Evidence map›Paper›PMID 41113571›Full record

SynthesisFrontiers in pediatrics2025

The predictive value of newborn and infant lung ultrasound score for mechanical ventilation needs: a systematic review and meta-analysis.

Taomei He, Li Ma, Fei Chen

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Taomei HeDepartment of Ultrasound Medicine, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
Li MaDepartment of Ultrasound Medicine, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
Fei ChenDepartment of Ultrasound Medicine, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the predictive value of neonatal and infant lung ultrasound scores for the need for mechanical ventilation through meta-analysis. Methods: Literature up to October 1, 2024, on neonatal and infant lung ultrasound scores and mechanical ventilation was searched in PubMed, Web of Science, Embase, and The Cochrane Library databases. The diagnostic accuracy of the included studies was evaluated using the Quality Assessment tool for Diagnostic Accuracy Studies. Revman5.4 and StataSE-64 software were employed to calculate the pooled sensitivity, specificity and AUC value of neonatal and infant lung ultrasound scores for predicting the need of mechanical ventilation. Results: The meta-analysis comprised 9 studies (7 prospective studies and 2 retrospective), including a total of 1,746 patients. The LUS score predicted the need for mechanical ventilation. Overall sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio were 74% (95% CI: 66%-81%), 81% (95% CI: 71%-87%), 3.8 (2.4-6.1), 0.32 (0.23-0.46) and 12 (5-6), respectively. The forest plots indicated significant heterogeneity for sensitivity ( Conclusion: Meta-analysis of multivariate categorical variables indicated that the higher the LUS scores, the greater the risk of mechanical ventilation. The combined results of meta-analysis of diagnostic data suggest that LUS score has high accuracy in predicting the need for mechanical ventilation. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, PROSPERO CRD420251029542.

Indexed as

infantmechanical ventilationmeta-analysispulmonaryultrasound score

Identifiers

PMID41113571
PMCPMC12527875

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.