Evidence map›Paper›PMID 41508107›Full record

ArticleCritical care (London, England)2026

Real-life impact of clinical metagenomics in the intensive care unit: a multicenter retrospective study in greater paris area hospitals.

Pierre Bay, Pierre Cappy, Christophe Rodriguez, Nicolas Mongardon, Matthieu Petit, Guillaume Voiriot, Romain Sonneville, Marc Pineton de Chambrun, Tomas Urbina, Taï Pham and 10 more

Abstract readMulticenter Study
In one paragraph

Article in Critical care (London, England), 2026. 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. Clinical metagenomics: a call to action.Intensive care medicine · 2026
    Article
  2. Review
  3. Review
  4. Metagenomic next-generation sequencing: new horizons in microbiology.Frontiers in cellular and infection microbiology · 2026
    Review
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

20 authors.

Pierre Bay *DMU Médecine, Service de Médecine Intensive Réanimation, AP-HP (Assistance Publique-Hôpitaux de Paris), Hôpitaux Universitaires Henri Mondor, 1 Rue Gustave Eiffel, Créteil, 94010, France. pierre.bay@aphp.fr.
Pierre Cappy *UPEC (Université Paris Est Créteil) INSERM, Unité U955, Équipe 18, Créteil, 94010, France.
Christophe RodriguezUPEC (Université Paris Est Créteil) INSERM, Unité U955, Équipe 18, Créteil, 94010, France.
Nicolas MongardonUniversité Paris Est Créteil, INSERM, IMRB, Créteil, F-94010, France.
Matthieu PetitMedical Intensive Care Unit, Ambroise Paré Hospital, APHP Inserm, CESP, University Versailles Saint Quentin, University Paris Saclay, Guyancourt, U1018, France.
Guillaume VoiriotGRC 40 SoLID, Assistance Publique - Hôpitaux de Paris, Hôpital Tenon, Service de Médecine Intensive Réanimation, INSERM CRSA UMRS_938 Team 5PMed, Sorbonne Université, Paris, France.
Romain SonnevilleUMR 1137, Université Paris Cité, INSERM, Paris, 75018, France.
Marc Pineton de ChambrunUMRS_1166-ICAN, Service de Medecine Intensive Reanimation, iCAN, Institute of Cardiometabolism and Nutrition, INSERM, Hôpital de La Pitié-Salpêtrière, Sorbonne Universités, 47, Bd de L'Hôpital, Paris Cedex 13, 75651, France.
Tomas UrbinaMedical Intensive Care, Unit Hôpital Saint-Antoine Assistance Publique-Hôpitaux de Paris , Paris, France.
Taï PhamService de Médecine Intensive-Réanimation, DMU CORREVE, Groupe de Recherche CARMAS, AP-HP, Hôpital de Bicêtre, FHU SEPSIS, Hôpitaux Universitaires Paris-Saclay, Le Kremlin-Bicêtre, France.
Maxens DecavèleService de Médecine Intensive-Réanimation-SRPR, AP-HP, Groupe Hospitalier Universitaire APHP-Sorbonne Université, Site Pitié-Salpêtrière, Département R3S, Paris, France.
Sarah BenghanemService de Médecine Intensive - Réanimation, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris. Centre, Université Paris Cité, 27 Rue du Faubourg Saint-Jacques, Paris, 75014, France.
Damien ContouService de Réanimation Polyvalente, CH Victor Dupouy, Argenteuil, France.
Raphaël LepeuleDépartement Prévention Diagnostic Et Traitement Des Infections, AP-HP, Unité Transversale de Traitement Des Infections, Hôpital Henri Mondor, Créteil, 94000, France.
Giovanna MelicaUPEC (Université Paris Est Créteil) INSERM, Unité U955, Équipe 18, Créteil, 94010, France.
Nicolas de ProstDMU Médecine, Service de Médecine Intensive Réanimation, AP-HP (Assistance Publique-Hôpitaux de Paris), Hôpitaux Universitaires Henri Mondor, 1 Rue Gustave Eiffel, Créteil, 94010, France.
Cécile AngebaultDepartment of Microbiology, GenoBioMICS Platform, Hôpital Henri Mondor, AP-HP, Université Paris-Est, Créteil, France.
Armand Mekontso DessapDMU Médecine, Service de Médecine Intensive Réanimation, AP-HP (Assistance Publique-Hôpitaux de Paris), Hôpitaux Universitaires Henri Mondor, 1 Rue Gustave Eiffel, Créteil, 94010, France.
Paul-Louis WoertherUPEC (Université Paris Est Créteil) INSERM, Unité U955, Équipe 18, Créteil, 94010, France.
Keyvan RazaziDMU Médecine, Service de Médecine Intensive Réanimation, AP-HP (Assistance Publique-Hôpitaux de Paris), Hôpitaux Universitaires Henri Mondor, 1 Rue Gustave Eiffel, Créteil, 94010, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInfection are the leading cause of intensive care unit (ICU) admission, yet conventional microbiological methods frequently fail to identify the causative pathogen. Metagenomic next-generation sequencing (mNGS) is an emerging, unbiased, pan-pathogen diagnostic tool. However, its real-world microbiological and clinical impact in the ICU remains poorly characterized. This study aimed to assess the microbiological yield and clinical impact of mNGS when implemented in routine ICU practice.

methodsThis retrospective multicenter study was conducted across ten tertiary-care ICUs in the Greater Paris area between January 2018 and April 2024. All patients for whom an mNGS analysis was requested by clinicians from a microbiological sample were included. Any additional pathogens identified by mNGS were independently classified as causative, possibly causative, or non-causative by two reviewers. The independent reviewers also categorised therapeutic changes attributable to mNGS as escalation, de-escalation, discontinuation, or other decision support. Discrepancies were adjudicated by a third reviewer.

resultsA total of 144 mNGS analyses were performed in 132 critically ill patients (median age 55 years), 31% of whom were immunocompromised. The number of mNGS analyses requested increased each year. The most common sample types were cerebrospinal fluid (CSF) (n = 60/144, 41.7%) and pleural fluid (n = 21/144, 14.6%). Pathogens were identified by mNGS in 58 samples (40.3%), with a higher yield in pleural fluid (n = 11/21, 52.4%) than in CSF (n = 16/60, 26.6%). Of the 107 pathogens identified, 43 (40.2%) were detected exclusively by mNGS, notably anaerobic bacteria in pleural fluid and abscess samples. mNGS identified an additional pathogen in 34 cases (25.8%) of the 132 patients included, which was deemed causative in 18 cases (13.6%). mNGS findings influenced therapeutic management in seven patients (5.3%) including five cases of antibiotic de-escalation, one appropriate antibiotic escalation, and one case of clinical decision support.

conclusionIn this real-life ICU cohort, mNGS identified additional pathogens in 25.8% of patients, deemed causative in 18 cases (13.6%), and a direct therapeutic impact was observed in 5.3% of cases. However, the median turnaround time of 14 days likely limited its clinical impact. Further studies are needed to better define the role of mNGS in the diagnostic management of critically ill patients.

Indexed as

MetagenomicsAgedCritical IllnessFemaleHumansIntensive Care UnitsMaleMiddle AgedParisRetrospective StudiesClinical metagenomicsInfectionsIntensive care unitMetagenomics

Identifiers

PMID41508107
PMCPMC12784588

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.