Evidence map›Paper›PMID 27174922›Full record

ArticleJournal of applied physiology (Bethesda, Md. : 1985)2016

Predicting ventilator-induced lung injury using a lung injury cost function.

Katharine L Hamlington, Bradford J Smith, Gilman B Allen, Jason H T Bates

Open access · bronzeAbstract read
In one paragraph

Article in Journal of applied physiology (Bethesda, Md. : 1985), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 40 citations in OpenAlex.

  1. A precision approach to translational research in acute lung injury.American journal of respiratory and critical care medicine · 2026
    Article
  2. Review
  3. Article
  4. Review
  5. Article
  6. Thoracoabdominal asynchrony in a virtual preterm infant: computational modeling and analysis.American journal of physiology. Lung cellular and molecular physiology · 2023
    Article
  7. Article
  8. Engineering Dynamic 3D Models of Lung.Advances in experimental medicine and biology · 2023
    Article
  9. Article
  10. Computational lung modelling in respiratory medicine.Journal of the Royal Society, Interface · 2022
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Ventilator-induced lung injury and lung mechanics.Annals of translational medicine · 2018
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Katharine L HamlingtonDepartment of Medicine, University of Vermont College of Medicine, Burlington, Vermont.
Bradford J SmithDepartment of Medicine, University of Vermont College of Medicine, Burlington, Vermont.
Gilman B AllenDepartment of Medicine, University of Vermont College of Medicine, Burlington, Vermont.
Jason H T BatesDepartment of Medicine, University of Vermont College of Medicine, Burlington, Vermont jason.h.bates@uvm.edu.
University of Vermont · US

Funding

Multidisciplinary Training in Lung BiologyT32HL076122 · NHLBI · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI IRVIN, CHARLES G, POYNTER, MATTHEW E · 2004 to 2023
$6.1M
Preserving Epithelial Barrier Integrity in Ventilator-Induced Lung InjuryR01HL142702 · NHLBI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Jason HT Bates, DONALD P. GAVER · 2018 to 2026
$4.6M
Personalized Mechanical Ventilation for the Injured LungR01HL124052 · NHLBI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI BATES, JASON HT · 2014 to 2017
$2.4M
The Importance of Inhomogeneity in the Pathogenesis of Lung InjuryK99HL128944 · NHLBI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI SMITH, BRADFORD J · 2015 to 2016
$224k
NHLBI NIH HHS K99 HL128944NHLBI NIH HHS R01 HL124052NHLBI NIH HHS R01 HL142702NHLBI NIH HHS T32 HL076122
6 · The paper itself

Abstract

Managing patients with acute respiratory distress syndrome (ARDS) requires mechanical ventilation that balances the competing goals of sustaining life while avoiding ventilator-induced lung injury (VILI). In particular, it is reasonable to suppose that for any given ARDS patient, there must exist an optimum pair of values for tidal volume (VT) and positive end-expiratory pressure (PEEP) that together minimize the risk for VILI. To find these optimum values, and thus develop a personalized approach to mechanical ventilation in ARDS, we need to be able to predict how injurious a given ventilation regimen will be in any given patient so that the minimally injurious regimen for that patient can be determined. Our goal in the present study was therefore to develop a simple computational model of the mechanical behavior of the injured lung in order to calculate potential injury cost functions to serve as predictors of VILI. We set the model parameters to represent normal, mildly injured, and severely injured lungs and estimated the amount of volutrauma and atelectrauma caused by ventilating these lungs with a range of VT and PEEP. We estimated total VILI in two ways: 1) as the sum of the contributions from volutrauma and atelectrauma and 2) as the product of their contributions. We found the product provided estimates of VILI that are more in line with our previous experimental findings. This model may thus serve as the basis for the objective choice of mechanical ventilation parameters for the injured lung.

Indexed as

Computer SimulationHumansLungPositive-Pressure RespirationRespiration, ArtificialRespiratory Distress SyndromeRespiratory MechanicsTidal VolumeVentilator-Induced Lung Injuryacute respiratory distress syndromeatelectraumamechanical ventilationvolutrauma

Identifiers

PMID27174922
PMCPMC4967254
OpenAlexW2393715772

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.