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Personalizing mechanical ventilation according to physiologic parameters to stabilize alveoli and minimize ventilator induced lung injury (VILI)

Overview of attention for article published in Intensive Care Medicine Experimental, February 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

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9 X users
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1 patent

Citations

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95 Dimensions

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232 Mendeley
Title
Personalizing mechanical ventilation according to physiologic parameters to stabilize alveoli and minimize ventilator induced lung injury (VILI)
Published in
Intensive Care Medicine Experimental, February 2017
DOI 10.1186/s40635-017-0121-x
Pubmed ID
Authors

Gary F. Nieman, Joshua Satalin, Penny Andrews, Hani Aiash, Nader M. Habashi, Louis A. Gatto

Abstract

It has been shown that mechanical ventilation in patients with, or at high-risk for, the development of acute respiratory distress syndrome (ARDS) can be a double-edged sword. If the mechanical breath is improperly set, it can amplify the lung injury associated with ARDS, causing a secondary ventilator-induced lung injury (VILI). Conversely, the mechanical breath can be adjusted to minimize VILI, which can reduce ARDS mortality. The current standard of care ventilation strategy to minimize VILI attempts to reduce alveolar over-distension and recruitment-derecruitment (R/D) by lowering tidal volume (Vt) to 6 cc/kg combined with adjusting positive-end expiratory pressure (PEEP) based on a sliding scale directed by changes in oxygenation. Thus, Vt is often but not always set as a "one-size-fits-all" approach and although PEEP is often set arbitrarily at 5 cmH2O, it may be personalized according to changes in a physiologic parameter, most often to oxygenation. However, there is evidence that oxygenation as a method to optimize PEEP is not congruent with the PEEP levels necessary to maintain an open and stable lung. Thus, optimal PEEP might not be personalized to the lung pathology of an individual patient using oxygenation as the physiologic feedback system. Multiple methods of personalizing PEEP have been tested and include dead space, lung compliance, lung stress and strain, ventilation patterns using computed tomography (CT) or electrical impedance tomography (EIT), inflection points on the pressure/volume curve (P/V), and the slope of the expiratory flow curve using airway pressure release ventilation (APRV). Although many studies have shown that personalizing PEEP is possible, there is no consensus as to the optimal technique. This review will assess various methods used to personalize PEEP, directed by physiologic parameters, necessary to adaptively adjust ventilator settings with progressive changes in lung pathophysiology.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 232 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Austria 1 <1%
Canada 1 <1%
Unknown 229 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 12%
Other 26 11%
Student > Postgraduate 24 10%
Student > Ph. D. Student 23 10%
Student > Master 17 7%
Other 58 25%
Unknown 57 25%
Readers by discipline Count As %
Medicine and Dentistry 126 54%
Engineering 21 9%
Nursing and Health Professions 13 6%
Agricultural and Biological Sciences 3 1%
Biochemistry, Genetics and Molecular Biology 2 <1%
Other 7 3%
Unknown 60 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 June 2023.
All research outputs
#3,474,593
of 23,850,698 outputs
Outputs from Intensive Care Medicine Experimental
#97
of 478 outputs
Outputs of similar age
#72,073
of 425,396 outputs
Outputs of similar age from Intensive Care Medicine Experimental
#2
of 7 outputs
Altmetric has tracked 23,850,698 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 478 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has done well, scoring higher than 79% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 425,396 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.