Evidence map›Paper›PMID 32984832›Full record

ArticleCritical care explorations2020

In Silico Modeling of Coronavirus Disease 2019 Acute Respiratory Distress Syndrome: Pathophysiologic Insights and Potential Management Implications.

Anup Das, Sina Saffaran, Marc Chikhani, Timothy E Scott, Marianna Laviola, Nadir Yehya, John G Laffey, Jonathan G Hardman, Declan G Bates

Open access · goldAbstract read
In one paragraph

Article in Critical care explorations, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
2.2field-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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. Pooled it
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  3. Article
  4. Article
  5. Observational
  6. Review
  7. Article
  8. Review
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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

9 authors at 4 institutions in 2 countries.

Anup DasSchool of Engineering, University of Warwick, Coventry, United Kingdom.
Sina SaffaranSchool of Engineering, University of Warwick, Coventry, United Kingdom.
Marc ChikhaniNottingham University Hospitals NHS Trust, Nottingham, United Kingdom.
Timothy E ScottUniversity Hospitals North Midlands, Stoke-On-Trent, United Kingdom.
Marianna LaviolaSchool of Medicine, University of Nottingham, Nottingham, United Kingdom.
Nadir YehyaDivision of Pediatric Critical Care Medicine, Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA.
John G LaffeyGalway University Hospitals, Galway, Ireland.
Jonathan G HardmanNottingham University Hospitals NHS Trust, Nottingham, United Kingdom.
Declan G BatesSchool of Engineering, University of Warwick, Coventry, United Kingdom.
University of Warwick · GBUniversity of Nottingham · GBNottingham University Hospitals NHS Trust · GBUniversity Hospital Galway · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesPatients with coronavirus disease 2019 acute respiratory distress syndrome appear to present with at least two distinct phenotypes: severe hypoxemia with relatively well-preserved lung compliance and lung gas volumes (type 1) and a more conventional acute respiratory distress syndrome phenotype, displaying the typical characteristics of the "baby lung" (type 2). We aimed to test plausible hypotheses regarding the pathophysiologic mechanisms underlying coronavirus disease 2019 acute respiratory distress syndrome and to evaluate the resulting implications for ventilatory management.

designWe adapted a high-fidelity computational simulator, previously validated in several studies of acute respiratory distress syndrome, to: 1) develop quantitative insights into the key pathophysiologic differences between the coronavirus disease 2019 acute respiratory distress syndrome and the conventional acute respiratory distress syndrome and 2) assess the impact of different positive end-expiratory pressure, Fio

settingInterdisciplinary Collaboration in Systems Medicine Research Network. SUBJECTS: The simulator was calibrated to represent coronavirus disease 2019 acute respiratory distress syndrome patients with both normal and elevated body mass indices undergoing invasive mechanical ventilation.

interventionsNone. MEASUREMENTS AND MAIN

resultsAn acute respiratory distress syndrome model implementing disruption of hypoxic pulmonary vasoconstriction and vasodilation leading to hyperperfusion of collapsed lung regions failed to replicate clinical data on type 1 coronavirus disease 2019 acute respiratory distress syndrome patients. Adding mechanisms to reflect disruption of alveolar gas-exchange due to the effects of pneumonitis and heightened vascular resistance due to the emergence of microthrombi produced levels of ventilation perfusion mismatch and hypoxemia consistent with data from type 1 coronavirus disease 2019 acute respiratory distress syndrome patients, while preserving close-to-normal lung compliance and gas volumes. Atypical responses to positive end-expiratory pressure increments between 5 and 15 cm H

conclusionsOur model suggests that use of standard positive end-expiratory pressure/Fio

Indexed as

acute respiratory distress syndromecoronavirus disease 2019mechanical ventilationventilator-induced lung injury

Identifiers

PMID32984832
PMCPMC7505291
OpenAlexW3088365441

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.