Lung Ultrasound for Diagnosis of Acute Respiratory Failure in Critically Ill Patients: A Systematic Review and Meta‐Analysis
Year: 2026
Type: review
Study designs: Systematic Review, Meta-Analysis
Abstract: ABSTRACT Background Lung ultrasound is increasingly used as a bedside imaging modality in critically ill patients with acute respiratory failure (ARF), but its overall diagnostic performance across diverse settings remains uncertain. We conducted a diagnostic test accuracy meta‐analysis to evaluate the accuracy of lung ultrasound in identifying the thoracic cause of ARF in critically ill patients. Methods We systematically searched MEDLINE, Embase, Web of Science, Scopus from inception to November 2025. Two reviewers independently performed study selection, data extraction, and QUADAS‐2 quality assessment. Pooled sensitivity, specificity, and summary receiver operating characteristic (SROC) curves were estimated using a bivariate random‐effects model; likelihood ratios, diagnostic odds ratio (DOR), Fagan nomogram, and Deek's funnel plot were derived. Results Twenty studies (3083 units; 1227 reference‐positive, 1856 reference‐negative) were included. Pooled sensitivity and specificity of lung ultrasound for thoracic cause of ARF were 0.89 (95% CI: 0.83–0.94) and 0.94 (95% CI: 0.90–0.97), respectively. Positive and negative likelihood ratios were 16.2 (95% CI: 9.1–28.9) and 0.11 (95% CI: 0.07–0.18), yielding DOR of 144 (95% CI: 63–330) and area under the SROC curve of 0.97 (95% CI: 0.95–0.98). Heterogeneity was substantial ( I 2 = 96%), but Deek's test did not show strong evidence of publication bias ( p = 0.09). Conclusions Lung ultrasound can be applied as rapid bedside adjunct to support early ARDS evaluation in ARF, potentially reducing delays associated with transport‐dependent imaging. However, clinicians should interpret findings in context, as diagnostic performance may be attenuated when acoustic windows are limited (e.g., obesity) and when mechanical ventilation settings (e.g., high PEEP) alter lung aeration and artifact patterns.
Source: Journal of Clinical Ultrasound
DOI: 10.1002/jcu.70205
Authors: Zhou Jianguo, Liu Ligen
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W7128808203