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Novel Diagnostics in Revision Arthroplasty: Implant Sonication and Multiplex Polymerase Chain Reaction
Published on: December 3, 2017
Optimizing multimodal diagnostic strategies for periprosthetic joint infection: Current advances and integration
Jianhui Zhai1, Yumei Zhang2, Jinwang Dong3
1Department of Joint Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, China.
None:
Periprosthetic Joint Infection (PJI) represents a significant complication following joint arthroplasty, highlighting the necessity for accurate diagnostic strategies to guide clinical decision-making. This review highlights advancements in PJI diagnostic techniques, including pathogen identification, biomarker profiling, imaging, and molecular biology techniques. Diagnostic accuracy in culture-based pathogen identification is influenced by factors such as sampling method, antibiotic administration, specimen type, incubation period, and type of culture media. Methods such as ultrasonic agitation and chemical dissolution (e.g., dithiothreitol, DTT) have demonstrated potential in improving pathogen identification in biofilms. Moreover, commonly employed biomarkers include C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), α-defensin, D-dimer, and interleukin-6 (IL-6), though each exhibits variable specificity and sensitivity. Imaging techniques such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) offer diverse diagnostic capabilities, with PET/CT demonstrating high sensitivity and specificity despite higher costs. Furthermore, molecular biology methods such as Polymerase Chain Reaction (PCR) and next-generation sequencing (NGS) provide rapid and sensitive detection, which is especially valuable in culture-negative and polymicrobial infections. However, financial constraints limit their routine clinical use. Future research should focus on further optimizing diagnostic modality, exploring novel diagnostic techniques (such as machine learning, (ML) analysis and point-of-care, (POC) diagnostics), while integrating multimodal strategies to enhance the accuracy and efficiency of PJI identification.
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