Evidence map›Paper›PMID 34124634›Full record

ArticleEClinicalMedicine2021

Population-level faecal metagenomic profiling as a tool to predict antimicrobial resistance in

Olga Tosas Auguet, Rene Niehus, Hyun Soon Gweon, James A Berkley, Joseph Waichungo, Tsi Njim, Jonathan D Edgeworth, Rahul Batra, Kevin Chau, Jeremy Swann and 7 more

Open access · goldAbstract read
In one paragraph

Article in EClinicalMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.1field-weighted citation impact, top 24% of its field
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

10 citing papers in PubMed, 20 citations in OpenAlex.

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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

17 authors at 9 institutions in 5 countries.

Olga Tosas AuguetCentre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK.
Rene NiehusHarvard T.H. Chan School of Public Health, Harvard University, Boston, USA.
Hyun Soon GweonSchool of Biological Sciences, University of Reading, Reading, UK.
James A BerkleyCentre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK.
Joseph WaichungoKEMRI/Wellcome Trust Research Programme, Kilifi, Kenya.
Tsi NjimCentre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK.
Jonathan D EdgeworthCentre for Clinical Infection and Diagnostics Research (CIDR), Department of Infectious Diseases, King's College London, London, UK.
Rahul BatraCentre for Clinical Infection and Diagnostics Research (CIDR), Department of Infectious Diseases, King's College London, London, UK.
Kevin ChauNuffield Department of Medicine, University of Oxford, Oxford, UK.
Jeremy SwannNuffield Department of Medicine, University of Oxford, Oxford, UK.
Sarah A WalkerNuffield Department of Medicine, University of Oxford, Oxford, UK.
Tim E A PetoNuffield Department of Medicine, University of Oxford, Oxford, UK.
Derrick W CrookNuffield Department of Medicine, University of Oxford, Oxford, UK.
Sarah LambleWellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
Paul TurnerCentre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK.
Ben S CooperCentre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK.
Nicole StoesserNuffield Department of Medicine, University of Oxford, Oxford, UK.
National Institute for Health Research · GBUniversity of Oxford · GBKenya Medical Research Institute · KEKing's College London · GBAngkor Hospital for Children · KHCentre for Human Genetics · GBHarvard University · USMahidol University · THUK Centre for Ecology & Hydrology · GB

Funding

Medical Research Council MR/M007367/1
6 · The paper itself

Abstract

backgroundAntimicrobial resistance (AMR) in

methodsIn this exploratory study, a novel approach to population-level prediction of AMR in

findingsPopulation metagenomes comprised samples from 177, 157, and 156 individuals in Kenya, the UK, and Cambodia, respectively, collected between September 2014 and April 2016. Clinical data from independent populations included 910, 3356 and 197 bacterial isolates from blood/cerebrospinal fluid infections in Kenya, the UK and Cambodia, respectively (samples collected between January 2010 and May 2017).

interpretationWhilst our findings are exploratory and require validation, intermittent metagenomics of pooled samples could represent an effective approach for AMR surveillance and to predict population-level AMR in clinical isolates, complementary to ongoing development of laboratory infrastructures processing individual samples.

Indexed as

Antimicrobial resistance surveillanceClinical infectionMetagenomics

Identifiers

PMID34124634
PMCPMC8173267
OpenAlexW3166011525

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.