Evidence map›Paper›PMID 31516821›Full record

ArticleRespiratory medicine case reports2019

Clinical ACO phenotypes: Description of a heterogeneous entity.

S Lainez, I Court-Fortune, P Vercherin, L Falchero, T Didi, P Beynel, D Piperno, E Frappe, M Froudarakis, J M Vergnon and 1 more

Abstract readCase Reports
In one paragraph

Article in Respiratory medicine case reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Hospitalization Outcomes of Patients with Asthma, COPD, and Asthma-COPD Overlap Syndrome.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2025
    Article
  2. Article
  3. Article
  4. Role of iBALT in Respiratory Immunity.Current topics in microbiology and immunology · 2020
    Review
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

11 authors.

S LainezService de Pneumologie, Hôpital Nord, CHU de Saint-Etienne, Saint-Etienne, France.
I Court-FortuneService de Pneumologie, Hôpital Nord, CHU de Saint-Etienne, Saint-Etienne, France.
P VercherinService de Santé Publique, Hôpital Nord, CHU de Saint-Etienne, Saint-Etienne, France.
L FalcheroService de Pneumologie, L'Hôpital Nord-Ouest, CH de Villefranche-sur-Saône, Gleizé, France.
T DidiService de Pneumologie, CH Annecy Genevois, Metz-Tessy, France.
P BeynelService de Pneumologie, Hôpital Fleyriat, Bourg-en-Bresse, France.
D PipernoCentre Médical Parot, Lyon, France.
E FrappeService de Pneumologie, Hôpital Du Gier, Saint-Chamond, France.
M FroudarakisService de Pneumologie, Hôpital Nord, CHU de Saint-Etienne, Saint-Etienne, France.
J M VergnonService de Pneumologie, Hôpital Nord, CHU de Saint-Etienne, Saint-Etienne, France.
G DevouassouxService de Pneumologie, Hôpital de La Croix-Rousse, Hospices Civils de Lyon, and EA7426, Université Claude Bernard Lyon 1, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBecause ACO (Asthma-COPD-Overlap) does not fill out asthma or COPD (Chronic Obstructive Pulmonary Disease) criteria, such patients are poorly evaluated. The aim of this study was to screen asthma and COPD for an alternative diagnosis of ACO, then to determine subgroups of patients, using cluster analysis. MATERIAL AND

methodsUsing GINA-GOLD stepwise approach, asthmatics and COPD were screened for ACO. Clusterization was then performed employing Multiple Correspondent Analysis (MCA) model, encompassing 9 variables (age, symptoms onset, sex, BMI (Body Mass Index), smoking, FEV-1, dyspnea, exacerbation, comorbidity). Finally, clusters were compared to determine phenotypes.

resultsMCA analysis was performed on 172 ACO subjects. To better distinguish clusters, the analysis was then focused on 55 subjects, having at least one cosine squared >0.3. Six clusters were identified, allowing the description of 4 phenotypes. Phenotype A represented overweighed heavy smokers, with an early onset and a severe disease (27% of ACO patients). Phenotype B gathered similar patients, with a late onset (29%). Patients from Phenotypes C-D were slighter smokers, presenting a moderate disease, with early and late onset respectively (respectively 13% and 31%).

conclusionsBy providing evidences for clusters within ACO, our study confirms its heterogeneity, allowing the identification of 4 phenotypes. Further prospective studies are mandatory to confirm these data, to determine both specific management requirements and prognostic value.

Indexed as

ACOAsthmaClusterCOPDHeterogeneityMCAPhenotype

Identifiers

PMID31516821
PMCPMC6733899

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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.