Evidence map›Paper›PMID 25471125›Full record

ArticleJournal of translational medicine2014

Computational modeling of the obstructive lung diseases asthma and COPD.

Kelly Suzanne Burrowes, Tom Doel, Chris Brightling

Abstract read
In one paragraph

Article in Journal of translational medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
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

25 citing papers in PubMed.

  1. Review
  2. Article
  3. Observational
  4. Article
  5. Article
  6. COPD Diagnosis: Time for Disruption.Journal of clinical medicine · 2021
    Review
  7. Article
  8. Computational fluid dynamics of the airways after left-upper pulmonary lobectomy: A case study.International journal for numerical methods in biomedical engineering · 2021
    Article
  9. Article
  10. Article
  11. Multi-scale models of lung fibrosis.Matrix biology : journal of the International Society for Matrix Biology · 2020
    Review
  12. Article
  13. Patient-specific modeling of aerosol delivery in healthy and asthmatic adults.Journal of applied physiology (Bethesda, Md. : 1985) · 2019
    Article
  14. Article
  15. Personalized medicine for patients with COPD: where are we?International journal of chronic obstructive pulmonary disease · 2019
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

3 authors.

Kelly Suzanne Burrowes
Tom Doel
Chris Brightling

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airway obstruction and airflow imitation and pose a huge burden to society. These obstructive lung diseases impact the lung physiology across multiple biological scales. Environmental stimuli are introduced via inhalation at the organ scale, and consequently impact upon the tissue, cellular and sub-cellular scale by triggering signaling pathways. These changes are propagated upwards to the organ level again and vice versa. In order to understand the pathophysiology behind these diseases we need to integrate and understand changes occurring across these scales and this is the driving force for multiscale computational modeling. There is an urgent need for improved diagnosis and assessment of obstructive lung diseases. Standard clinical measures are based on global function tests which ignore the highly heterogeneous regional changes that are characteristic of obstructive lung disease pathophysiology. Advances in scanning technology such as hyperpolarized gas MRI has led to new regional measurements of ventilation, perfusion and gas diffusion in the lungs, while new image processing techniques allow these measures to be combined with information from structural imaging such as Computed Tomography (CT). However, it is not yet known how to derive clinical measures for obstructive diseases from this wealth of new data. Computational modeling offers a powerful approach for investigating this relationship between imaging measurements and disease severity, and understanding the effects of different disease subtypes, which is key to developing improved diagnostic methods. Gaining an understanding of a system as complex as the respiratory system is difficult if not impossible via experimental methods alone. Computational models offer a complementary method to unravel the structure-function relationships occurring within a multiscale, multiphysics system such as this. Here we review the currentstate-of-the-art in techniques developed for pulmonary image analysis, development of structural models of therespiratory system and predictions of function within these models. We discuss application of modeling techniques to obstructive lung diseases, namely asthma and emphysema and the use of models to predict response to therapy. Finally we introduce a large European project, AirPROM that is developing multiscale models toinvestigate structure-function relationships in asthma and COPD.

Indexed as

AlgorithmsAsthmaComputer SimulationEmphysemaEuropeHumansLungLung Diseases, ObstructiveMagnetic Resonance ImagingPulmonary Disease, Chronic ObstructiveRespirationSignal TransductionTomography, X-Ray Computed

Identifiers

PMID25471125
PMCPMC4255909

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.