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Toward a clinical algorithm for the detection of periprosthetic joint infections using targeted NGS
Ander Uribarri1, Lucia Henriquez1, Iñaki Beguiristain1
1Navarre Hospital Complex, Clinical Microbiology Department, Spain; Instituto de Investigación Sanitaria de Navarra (IdiSNA), Spain.
Objectives:
Periprosthetic joint infection (PJI) represents a severe surgical complication, and despite advances have been made in PJI diagnosis, many cases remain microbiologically negative. In this context, metagenomic Next-Generation Sequencing (mNGS) emerged as a promising diagnostic tool for PJI. This study aimed to establish objective, widely-usable methodology and criteria for diagnosing PJI based on sequence analysis.
Methods:
We retrospectively analyzed sonication fluid samples from 34 patients who underwent surgical treatment for PJI in 2022. A 16S rRNA-based targeted NGS (tNGS) approach was carried out amplifying 16S rRNA gene for 25 and 35 cycles. Sequencing was performed on the Illumina MiSeq platform and data was processed using nf-core/ampliseq.
Results:
A significantly higher number of classifiable reads was observed in PJI cases (p < 0.0001). A negative control database was created using negative tNGS controls and samples from patients classified as aseptic failure. The database was then employed to establish three tNGS diagnostic criteria. The diagnostic efficacy of tNGS employing these criteria was assessed through receiver operating characteristic (ROC) curve analysis, which revealed optimal results after 25 cycles of 16S rRNA amplification (AUC = 0.924, p < 0.0001). An optimal cut-off of >12 % was calculated, obtaining a sensitivity of 100 % (95 % CI 75.3 %-100 %) and a specificity of 90.5 % (95 % CI 69.9 %-98.8 %). In both of our culture-negative PJI cases potential pathogens were detected: Listeria and Cutibacterium.
Conclusions:
To the best of our knowledge, this is one of the few studies that attempts to establish standardized criteria and diagnostic cut-offs for the analysis of PJI tNGS data. We believe that these results could serve as a valuable reference for future PJI metagenomics diagnostic studies.
Insights
This study establishes standardized criteria for diagnosing periprosthetic joint infection (PJI) using targeted Next-Generation Sequencing (tNGS). The developed criteria demonstrate high accuracy, offering a reliable method for identifying PJI, even in culture-negative cases.
Area of Science:
- Medical Microbiology
- Genomics
- Infectious Diseases
Background:
- Periprosthetic joint infection (PJI) is a severe complication with diagnostic challenges, particularly in microbiologically negative cases.
- Metagenomic Next-Generation Sequencing (mNGS) shows promise for PJI diagnosis.
Purpose of the Study:
- To establish objective, widely usable methodology and criteria for diagnosing PJI using sequence analysis.
- To evaluate the diagnostic efficacy of targeted Next-Generation Sequencing (tNGS) for PJI.
Main Methods:
- Retrospective analysis of sonication fluid samples from 34 PJI patients.
- 16S rRNA-based targeted NGS (tNGS) with 25 and 35 amplification cycles.
- Data processing using nf-core/ampliseq and creation of a negative control database.
Main Results:
- Significantly higher classifiable reads in PJI cases (p < 0.0001).
- Optimal diagnostic efficacy achieved with 25 amplification cycles (AUC = 0.924, p < 0.0001).
- Established criteria yielded 100% sensitivity and 90.5% specificity with a >12% cut-off.
Conclusions:
- This study presents one of the few attempts to standardize criteria and diagnostic cut-offs for PJI tNGS data analysis.
- The developed criteria and cut-offs can serve as a valuable reference for future PJI metagenomics diagnostic studies.
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