Evidence map›Paper›PMID 31870355›Full record

ArticleBMC pulmonary medicine2019

Dynamics of microbiota during mechanical ventilation in aspiration pneumonia.

Ken Otsuji, Kazumasa Fukuda, Midori Ogawa, Yoshihisa Fujino, Masayuki Kamochi, Mitsumasa Saito

Open access · goldAbstract read
In one paragraph

Article in BMC pulmonary medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
2.4field-weighted citation impact, top 11% 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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Factors associated withJournal of thoracic disease · 2026
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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

6 authors at 1 institution in 1 country.

Ken OtsujiDepartment of Microbiology, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan. otsujiken@clnc.uoeh-u.ac.jp.ORCID http://orcid.org/0000-0003-4035-4869
Kazumasa FukudaDepartment of Microbiology, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan.
Midori OgawaDepartment of Microbiology, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan.
Yoshihisa FujinoDepartment of Environmental Epidemiology, Institute of Industrial Ecological Sciences, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan.
Masayuki KamochiDepartment of Intensive Care Medicine, Hospital of the University of Occupational and Environmental Health, Japan, Kitakyushu, Japan.
Mitsumasa SaitoDepartment of Microbiology, School of Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan.
University of Occupational and Environmental Health Japan · JP

Funding

Japan Society for the Promotion of Science 17K17078
6 · The paper itself

Abstract

backgroundThe emergence of multi-drug resistant pathogens is an urgent health-related problem, and the appropriate use of antibiotics is imperative. It is often difficult to identify the causative bacteria in patients with aspiration pneumonia because tracheal aspirate contains contaminants of oral bacteria. We investigated the dynamics of microbiota in mechanically ventilated patients with aspiration pneumonia to develop a treatment strategy.

methodsTwenty-two intubated patients with aspiration pneumonia were recruited. Saliva and tracheal aspirate of the subjects were collected at three time points: (A) within 2 h after intubation, (B) just before administration of antibiotics, and (C) 48-72 h after administration of antibiotics. The microbiota in each specimen was analyzed by using the 16S rRNA gene clone library sequencing method. Bacterial floras of the samples were analyzed by principal component analysis.

resultsPrincipal component analysis based on the composition of genus revealed that although the changes of microbiota in the saliva from (A) to (B) were not clear, the composition of anaerobes in the tracheal aspirate (B) was lower than (A). In fact, the reduction of anaerobes, not in the saliva but in the tracheal aspirate from (A) to (B), was confirmed by incident rate ratios estimated by a multilevel Poisson regression model (p < 0.001). The extent of decrease in anaerobes was fully dependent on the time difference between the sampling of tracheal aspirate (A) and (B)-in particular, over 3 h of mechanical ventilation. This indicates that the alterations of microbiota (involving the reduction of anaerobes in the lower respiratory tract) occurred during mechanical ventilation prior to the administration of antibiotics. After the administration of antibiotics, Enterobacter spp., Corynebacterium spp., Pseudomonas aeruginosa, Klebsiella pneumoniae, Staphylococcus aureus, and Granulicatera adiacens were predominantly detected in the tracheal aspirate (C).

conclusionThe microbiota of the lower respiratory tract changes dynamically during mechanical ventilation and during the administration of antibiotics in intubated patients with aspiration pneumonia. Antibiotics should be selected on the premise that dynamic changes in microbiota (involved in the reduction of anaerobes) may occur during the mechanical ventilation in these patients.

Indexed as

Respiration, ArtificialAnti-Bacterial AgentsCarnobacteriaceaeCorynebacteriumEnterobacterFemaleHumansKlebsiella pneumoniaeMaleMicrobiotaMouthPneumonia, AspirationPrincipal Component AnalysisPseudomonas aeruginosaRNA, Ribosomal, 16SSalivaAnti-Bacterial AgentsRNA, Ribosomal, 16SAnaerobesAspiration pneumoniaDynamicsMechanical ventilationMicrobiota

Identifiers

PMID31870355
PMCPMC6929358
OpenAlexW2994978742

What OpenQuestion holds

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LicenceCC BY
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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.