Medical ultrasonography · 2026
Diagnostic accuracy of lung ultrasound for the identification of acute respiratory distress syndrome: A systematic review and meta-analysis.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод пока не подготовлен
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Ji Chunyan, Zhou Ling, Jiang Mingzhu, He Lilan, Zhou Min
Аннотация
AIM: This review was done to evaluate diagnostic accuracy of LUS against established ARDS reference standards in adult populations.
MATERIAL AND METHODS: Four electronic databases were searched through June 2025 without language restrictions. Two reviewers independently screened studies, extracted data, and assessed quality using QUADAS‑2. Bivariate random‑effects models generated pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratios (DORs), and hierarchical summary receiver‑operating characteristic (HSROC) curves. Subgroup analyses explored pattern‑ versus score‑based protocols and prospective study designs. Publication bias was assessed via Deeks' funnel‑plot test.
RESULTS: Fourteen studies (531 ARDS-positive; 1354 ARDS-negative; pre‑test probability=28%) met inclusion criteria. Overall pooled sensitivity was 0.84 (95%CI: 0.69-0.92) and specificity 0.94 (95%CI: 0.83-0.98), with an AUROC of 0.95 (95%CI: 0.93-0.96). The positive likelihood ratio was 13.3 (95%CI: 5.0-35.9) and negative likelihood ratio 0.17 (95%CI: 0.09-0.34), corresponding to a DOR of 77 (95%CI: 26-227). Pattern‑based protocols achieved sensitivity 0.82 and specificity 0.96 (AUROC=0.96), while score‑based approaches yielded sensitivity 0.90 and specificity 0.83 (AUROC=0.93). Deeks' test indicated potential publication bias (p=0.004).
CONCLUSIONS: LUS demonstrates excellent rule‑in and rule‑out performance for ARDS in critically ill adults, rivalling CT accuracy without its drawbacks. Adoption of standardized LUS protocols and integration into ARDS diagnostic pathways could enhance early detection, optimize management, and reduce reliance on ionizing imaging.
MATERIAL AND METHODS: Four electronic databases were searched through June 2025 without language restrictions. Two reviewers independently screened studies, extracted data, and assessed quality using QUADAS‑2. Bivariate random‑effects models generated pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratios (DORs), and hierarchical summary receiver‑operating characteristic (HSROC) curves. Subgroup analyses explored pattern‑ versus score‑based protocols and prospective study designs. Publication bias was assessed via Deeks' funnel‑plot test.
RESULTS: Fourteen studies (531 ARDS-positive; 1354 ARDS-negative; pre‑test probability=28%) met inclusion criteria. Overall pooled sensitivity was 0.84 (95%CI: 0.69-0.92) and specificity 0.94 (95%CI: 0.83-0.98), with an AUROC of 0.95 (95%CI: 0.93-0.96). The positive likelihood ratio was 13.3 (95%CI: 5.0-35.9) and negative likelihood ratio 0.17 (95%CI: 0.09-0.34), corresponding to a DOR of 77 (95%CI: 26-227). Pattern‑based protocols achieved sensitivity 0.82 and specificity 0.96 (AUROC=0.96), while score‑based approaches yielded sensitivity 0.90 and specificity 0.83 (AUROC=0.93). Deeks' test indicated potential publication bias (p=0.004).
CONCLUSIONS: LUS demonstrates excellent rule‑in and rule‑out performance for ARDS in critically ill adults, rivalling CT accuracy without its drawbacks. Adoption of standardized LUS protocols and integration into ARDS diagnostic pathways could enhance early detection, optimize management, and reduce reliance on ionizing imaging.