Evidence map›Paper›PMID 36223552›Full record

SynthesisChronic respiratory disease

Computed tomography-based body composition measures in COPD and their association with clinical outcomes: A systematic review.

John M Nicholson, Camila E Orsso, Sahar Nourouzpour, Brenawen Elangeswaran, Karan Chohan, Ani Orchanian-Cheff, Lee Fidler, Sunita Mathur, Dmitry Rozenberg

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Chronic respiratory disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
4.0field-weighted citation impact, top 5% 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

21 citing papers in PubMed, 28 citations in OpenAlex.

  1. Observational
  2. Article
  3. Article
  4. Review
  5. Article
  6. Observational
  7. Muscle and Fat Composition in OSA: A CT-Based Study.Journal of clinical medicine · 2025
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Review
  15. Article
  16. Article
  17. Review
  18. Article
  19. Review
  20. Unveiling the Knowledge Frontier: A Scientometric Analysis of COPD with Sarcopenia.International journal of chronic obstructive pulmonary disease · 2024
    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 5 institutions in 1 country.

John M NicholsonDepartment of Medicine, Respirology, 10033London Health Science Center, London, ON, Canada.
Camila E OrssoDepartment of Agricultural, Food and Nutritional Science, 3158University of Alberta, Edmonton, AB, Canada.
Sahar NourouzpourTemerty Faculty of Medicine, Respirology, Lung Transplant Program, Toronto General Hospital Research Institute, 7989University Health Network, Toronto, ON, Canada.
Brenawen ElangeswaranTemerty Faculty of Medicine, Respirology, Lung Transplant Program, Toronto General Hospital Research Institute, 7989University Health Network, Toronto, ON, Canada.
Karan ChohanTemerty Faculty of Medicine, Respirology, Lung Transplant Program, Toronto General Hospital Research Institute, 7989University Health Network, Toronto, ON, Canada.
Ani Orchanian-CheffLibrary and Information Services, 7989University Health Network, Toronto, ON, Canada.ORCID 0000-0002-9943-2692
Lee FidlerDepartment of Medicine, Respirology, 7989University Health Network, Toronto, Canada.
Sunita MathurDeparment of Physical Therapy, 7938University of Toronto, Toronto, ON, Canada.
Dmitry RozenbergTemerty Faculty of Medicine, Respirology, Lung Transplant Program, Toronto General Hospital Research Institute, 7989University Health Network, Toronto, ON, Canada.ORCID 0000-0001-8786-9152
University Health Network · CALondon Health Sciences Centre · CASunnybrook Health Science Centre · CAUniversity of Alberta · CAUniversity of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComputed tomography (CT) is commonly utilized in chronic obstructive pulmonary disease (COPD) for lung cancer screening and emphysema characterization. Computed tomography-morphometric analysis of body composition (muscle mass and adiposity) has gained increased recognition as a marker of disease severity and prognosis. This systematic review aimed to describe the CT-methodology used to assess body composition and identify the association of body composition measures and disease severity, health-related quality of life (HRQL), cardiometabolic risk factors, respiratory exacerbations, and survival in patients with COPD.

methodsSix databases were searched (inception-September 2021) for studies evaluating adult COPD patients using thoracic or abdominal CT-muscle or adiposity body composition measures. The systematic review was conducted in accordance with the PRISMA guidelines.

resultsTwenty eight articles were included with 15,431 COPD patients, across all GOLD stages with 77% males, age range (mean/median 59-78 years), and BMI range 19.8-29.3 kg/m

conclusionThere was significant variability in CT-body composition measures. In several studies, low muscle mass was associated with increased disease severity and lower HRQL, whereas adiposity with cardiovascular disease/risk factors. Given the heterogeneity in body composition measures and clinical outcomes, the prognostic utility of CT-body composition in COPD requires further study.

Indexed as

Cardiovascular DiseasesLung NeoplasmsPulmonary Disease, Chronic ObstructiveAdultAgedBody CompositionBody Mass IndexEarly Detection of CancerFemaleHumansMaleMiddle AgedObesityQuality of LifeTomography, X-Ray Computedbody compositionchronic obstructive pulmonary diseaseLung diseasesarcopeniatomography scannersX-ray computed

Identifiers

PMID36223552
PMCPMC9561670
OpenAlexW4306168070

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.