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Record W4284888786 · doi:10.1186/s13054-022-04068-z

Composition and diversity analysis of the lung microbiome in patients with suspected ventilator-associated pneumonia

2022· article· en· W4284888786 on OpenAlexaff
Dominic Fenn, Mahmoud I. Abdel‐Aziz, Pouline M. P. van Oort, Paul Brinkman, Waqar Ahmed, Timothy Felton, Antonio Artigas, Pedro Póvoa, Ignacio Martín‐Loeches, Marcus J. Schultz, Paul Dark, Stephen J. Fowler, Lieuwe D. J. Bos, Antonio Artigas Raventós, Jonathan Bannard‐Smith, Marta Camprubi, Luís Coelho, Alan Davie, Emili Dı́az, Gemma Gomà, Royston Goodacre, Craig F. Johnson, Hugo Knobel, Oluwasola Lawal, Jan‐Hendrik Leopold, Tamara M. E. Nijsen, Nicholas J. W. Rattray, Guus Rijnders, Ruud Steenwelle, Peter J. Sterk, Jordi Vallés, Fred Verhoeckx, Anton Vink, Hans Weda, Iain R. White, Tineke Winters, Tetyana Zakharkina

Bibliographic record

VenueCritical Care · 2022
Typearticle
Languageen
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsInstitute of Infection and Immunity
FundersFP7 People: Marie-Curie ActionsEuropean CommissionNational Institute for Health and Care Research
KeywordsMicrobiomeBronchoalveolar lavageMedicineVentilator-associated pneumoniaPneumoniaMicrobiological cultureDysbiosisInternal medicineImmunologyAmplicon sequencingLung16S ribosomal RNABioinformaticsBacteriaBiologyDisease

Abstract

fetched live from OpenAlex

Abstract Background Ventilator-associated pneumonia (VAP) is associated with high morbidity and health care costs, yet diagnosis remains a challenge. Analysis of airway microbiota by amplicon sequencing provides a possible solution, as pneumonia is characterised by a disruption of the microbiome. However, studies evaluating the diagnostic capabilities of microbiome analysis are limited, with a lack of alignment on possible biomarkers. Using bronchoalveolar lavage fluid (BALF) from ventilated adult patients suspected of VAP, we aimed to explore how key characteristics of the microbiome differ between patients with positive and negative BALF cultures and whether any differences could have a clinically relevant role. Methods BALF from patients suspected of VAP was analysed using 16s rRNA sequencing in order to: (1) differentiate between patients with and without a positive culture; (2) determine if there was any association between microbiome diversity and local inflammatory response; and (3) correctly identify pathogens detected by conventional culture. Results Thirty-seven of 90 ICU patients with suspected VAP had positive cultures. Patients with a positive culture had significant microbiome dysbiosis with reduced alpha diversity. However, gross compositional variance was not strongly associated with culture positivity (AUROCC range 0.66–0.71). Patients with a positive culture had a significantly higher relative abundance of pathogenic bacteria compared to those without [0.45 (IQR 0.10–0.84), 0.02 (IQR 0.004–0.09), respectively], and an increased interleukin (IL)-1β was associated with reduced species evenness ( r s = − 0.33, p < 0.01) and increased pathogenic bacteria presence ( r s = 0.28, p = 0.013). Untargeted 16s rRNA pathogen detection was limited by false positives, while the use of pathogen-specific relative abundance thresholds showed better diagnostic accuracy (AUROCC range 0.89–0.998). Conclusion Patients with positive BALF culture had increased dysbiosis and genus dominance. An increased caspase-1-dependent IL-1b expression was associated with a reduced species evenness and increased pathogenic bacterial presence, providing a possible causal link between microbiome dysbiosis and lung injury development in VAP. However, measures of diversity were an unreliable predictor of culture positivity and 16s sequencing used agnostically could not usefully identify pathogens; this could be overcome if pathogen-specific relative abundance thresholds are used.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.250
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations52
Published2022
Admission routes1
Has abstractyes

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