Lung ultrasound volume sweep imaging for respiratory illness: a new horizon in expanding imaging access.


Journal

BMJ open respiratory research
ISSN: 2052-4439
Titre abrégé: BMJ Open Respir Res
Pays: England
ID NLM: 101638061

Informations de publication

Date de publication:
11 2021
Historique:
received: 16 03 2021
accepted: 19 10 2021
entrez: 13 11 2021
pubmed: 14 11 2021
medline: 31 12 2021
Statut: ppublish

Résumé

Respiratory illness is a leading cause of morbidity in adults and the number one cause of mortality in children, yet billions of people lack access to medical imaging to assist in its diagnosis. Although ultrasound is highly sensitive and specific for respiratory illness such as pneumonia, its deployment is limited by a lack of sonographers. As a solution, we tested a standardised lung ultrasound volume sweep imaging (VSI) protocol based solely on external body landmarks performed by individuals without prior ultrasound experience after brief training. Each step in the VSI protocol is saved as a video clip for later interpretation by a specialist. Dyspneic hospitalised patients were scanned by ultrasound naive operators after 2 hours of training using the lung ultrasound VSI protocol. Separate blinded readers interpreted both lung ultrasound VSI examinations and standard of care chest radiographs to ascertain the diagnostic value of lung VSI considering chest X-ray as the reference standard. Comparison to clinical diagnosis as documented in the medical record and CT (when available) were also performed. Readers offered a final interpretation of normal, abnormal, or indeterminate/borderline for each VSI examination, chest X-ray, and CT. Operators scanned 102 subjects (0-89 years old) for analysis. Lung VSI showed a sensitivity of 93% and a specificity of 91% for an abnormal chest X-ray and a sensitivity of 100% and a specificity of 93% for a clinical diagnosis of pneumonia. When any cases with an indeterminate rating on chest X-ray or ultrasound were excluded (n=38), VSI lung ultrasound showed 92% agreement with chest X-ray (Cohen's κ 0.83 (0.68 to 0.97, p<0.0001)). Among cases with CT (n=21), when any ultrasound with an indeterminate rating was excluded (n=3), there was 100% agreement with VSI. Lung VSI performed by previously inexperienced ultrasound operators after brief training showed excellent agreement with chest X-ray and high sensitivity and specificity for a clinical diagnosis of pneumonia. Blinded readers were able to identify other respiratory diseases including pulmonary oedema and pleural effusion. Deployment of lung VSI could benefit the health of the global community.

Sections du résumé

BACKGROUND
Respiratory illness is a leading cause of morbidity in adults and the number one cause of mortality in children, yet billions of people lack access to medical imaging to assist in its diagnosis. Although ultrasound is highly sensitive and specific for respiratory illness such as pneumonia, its deployment is limited by a lack of sonographers. As a solution, we tested a standardised lung ultrasound volume sweep imaging (VSI) protocol based solely on external body landmarks performed by individuals without prior ultrasound experience after brief training. Each step in the VSI protocol is saved as a video clip for later interpretation by a specialist.
METHODS
Dyspneic hospitalised patients were scanned by ultrasound naive operators after 2 hours of training using the lung ultrasound VSI protocol. Separate blinded readers interpreted both lung ultrasound VSI examinations and standard of care chest radiographs to ascertain the diagnostic value of lung VSI considering chest X-ray as the reference standard. Comparison to clinical diagnosis as documented in the medical record and CT (when available) were also performed. Readers offered a final interpretation of normal, abnormal, or indeterminate/borderline for each VSI examination, chest X-ray, and CT.
RESULTS
Operators scanned 102 subjects (0-89 years old) for analysis. Lung VSI showed a sensitivity of 93% and a specificity of 91% for an abnormal chest X-ray and a sensitivity of 100% and a specificity of 93% for a clinical diagnosis of pneumonia. When any cases with an indeterminate rating on chest X-ray or ultrasound were excluded (n=38), VSI lung ultrasound showed 92% agreement with chest X-ray (Cohen's κ 0.83 (0.68 to 0.97, p<0.0001)). Among cases with CT (n=21), when any ultrasound with an indeterminate rating was excluded (n=3), there was 100% agreement with VSI.
CONCLUSION
Lung VSI performed by previously inexperienced ultrasound operators after brief training showed excellent agreement with chest X-ray and high sensitivity and specificity for a clinical diagnosis of pneumonia. Blinded readers were able to identify other respiratory diseases including pulmonary oedema and pleural effusion. Deployment of lung VSI could benefit the health of the global community.

Identifiants

pubmed: 34772730
pii: 8/1/e000919
doi: 10.1136/bmjresp-2021-000919
pmc: PMC8593737
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: Benjamin Castaneda has financial stake in the company Medical Innovation and Technology which seeks to bring ultrasound to rural communities. The other authors declare no conflict of interest.

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Auteurs

Thomas J Marini (TJ)

Department of Imaging Sciences, URMC, Rochester, NY, USA rochesterradiology2021@gmail.com.

Justin M Weis (JM)

Department of Medicine, URMC, Rochester, NY, USA.

Timothy M Baran (TM)

Department of Imaging Sciences, URMC, Rochester, NY, USA.

Jonah Kan (J)

University of Rochester School of Medicine and Dentistry, URMC, Rochester, NY, USA.

Steven Meng (S)

University of Rochester School of Medicine and Dentistry, URMC, Rochester, NY, USA.

Alex Yeo (A)

Department of Medicine, Boston University Medical Center, Boston, MA, USA.

Yu T Zhao (YT)

Department of Imaging Sciences, URMC, Rochester, NY, USA.

Robert Ambrosini (R)

Department of Imaging Sciences, URMC, Rochester, NY, USA.

Sean Cleary (S)

Department of Imaging Sciences, URMC, Rochester, NY, USA.

Deborah Rubens (D)

Department of Imaging Sciences, URMC, Rochester, NY, USA.

Mitchell Chess (M)

Department of Imaging Sciences, URMC, Rochester, NY, USA.

Benjamin Castaneda (B)

Departmento de Ingeniería, Pontificia Universidad Católica del Perú, Lima, Peru.

Ann Dozier (A)

Department of Public Health Sciences, URMC, Rochester, NY, USA.

Timothy O'Connor (T)

Department of Emergency Medicine, URMC, Rochester, NY, USA.

Brian Garra (B)

Medical Imaging Ministries of the Americas, Clermont, FL, USA.

Katherine Kaproth-Joslin (K)

Department of Imaging Sciences, URMC, Rochester, NY, USA.

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Classifications MeSH