ArticleBone & joint research2025
Detection limitations of bacteria in tissue samples.
Article in Bone & joint research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The incidence of detectable bacterial contaminated primary joint arthroplasties and periprosthetic joint infection: a systematic review and meta-analysis.EFORT open reviews · 2026Pooled it
- Impact of Narrow Empiric Antibiotic Spectrum and Patient Characteristics on Clinical Outcomes in Bone and Joint Infections: A Retrospective Cohort Study.Antibiotics (Basel, Switzerland) · 2026Article
- Effective therapy for periprosthetic joint infection through sequential application of non-contact low-frequency ultrasonic debridement and antiseptic solutions.Bone & joint research · 2026Article
- CXCL5 suppresses osteoclastogenesis and protects against lipoteichoic acid-induced bone loss by modulating PLCγ2 and c-Fos signalling in gram-positive periprosthetic joint infection.Bone & joint research · 2026Article
- Advancing diagnostics in suspected periprosthetic joint infections using synthetic synovial fluid and microcalorimetry.Journal of bone and joint infection · 2026Article
- Exploring the Value of Paired Microbiology and Histology in Chronic Osteomyelitis and Fracture-Related Infections.Antibiotics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Successful identification of bacteria in tissue samples requires careful consideration of multiple factors, including sample type and quality, the type of bacteria being detected, and the sensitivity and specificity of the detection method. Here, we address the issues of detecting a small number of bacteria, often found in biofilms and heterogeneously distributed in a large volume (the surgical site with suspected infection). Specifically, the study seeks to address the difficulties in detecting small numbers of bacteria, and to evaluate the impact of bacterial aggregation on the probability of successful detection. Methods: We present simple formulae for the probability of detecting bacteria in different infection scenarios where the number of bacteria and size of bacterial aggregates are incorporated as variables. We define a critical aggregation parameter, above which the probability of sampling bacteria decreases dramatically. Results: Our calculations demonstrate that aggregation of bacteria in tissues can strongly impact the probability of detection, where an increase in aggregate size results in a reduced probability of obtaining a positive biopsy. Our calculations underscore the challenges in effectively sampling tissue for diagnostic purposes, particularly in low-grade infections characterized by small bacterial quantities within aggregates. Below the critical aggregation parameter, obtaining five tissue specimens is associated with a high probability of detecting infection, but at a higher aggregation level, increasing the number of specimens is rendered ineffective, resulting in culture-negative diagnoses. Conclusion: We hypothesize that the high false-negative rate in diagnosing orthopaedic surgical site infections, such as periprosthetic joint infections, could be partly influenced by the heterogeneous bacterial distribution and the sampling complexities of such populations outlined here. Homogenization of tissue specimens is a technique to enhance the surface area which potentially could increase the detection of heterogeneously distributed bacteria.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.