Lung Ultrasound Prediction Model for Acute Respiratory Distress Syndrome: A Multicenter Prospective Observational Study
Issued Date
2023-06-15
Resource Type
eISSN
15354970
Scopus ID
2-s2.0-85152548931
Pubmed ID
36790377
Journal Title
American journal of respiratory and critical care medicine
Volume
207
Issue
12
Start Page
1591
End Page
1601
Rights Holder(s)
SCOPUS
Bibliographic Citation
American journal of respiratory and critical care medicine Vol.207 No.12 (2023) , 1591-1601
Suggested Citation
Smit M.R., Hagens L.A., Heijnen N.F.L., Pisani L., Cherpanath T.G.V., Dongelmans D.A., de Grooth H.J.S., Pierrakos C., Tuinman P.R., Zimatore C., Paulus F., Schnabel R.M., Schultz M.J., Bergmans D.C.J.J., Bos L.D.J. Lung Ultrasound Prediction Model for Acute Respiratory Distress Syndrome: A Multicenter Prospective Observational Study. American journal of respiratory and critical care medicine Vol.207 No.12 (2023) , 1591-1601. 1601. doi:10.1164/rccm.202210-1882OC Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/88022
Title
Lung Ultrasound Prediction Model for Acute Respiratory Distress Syndrome: A Multicenter Prospective Observational Study
Author's Affiliation
Mahidol Oxford Tropical Medicine Research Unit
Centre Hospitalier Universitaire Brugmann, Brussels
Università degli studi di Bari Aldo Moro
Universiteit Maastricht
Nuffield Department of Medicine
Vrije Universiteit Amsterdam
Maastricht Universitair Medisch Centrum+
Amsterdam UMC - University of Amsterdam
Miulli Regional Hospital
Centre Hospitalier Universitaire Brugmann, Brussels
Università degli studi di Bari Aldo Moro
Universiteit Maastricht
Nuffield Department of Medicine
Vrije Universiteit Amsterdam
Maastricht Universitair Medisch Centrum+
Amsterdam UMC - University of Amsterdam
Miulli Regional Hospital
Other Contributor(s)
Abstract
Rationale: Lung ultrasound (LUS) is a promising tool for diagnosis of acute respiratory distress syndrome (ARDS), but adequately sized studies with external validation are lacking. Objectives: To develop and validate a data-driven LUS score for diagnosis of ARDS and compare its performance with that of chest radiography (CXR). Methods: This multicenter prospective observational study included invasively ventilated ICU patients who were divided into a derivation cohort and a validation cohort. Three raters scored ARDS according to the Berlin criteria, resulting in a classification of "certain no ARDS," or "certain ARDS" when experts agreed or "uncertain ARDS" when evaluations conflicted. Uncertain cases were classified in a consensus meeting. Results of a 12-region LUS exam were used in a logistic regression model to develop the LUS-ARDS score. Measurements and Main Results: Three hundred twenty-four (16% certain ARDS) and 129 (34% certain ARDS) patients were included in the derivation cohort and the validation cohort, respectively. With an ARDS diagnosis by the expert panel as the reference test, the LUS-ARDS score, including the left and right LUS aeration scores and anterolateral pleural line abnormalities, had an area under the receiver operating characteristic (ROC) curve of 0.90 (95% confidence interval [CI], 0.85-0.95) in certain patients of the derivation cohort and 0.80 (95% CI, 0.72-0.87) in all patients of the validation cohort. Within patients who had imaging-gold standard chest computed tomography available, diagnostic accuracy of eight independent CXR readers followed the ROC curve of the LUS-ARDS score. Conclusions: The LUS-ARDS score can be used to accurately diagnose ARDS also after external validation. The LUS-ARDS score may be a useful adjunct to a diagnosis of ARDS after further validation, as it showed performance comparable with that of the current practice with experienced CXR readers but more objectifiable diagnostic accuracy at each cutoff.