Evidence map›Paper›PMID 31296985›Full record

ArticleInternational journal of chronic obstructive pulmonary disease2019

Clinical features of three-dimensional computed tomography-based radiologic phenotypes of chronic obstructive pulmonary disease.

Masato Karayama, Naoki Inui, Hideki Yasui, Masato Kono, Hironao Hozumi, Yuzo Suzuki, Kazuki Furuhashi, Dai Hashimoto, Noriyuki Enomoto, Tomoyuki Fujisawa and 3 more

Abstract read
In one paragraph

Article in International journal of chronic obstructive pulmonary disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. Observational
  3. Article
  4. Review
  5. Review
  6. 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

13 authors.

Masato KarayamaSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
Naoki InuiSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
Hideki YasuiSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
Masato KonoSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
Hironao HozumiSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
Yuzo SuzukiSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
Kazuki FuruhashiSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
Dai HashimotoSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
Noriyuki EnomotoSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
Tomoyuki FujisawaSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
Yutaro NakamuraSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
Hiroshi WatanabeDepartment of Clinical Pharmacology and Therapeutics, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.
Takafumi SudaSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Hamamatsu 431-3192, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The diagnosis and severity of chronic obstructive pulmonary disease (COPD) are defined by airflow limitation using spirometry. However, COPD has diverse clinical features, and several phenotypes based on non-spirometric data have been investigated. To identify novel phenotypes of COPD using radiologic data obtained by three-dimensional computed tomography (3D-CT). Patients and methods: The inner luminal area and wall thickness of third- to sixth-generation bronchi and the percentage of the low-attenuation area (less than -950 HU) of the lungs were measured using 3D-CT in patients with COPD. Using the radiologic data, hierarchical clustering was performed. Respiratory reactance and resistance were measured to evaluate functional differences among the clusters. Results: Four clusters were identified among 167 patients with COPD: Cluster I, mild emphysema with severe airway changes, severe airflow limitation, and high exacerbation risk; Cluster II, mild emphysema with moderate airway changes, mild airflow limitation, and mild dyspnea; Cluster III, severe emphysema with moderate airway changes, severe airflow limitation, and increased dyspnea; and Cluster IV, moderate emphysema with mild airway changes, mild airflow limitation, low exacerbation risk, and mild dyspnea. Cluster I had the highest respiratory resistance among the four clusters. Clusters I and III had higher respiratory reactance than Clusters II and IV. Conclusions: The 3D-CT-based radiologic phenotypes were associated with the clinical features of COPD. Measurement of respiratory resistance and reactance may help to identify phenotypic differences.

Indexed as

Imaging, Three-DimensionalTomography, X-Ray ComputedAgedAged, 80 and overAirway RemodelingAirway ResistanceCluster AnalysisCross-Sectional StudiesDyspneaFemaleHumansLungMaleMiddle AgedPhenotypePredictive Value of Testsairwaychronic obstructive pulmonary diseaseclustering analysisemphysemaphenotypethree-dimensional computed tomography

Identifiers

PMID31296985
PMCPMC6598936

What OpenQuestion holds

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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.