analysis indicated a modestly longer revision-free period for cemented stems within the first ten years post-implantation (p < .022).

CONCLUSION

During first-time revision, cemented stems show significantly larger femoral bone defects than uncemented stems. Septic stem loosening occurred 17.30% more in cemented stems.

Single-stage revision in the management of prosthetic joint infections after total knee arthroplasty - A review of current concepts

Introduction

Prosthetic joint infection (PJI) is a devastating complication following total knee arthroplasty (TKA); and the gold standard surgical approach involves a two-staged, revision TKA (TSR). Owing to the newer, emerging evidence on this subject, there has been gradual shift towards a single-stage revision approach (SSR), with the purported benefits of mitigated patient morbidity, decreased complications and reduced costs. However, there is still substantial lacuna in the evidence regarding the safety and outcome of the two approaches in chronic PJI. This study aimed to comprehensively review of the literature on SSR; and evaluate its role within Revision TKA post PJI.

Methods

The narrative review involved a comprehensive search of the databases (Embase, Medline and Pubmed), conducted on 20th of January 2024 using specific key words. All the manuscripts discussing the use of SSR for the management of PJI after TKA were considered for the review. Among the screened manuscripts, opinion articles, letters to the editor and non-English manuscripts were excluded.

Results

The literature search yielded a total 232 studies. Following a detailed scrutiny of these manuscripts, 26 articles were finally selected. The overall success rate following SSR is reported to range from 73 % to 100 % (and is comparable to TSR). SSR is performed in PJI patients with bacteriologically-proven infection, adequate soft tissue cover, immuno-competent host and excellent tolerance to antibiotics. The main difference between SSR and TSR is that the interval between the 2 stages is only a few minutes instead of 6 weeks. Appropriate topical, intraoperative antibiotic therapy, followed by adequate postoperative systemic antibiotic cover are necessary to ascertain good outcome. Some of the major benefits of SSR over TSR include reduced morbidity, decreased complications (such as arthrofibrosis or anesthesia-associated adverse events), meliorated extremity function, earlier return to activities, mitigated mechanical (prosthesis-associated) complications and enhanced patient satisfaction.

Conclusion

SSR is a reliable approach for the management of chronic PJI. Based on our comprehensive review of the literature, it may be concluded that the right selection of patients, extensive debridement, sophisticated reconstruction strategy, identification of the pathogenic organism, initiation of appropriate antibiotic therapy and ensuring adequate follow-up are the key determinants of successful outcome. To achieve this will undoubtedly require an MDT approach to be taken on a case-by-case basis.

Achieving High Accuracy in Predicting the Probability of Periprosthetic Joint Infection From Synovial Fluid in Patients Undergoing Hip or Knee Arthroplasty: The Development and Validation of a Multivariable Machine Learning Algorithm

Background and objective The current periprosthetic joint infection (PJI) diagnostic guidelines require clinicians to interpret and integrate multiple criteria into a complex scoring system. Also, PJI classifications are often inconclusive, failing to provide a clinical diagnosis. Machine learning (ML) models could be leveraged to reduce reliance on these complex systems and thereby reduce diagnostic uncertainty. This study aimed to develop an ML algorithm using synovial fluid (SF) test results to establish a PJI probability score. Methods We used a large clinical laboratory's dataset of SF samples, aspirated from patients with hip or knee arthroplasty as part of a PJI evaluation. Patient age and SF biomarkers [white blood cell count, neutrophil percentage (%PMN), red blood cell count, absorbance at 280 nm wavelength, C-reactive protein (CRP), alpha-defensin (AD), neutrophil elastase, and microbial antigen (MID) tests] were used for model development. Data preprocessing, principal component analysis, and unsupervised clustering (K-means) revealed four clusters of samples that naturally aggregated based on biomarker results. Analysis of the characteristics of each of these four clusters revealed three clusters (n=13,133) with samples having biomarker results typical of a PJI-negative classification and one cluster (n=4,032) with samples having biomarker results typical of a PJI-positive classification. A decision tree model, trained and tested independently of external diagnostic rules, was then developed to match the classification determined by the unsupervised clustering. The performance of the model was assessed versus a modified 2018 International Consensus Meeting (ICM) criteria, in both the test cohort and an independent unlabeled validation set of 5,601 samples. The SHAP (SHapley Additive exPlanations) method was used to explore feature importance. Results The ML model showed an area under the curve of 0.993, with a sensitivity of 98.8%, specificity of 97.3%, positive predictive value (PPV) of 92.9%, and negative predictive value (NPV) of 99.8% in predicting the modified 2018 ICM diagnosis among test set samples. The model maintained its diagnostic accuracy in the validation cohort, yielding 99.1% sensitivity, 97.1% specificity, 91.9% PPV, and 99.9% NPV. The model's inconclusive rate (diagnostic probability between 20-80%) in the validation cohort was only 1.3%, lower than that observed with the modified 2018 ICM PJI classification (7.4%; p<0.001). The SHAP analysis found that AD was the most important feature in the model, exhibiting dominance among >95% of "infected" and "not infected" diagnoses. Other important features were the sum of the MID test panel, %PMN, and SF-CRP. Conclusions Although defined methods and tools for diagnosis of PJI using multiple biomarker criteria are available, they are not consistently applied or widely implemented. There is a need for algorithmic interpretation of these biomarkers to enable consistent interpretation of the results to drive treatment decisions. The new model, using clinical parameters measured from a patient's SF sample, renders a preoperative probability score for PJI which performs well compared to a modified 2018 ICM definition. Taken together with other clinical signs, this model has the potential to increase the accuracy of clinical evaluations and reduce the rate of inconclusive classification, thereby enabling more appropriate and expedited downstream treatment decisions.