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Real-time images of tidal recruitment using lung ultrasound

Overview of attention for article published in The Ultrasound Journal, December 2015
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Title
Real-time images of tidal recruitment using lung ultrasound
Published in
The Ultrasound Journal, December 2015
DOI 10.1186/s13089-015-0036-2
Pubmed ID
Authors

Gerardo Tusman, Cecilia M. Acosta, Marco Nicola, Mariano Esperatti, Stephan H. Bohm, Fernando Suarez-Sipmann

Abstract

Ventilator-induced lung injury is a form of mechanical damage leading to a pulmonary inflammatory response related to the use of mechanical ventilation enhanced by the presence of atelectasis. One proposed mechanism of this injury is the repetitive opening and closing of collapsed alveoli and small airways within these atelectatic areas-a phenomenon called tidal recruitment. The presence of tidal recruitment is difficult to detect, even with high-resolution images of the lungs like CT scan. The purpose of this article is to give evidence of tidal recruitment by lung ultrasound. A standard lung ultrasound inspection detected lung zones of atelectasis in mechanically ventilated patients. With a linear probe placed in the intercostal oblique position. We observed tidal recruitment within atelectasis as an improvement in aeration at the end of inspiration followed by the re-collapse at the end of expiration. This mechanism disappeared after the performance of a lung recruitment maneuver. Lung ultrasound was helpful in detecting the presence of atelectasis and tidal recruitment and in confirming their resolution after a lung recruitment maneuver.

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The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 1%
United States 1 1%
Unknown 79 98%

Demographic breakdown

Readers by professional status Count As %
Other 16 20%
Researcher 13 16%
Student > Doctoral Student 9 11%
Student > Postgraduate 6 7%
Professor > Associate Professor 6 7%
Other 17 21%
Unknown 14 17%
Readers by discipline Count As %
Medicine and Dentistry 55 68%
Nursing and Health Professions 2 2%
Arts and Humanities 1 1%
Veterinary Science and Veterinary Medicine 1 1%
Engineering 1 1%
Other 0 0%
Unknown 21 26%