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Title: Patient-ventilator asynchronies: Types, outcomes and nursing detection skills
Authors: Enrico Bulleri
Cristian Fusi
Stefano Bambi
Luigi Pisani
Azienda Ospedaliera Careggi
Mahidol University
Amsterdam UMC - University of Amsterdam
Fondazione Poliambulanza
Keywords: Medicine
Issue Date: 1-Jan-2018
Citation: Acta Biomedica. Vol.89, No.7 (2018), 6-18
Abstract: © Mattioli 1885. Background: Mechanical ventilation is often employed as partial ventilatory support where both the patient and the ventilator work together. The ventilator settings should be adjusted to maintain a harmonious patient–ventilator interaction. However, this balance is often altered by many factors able to generate a patient ventilator asynchrony (PVA). The aims of this review were: to identify PVAs, their typologies and classifications; to describe how and to what extent their occurrence can affect the patients’ outcomes; to investigate the levels of nursing skill in detecting PVAs. Methods: Literature review performed on Cochrane Library, Medline and CINAHL databases. Results: 1610 records were identified; 43 records were included after double blind screening. PVAs have been classified with respect to the phase of the respiratory cycle or based on the circumstance of occurrence. There is agreement on the existence of 7 types of PVAs: ineffective effort, double trigger, premature cycling, delayed cycling, reverse triggering, flow starvation and auto-cycling. PVAs can be identified through the ventilator graphics monitoring of pressure and flow waveforms. The influence on patient outcomes varies greatly among studies but PVAs are mostly associated with poorer outcomes. Adequately trained nurses can learn and retain how to correctly detect PVAs. Conclusions: Since its challenging interpretation and the potential advantages of its implementation, ventilator graphics monitoring can be classified as an advanced competence for ICU nurses. The knowledge and skills to adequately manage PVAs should be provided by specific post-graduate university courses.
ISSN: 25316745
Appears in Collections:Scopus 2018

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