Title |
Real-time images of tidal recruitment using lung ultrasound
|
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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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Geographical breakdown
Country | Count | As % |
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United States | 2 | 13% |
United Kingdom | 2 | 13% |
Canada | 1 | 6% |
Guyana | 1 | 6% |
Australia | 1 | 6% |
Unknown | 9 | 56% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 15 | 94% |
Scientists | 1 | 6% |
Mendeley readers
Geographical breakdown
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Japan | 1 | 1% |
United States | 1 | 1% |
Unknown | 79 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
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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% |
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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% |