Evidence map›Paper›PMID 37745948›Full record

ArticleTransplantation direct2023

Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose.

Nynke Wijbenga, Nadine L A de Jong, Rogier A S Hoek, Bas J Mathot, Leonard Seghers, Joachim G J V Aerts, Daniel Bos, Olivier C Manintveld, Merel E Hellemons

Abstract read
In one paragraph

Article in Transplantation direct, 2023. 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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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

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

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

9 authors.

Nynke WijbengaDepartment of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0003-1681-3324
Nadine L A de JongDepartment of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0002-3477-2901
Rogier A S HoekDepartment of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0003-2165-3222
Bas J MathotDepartment of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0002-4196-6421
Leonard SeghersDepartment of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0003-1054-5728
Joachim G J V AertsDepartment of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0001-6662-2951
Daniel BosDepartment of Radiology and Nuclear Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0001-8979-2603
Olivier C ManintveldErasmus Medical Center Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0002-2202-7314
Merel E HellemonsDepartment of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.ORCID https://orcid.org/0000-0002-6666-5027

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Bacterial colonization (BC) of the lower airways is common in lung transplant recipients (LTRs) and increases the risk of chronic lung allograft dysfunction. Diagnosis often requires bronchoscopy. Exhaled breath analysis using electronic nose (eNose) technology may noninvasively detect BC in LTRs. Therefore, we aimed to assess the diagnostic accuracy of an eNose to detect BC in LTRs. Methods: We performed a cross-sectional analysis within a prospective, single-center cohort study assessing the diagnostic accuracy of detecting BC using eNose technology in LTRs. In the outpatient clinic, consecutive LTR eNose measurements were collected. We assessed and classified the eNose measurements for the presence of BC. Using supervised machine learning, the diagnostic accuracy of eNose for BC was assessed in a random training and validation set. Model performance was evaluated using receiver operating characteristic analysis. Results: In total, 161 LTRs were included with 80 exclusions because of various reasons. Of the remaining 81 patients, 16 (20%) were classified as BC and 65 (80%) as non-BC. eNose-based classification of patients with and without BC provided an area under the curve of 0.82 in the training set and 0.97 in the validation set. Conclusions: Exhaled breath analysis using eNose technology has the potential to noninvasively detect BC.

Identifiers

PMID37745948
PMCPMC10513211

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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.