ReviewDiagnostics (Basel, Switzerland)2024
Lung Ultrasound in Neonatal Respiratory Distress Syndrome: A Narrative Review of the Last 10 Years.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Lung ultrasound aeration score as a predictor of extubation failure in neonates: an updated systematic review with meta-analysis and meta-regression.European journal of pediatrics · 2026Pooled it
- Article
- Lung Ultrasound in Pediatrics: A Review with Core Principles That Every User Should Know.Diagnostics (Basel, Switzerland) · 2025Review
- Clinical perspective on targeted alpha therapy usingAnnals of nuclear medicine · 2025Review
- ExploringDiagnostics (Basel, Switzerland) · 2025Article
- Can Lung Ultrasound Act as a Diagnosis and Monitoring Tool in Children with Community Acquired Pneumonia? Correlation with Risk Factors, Clinical Indicators and Biologic Results.Journal of clinical medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neonatal respiratory distress syndrome (RDS) is a common and potentially life-threatening condition in preterm infants, primarily due to surfactant deficiency. Early and accurate diagnosis is critical to guide timely interventions such as surfactant administration and respiratory support. Traditionally, chest X-rays have been used for diagnosis, but lung ultrasound (LUS) has gained prominence due to its non-invasive, radiation-free, and bedside applicability. Compared to chest X-rays and CT scans, LUS demonstrates superior sensitivity and specificity in diagnosing RDS, particularly in identifying surfactant need and predicting CPAP failure. Additionally, LUS offers real-time imaging without radiation exposure, an advantage over other modalities. However, its broader adoption is limited by challenges in standardizing training, ensuring diagnostic reproducibility, and validating scoring systems, especially in resource-limited settings. This narrative review aims to evaluate the role of LUS in the diagnosis and management of neonatal RDS over the past decade, focusing on its clinical utility, scoring systems, and emerging applications. We reviewed the literature from 2013 to 2023, focusing on studies evaluating LUS' diagnostic accuracy, scoring systems, and its potential role in guiding surfactant therapy and predicting CPAP failure. Despite its benefits, addressing the variability in operator expertise and integrating artificial intelligence to enhance usability are crucial for ensuring LUS' efficacy across diverse clinical environments. Future research should prioritize standardizing training and scoring protocols to facilitate wider implementation and optimize neonatal respiratory care outcomes.
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Registered trials
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.