Evidence map›Paper›PMID 41635718›Full record

Observational studyFrontiers in cellular and infection microbiology2025

Characteristics of pathogenic microorganisms in COPD-related infections: prognostic correlations and implications.

Chaoying Liu, Caihong Liu, Huibo Liu, Shan Lin

Abstract readObservational Study
In one paragraph

Observational study in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Chaoying LiuDepartment of Respiratory Medicine, The First Hospital of Jilin University, Changchun, China.
Caihong LiuDepartment of Laboratory Medicine, The First Hospital of Jilin University, Changchun, China.
Huibo LiuDepartment of Dermatology, The First Hospital of Jilin University, Changchun, China.
Shan LinDepartment of Respiratory Medicine, The First Hospital of Jilin University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Chronic obstructive pulmonary disease (COPD) significantly impacts global health, primarily due to frequent acute exacerbations caused by respiratory infections. Precise microbial characterization may inform prognostic insights and optimize clinical management. Methods: We conducted a prospective observational study from December 2023 to February 2025 involving 1146 patients (259 COPD; 887 non-COPD) with suspected respiratory infections. Bronchoalveolar lavage fluid samples underwent next-generation sequencing (NGS) and conventional microbiological testing. Multivariate logistic regression identified COPD predictors, and machine learning modeled prognostic outcomes based on microbial profiles. Results: Distinct pathogen distributions emerged between COPD and non-COPD groups, with COPD patients exhibiting higher prevalence of gram-negative bacteria, particularly Conclusions: Comprehensive microbial profiling using NGS effectively distinguishes pathogen differences between COPD and non-COPD patients, revealing key associations with clinical prognosis. These insights can inform tailored clinical interventions aimed at mitigating COPD exacerbations and improving patient outcomes.

Indexed as

BacteriaPulmonary Disease, Chronic ObstructiveRespiratory Tract InfectionsAgedBronchoalveolar Lavage FluidFemaleFungiHigh-Throughput Nucleotide SequencingHumansMachine LearningPrognosisProspective StudiesCOPDinfectionmachine learningnext-generation sequencingpathogenic microorganismprognosis

Identifiers

PMID41635718
PMCPMC12862076

What OpenQuestion holds

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Registered trials

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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.