Evidence map›Paper›PMID 32835707›Full record

ReviewChest2021

CT Imaging and Comorbidities in COPD: Beyond Lung Cancer Screening.

Deepti Singhvi, Jessica Bon

Open access · greenAbstract readReview
In one paragraph

Review in Chest, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.9field-weighted citation impact, top 11% 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

17 citing papers in PubMed, 31 citations in OpenAlex.

  1. Article
  2. Deep learning model for osteoporosis screening on chest CT with low tube voltage.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
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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

2 authors at 1 institution in 1 country.

Deepti SinghviDivision of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA.
Jessica BonDivision of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA; VA Pittsburgh Healthcare System, Pittsburgh, PA. Electronic address: Bonjm@upmc.edu.
University of Pittsburgh · US

Funding

Autoimmunity and emphysema and risk of osteoporosis in smokersR01HL128289 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BON, JESSICA · 2016 to 2020
$1.9M
NHLBI NIH HHS R01 HL128289
6 · The paper itself

Abstract

Comorbidities significantly contribute to morbidity, mortality, and health-care costs in individuals with COPD. Comorbidity prevalence does not always correlate with lung disease severity, and the elevated risk of certain comorbidities is often independent of shared risk factors such as tobacco burden. Although COPD management guidelines recognize the importance of identifying and treating comorbidities as part of the comprehensive management of COPD patients, little guidance is provided regarding best screening practices. Whereas universal comorbidity screening in COPD patients is likely not cost-effective, targeted early screening and treatment in those at highest risk may have a significant impact on COPD outcomes. Recent studies suggest that certain radiographic features on thoracic imaging may serve as surrogate markers of comorbidity in patients with COPD. This review evaluates these studies in the context of the growing availability of chest CT scans in the lung cancer screening era and discusses how chest CT imaging can be leveraged to identify those COPD patients at highest risk for comorbid disease.

Indexed as

Tomography, X-Ray ComputedHumansMass ScreeningPulmonary Disease, Chronic ObstructiveCAT scanchestCOPDcoronary artery diseaseosteoporosispulmonary hypertension

Identifiers

PMID32835707
PMCPMC8256436
OpenAlexW3097582112

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.