Evidence map›Paper›PMID 35447459›Full record

ArticleComputers in biology and medicine2022

Towards a multi-scale computer modeling workflow for simulation of pulmonary ventilation in advanced COVID-19.

Shea Middleton, Elizabeth Dimbath, Anup Pant, Stephanie M George, Veeranna Maddipati, M Sean Peach, Kaida Yang, Andrew W Ju, Ali Vahdati

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
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 1 institution in 1 country.

Shea MiddletonDepartment of Engineering, College of Engineering and Technology, East Carolina University, Greenville, NC, USA.
Elizabeth DimbathDepartment of Engineering, College of Engineering and Technology, East Carolina University, Greenville, NC, USA.
Anup PantDepartment of Engineering, College of Engineering and Technology, East Carolina University, Greenville, NC, USA.
Stephanie M GeorgeDepartment of Engineering, College of Engineering and Technology, East Carolina University, Greenville, NC, USA.
Veeranna MaddipatiDivision of Pulmonary and Critical Medicine, Brody School of Medicine, East Carolina University, Greenville, NC, USA.
M Sean PeachDepartment of Radiation Oncology, Brody School of Medicine, East Carolina University, Greenville, NC, USA.
Kaida YangDepartment of Radiation Oncology, Brody School of Medicine, East Carolina University, Greenville, NC, USA.
Andrew W JuDepartment of Radiation Oncology, Brody School of Medicine, East Carolina University, Greenville, NC, USA.
Ali VahdatiDepartment of Engineering, College of Engineering and Technology, East Carolina University, Greenville, NC, USA. Electronic address: vahdatia18@ecu.edu.
East Carolina University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Physics-based multi-scale in silico models offer an excellent opportunity to study the effects of heterogeneous tissue damage on airflow and pressure distributions in COVID-19-afflicted lungs. The main objective of this study is to develop a computational modeling workflow, coupling airflow and tissue mechanics as the first step towards a virtual hypothesis-testing platform for studying injury mechanics of COVID-19-afflicted lungs. We developed a CT-based modeling approach to simulate the regional changes in lung dynamics associated with heterogeneous subject-specific COVID-19-induced damage patterns in the parenchyma. Furthermore, we investigated the effect of various levels of inflammation in a meso-scale acinar mechanics model on global lung dynamics. Our simulation results showed that as the severity of damage in the patient's right lower, left lower, and to some extent in the right upper lobe increased, ventilation was redistributed to the least injured right middle and left upper lobes. Furthermore, our multi-scale model reasonably simulated a decrease in overall tidal volume as the level of tissue injury and surfactant loss in the meso-scale acinar mechanics model was increased. This study presents a major step towards multi-scale computational modeling workflows capable of simulating the effect of subject-specific heterogenous COVID-19-induced lung damage on ventilation dynamics.

Indexed as

COVID-19ComputersComputer SimulationHumansLungPulmonary VentilationRespiratory MechanicsWorkflowAcute respiratory distress syndromeComputer modelingCOVID-19Lung mechanicsPulmonary mechanicsPulmonary ventilationSARS-CoV-2

Identifiers

PMID35447459
PMCPMC9005224
OpenAlexW4223637094

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.