Evidence map›Paper›PMID 38261172›Full record

ArticleWorld journal of pediatrics : WJP2024

Fetal lung growth predicts the risk for early-life respiratory infections and childhood asthma.

Dimitra E Zazara, Olympia Giannou, Steven Schepanski, Mirja Pagenkemper, Anastasios D Giannou, Maike Pincus, Ioannis Belios, Stefan Bonn, Ania C Muntau, Kurt Hecher and 2 more

Abstract read
In one paragraph

Article in World journal of pediatrics : WJP, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Maternal BMI, early-life growth, and atopic dermatitis by age 3 years.The journal of allergy and clinical immunology. Global · 2026
    Article
  2. Article
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

12 authors.

Dimitra E ZazaraDivision for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, 20251, Hamburg, Germany.
Olympia GiannouComputer Engineering and Informatics Department, Polytechnic School, University of Patras, Patras, Greece.
Steven SchepanskiDivision for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, 20251, Hamburg, Germany.
Mirja PagenkemperDepartment of Obstetrics and Fetal Medicine, UKE, Hamburg, Germany.
Anastasios D GiannouDepartment of General, Visceral and Thoracic Surgery, UKE, Hamburg, Germany.
Maike PincusPediatrics and Pediatric Pneumology Practice, Berlin, Germany.
Ioannis BeliosDivision for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, 20251, Hamburg, Germany.
Stefan BonnInstitute of Medical Systems Biology, ZMNH, UKE, Hamburg, Germany.
Ania C MuntauUniversity Children's Hospital, UKE, Hamburg, Germany.
Kurt HecherDepartment of Obstetrics and Fetal Medicine, UKE, Hamburg, Germany.
Anke DiemertDepartment of Obstetrics and Fetal Medicine, UKE, Hamburg, Germany.
Petra Clara ArckDivision for Experimental Feto-Maternal Medicine, Department of Obstetrics and Fetal Medicine, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, 20251, Hamburg, Germany. p.arck@uke.de.ORCID 0000-0002-2932-926X

Funding

Deutsche Forschungsgemeinschaft AR232
6 · The paper itself

Abstract

backgroundEarly-life respiratory infections and asthma are major health burdens during childhood. Markers predicting an increased risk for early-life respiratory diseases are sparse. Here, we identified the predictive value of ultrasound-monitored fetal lung growth for the risk of early-life respiratory infections and asthma.

methodsFetal lung size was serially assessed at standardized time points by transabdominal ultrasound in pregnant women participating in a pregnancy cohort. Correlations between fetal lung growth and respiratory infections in infancy or early-onset asthma at five years were examined. Machine-learning models relying on extreme gradient boosting regressor or classifier algorithms were developed to predict respiratory infection or asthma risk based on fetal lung growth. For model development and validation, study participants were randomly divided into a training and a testing group, respectively, by the employed algorithm.

resultsEnhanced fetal lung growth throughout pregnancy predicted a lower early-life respiratory infection risk. Male sex was associated with a higher risk for respiratory infections in infancy. Fetal lung growth could also predict the risk of asthma at five years of age. We designed three machine-learning models to predict the risk and number of infections in infancy as well as the risk of early-onset asthma. The models' R

conclusionSonographic monitoring of fetal lung growth allows to predict the risk for early-life respiratory infections and asthma.

Indexed as

AsthmaFetal DevelopmentLungRespiratory Tract InfectionsUltrasonography, PrenatalAdultChild, PreschoolCohort StudiesFemaleHumansInfantInfant, NewbornMachine LearningMalePredictive Value of TestsPregnancyAsthmaChild riskEarly-life respiratory infectionsPrenatal lung developmentSexual dimorphism

Identifiers

PMID38261172
PMCPMC11136800

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.