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Erschienen in: Journal of Clinical Monitoring and Computing 2/2020

14.05.2019 | Original Research

Optimizing B-lines on lung ultrasound: an in-vitro to in-vivo pilot study with clinical implications

verfasst von: Christopher N. Schmickl, Aravind Ajakumar Menon, Rajanigandha Dhokarh, Bhavna Seth, Frank Schembri

Erschienen in: Journal of Clinical Monitoring and Computing | Ausgabe 2/2020

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Abstract

B-lines on lung ultrasound (US) are the hallmark of pulmonary edema. It is unknown if ultrasound machine settings or probe type matter. We created an in-vitro gelatin model. Using lung presets as baseline, five blinded investigators assessed the impact of 32 distinct settings on B-line visibility based on a Likert-Scale (LS) from 0 to10 (< 5 worse, > 5 better) separately for two probes. The experiment was then repeated in-vivo in a patient with known pulmonary edema. Based on a multivariable regression LS-ratings were similar when comparing the in-vitro versus in-vivo experiment (P = 0.16; partial R2 = 0.2%) and when using the curvilinear versus linear probe (P = 0.69; partial R2 = 0.02%) but significantly different across machine settings (P < 0.0001; partial R2 = 34.4%). Limited by its pilot character, our study suggests that (1) certain US-machine settings heavily impact B-line visibility, with no clear difference between probes; (2) in-vitro models are a valid and practical alternative to more challenging patient-based research; (3) there is significant potential to improve B-line visibility and thus diagnostic yield in the clinical setting by using lung presets, centering the focal zone at the pleural line and increasing the distal time gain compensation, most of which are (in our experience) rarely done.
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Metadaten
Titel
Optimizing B-lines on lung ultrasound: an in-vitro to in-vivo pilot study with clinical implications
verfasst von
Christopher N. Schmickl
Aravind Ajakumar Menon
Rajanigandha Dhokarh
Bhavna Seth
Frank Schembri
Publikationsdatum
14.05.2019
Verlag
Springer Netherlands
Erschienen in
Journal of Clinical Monitoring and Computing / Ausgabe 2/2020
Print ISSN: 1387-1307
Elektronische ISSN: 1573-2614
DOI
https://doi.org/10.1007/s10877-019-00321-z

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