Evidence map›Paper›PMID 39587555›Full record

ArticleBMC medical genomics2024

Development of a clinical metagenomics workflow for the diagnosis of wound infections.

Carl Halford, Thanh Le Viet, Katie Edge, Paul Russell, Nathan Moore, Fiona Trim, Lluis Moragues-Solanas, Roman Lukaszewski, Simon A Weller, Matthew Gilmour

Abstract read
In one paragraph

Article in BMC medical genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Carl HalfordDefence Science and Technology Laboratory, Porton Down, Salisbury, Wiltshire, UK.
Thanh Le VietQuadram Institute Bioscience, Norwich Research Park, Norwich, Norfolk, UK.
Katie EdgeDefence Science and Technology Laboratory, Porton Down, Salisbury, Wiltshire, UK.
Paul RussellSalisbury NHS Foundation Trust, Salisbury Hospital, Salisbury, UK.
Nathan MooreHampshire Hospitals NHS Foundation Trust, Basingstoke and North Hampshire Hospital, Basingstoke, UK.
Fiona TrimSalisbury NHS Foundation Trust, Salisbury Hospital, Salisbury, UK.
Lluis Moragues-SolanasUniversity of East Anglia, Norwich, Norfolk, UK.
Roman LukaszewskiDefence Science and Technology Laboratory, Porton Down, Salisbury, Wiltshire, UK.
Simon A WellerDefence Science and Technology Laboratory, Porton Down, Salisbury, Wiltshire, UK.
Matthew GilmourUniversity of East Anglia, Norwich, Norfolk, UK. matthew.gilmour@quadram.ac.uk.

Funding

Biotechnology and Biological Sciences Research Council BB/CCG1860/1Biotechnology and Biological Sciences Research Council BB/X011011/1Medical Research Council MR/R015937/1
6 · The paper itself

Abstract

backgroundWound infections are a common complication of injuries negatively impacting the patient's recovery, causing tissue damage, delaying wound healing, and possibly leading to the spread of the infection beyond the wound site. The current gold-standard diagnostic methods based on microbiological testing are not optimal for use in austere medical treatment facilities due to the need for large equipment and the turnaround time. Clinical metagenomics (CMg) has the potential to provide an alternative to current diagnostic tests enabling rapid, untargeted identification of the causative pathogen and the provision of additional clinically relevant information using equipment with a reduced logistical and operative burden.

methodsThis study presents the development and demonstration of a CMg workflow for wound swab samples. This workflow was applied to samples prospectively collected from patients with a suspected wound infection and the results were compared to routine microbiology and real-time quantitative polymerase chain reaction (qPCR).

resultsWound swab samples were prepared for nanopore-based DNA sequencing in approximately 4 h and achieved sensitivity and specificity values of 83.82% and 66.64% respectively, when compared to routine microbiology testing and species-specific qPCR. CMg also enabled the provision of additional information including the identification of fungal species, anaerobic bacteria, antimicrobial resistance (AMR) genes and microbial species diversity.

conclusionsThis study demonstrates that CMg has the potential to provide an alternative diagnostic method for wound infections suitable for use in austere medical treatment facilities. Future optimisation should focus on increased method automation and an improved understanding of the interpretation of CMg outputs, including robust reporting thresholds to confirm the presence of pathogen species and AMR gene identifications.

Indexed as

MetagenomicsWorkflowWound InfectionBacteriaHumansClinical metagenomicsMolecular diagnosticsNanopore sequencingWound infections

Identifiers

PMID39587555
PMCPMC11587571

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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.