Periprosthetic Joint Infection: Modern Diagnostic Markers and Their Limitations

Abstract

Joint arthroplasty (JA) is a common surgical procedure that provides significant relief from degenerative joint pain, notably increases quality of life for patients, and markedly improves functional status. However, despite its many benefits, JA is not without risk, and one of the greatest risks patients who undergo JA face is periprosthetic joint infections (PJIs). PJIs are defined as infections occurring after the implantation of an artificial joint and have been shown to result in substantial morbidity, high treatment costs, and increased long-term mortality. Despite improvements in diagnostic criteria over time, early and accurate diagnosis of PJIs continues to be challenging. Challenges include overlapping clinical symptoms, a range of potential host responses to infection, and the absence of a single definitive diagnostic marker. The purpose of this narrative review is to evaluate the various diagnostic markers currently available for use in assessing PJI. A comprehensive search and review of all systematic reviews, meta-analyses, and diagnostic accuracy studies was done. While traditional serum inflammatory markers, such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), can remain useful in identifying infections, their sensitivity is significantly lower in cases of chronic or low-grade infections. Synovial fluid biomarkers, such as leukocyte esterase, alpha-defensin, and calprotectin, have demonstrated improved diagnostic performance when compared to traditional inflammatory markers. These markers appear to have superior ability to rule in or rule out infection; however, cost, variability in threshold values, and susceptibility to confounding variables limit their widespread adoption. Molecular diagnostics, including polymerase chain reaction (PCR) and next-generation sequencing (NGS), represent potentially powerful tools for detecting low-virulence and culture-negative organisms; however, these tools remain limited by cost, laboratory requirements, and interpretive difficulties. The evidence supports a multi-modal approach to diagnosing PJI. This approach would utilize a combination of clinical assessment and established serum and synovial markers, along with the selective use of advanced biomarkers and molecular assays. Future advancements in diagnosing PJI depend on several factors including the development of standardized diagnostic thresholds, improvement in the affordability and accessibility of point-of-care tools, and validation of artificial intelligence (AI)-enhanced diagnostic models capable of utilizing large amounts of complex data to increase diagnostic confidence and ultimately lead to better patient outcomes.