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Cluster analysis in 975 patients with current cough identifies a phenotype with several cough triggers, many background disorders, and low quality of life
BACKGROUND: Recognition of disorder phenotypes may help to estimate prognosis and to guide the clinical management. Current cough management guidelines classify patients according to the duration of the cough episode. However, this classification is not based on phenotype analyses. The present study...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7441640/ https://www.ncbi.nlm.nih.gov/pubmed/32819357 http://dx.doi.org/10.1186/s12931-020-01485-y |
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author | Koskela, Heikki O. Selander, Tuomas A. Lätti, Anne M. |
author_facet | Koskela, Heikki O. Selander, Tuomas A. Lätti, Anne M. |
author_sort | Koskela, Heikki O. |
collection | PubMed |
description | BACKGROUND: Recognition of disorder phenotypes may help to estimate prognosis and to guide the clinical management. Current cough management guidelines classify patients according to the duration of the cough episode. However, this classification is not based on phenotype analyses. The present study aimed to identify cough phenotypes by clustering. METHODS: An email survey among employed, working-age subjects identified 975 patients with current cough. All filled in a comprehensive 80-item questionnaire including the Leicester Cough Questionnaire. Phenotypes were identified utilizing K-means partitional clustering. A subgroup filled in a follow-up questionnaire 12 months later to investigate the possible differences in the prognosis between the phenotypes. RESULTS: Two clusters were found. The cluster A included 608 patients (62.4% of the population) and the cluster B 367 patients (37.6%). The three most important variables to separate the clusters were the number of the triggers of cough (mean 2.63 (SD 2.22) vs. 6.95 (2.30), respectively, p < 0.001), the number of the cough background disorders (chronic rhinosinusitis, current asthma, gastroesophageal reflux disease, 0.29 (0.50) vs. 1.28 (0.75), respectively, p < 0.001), and the Leicester Cough Questionnaire physical domain (5.33 (0.76) vs. 4.25 (0.84), respectively, p < 0.001). There were significant interrelationships between these three variables (each p < 0.001). Duration of the episode was not among the most important variables to separate the clusters. At 12 months, 27.0% of the patients of the cluster A and 46.1% of the patients of the cluster B suffered from cough that had continued without interruptions from the first survey (p < 0.001). CONCLUSIONS: Two cough phenotypes could be identified. Cluster A represents phenotype A, which includes the majority of patients and has a tendency to heal by itself. The authors propose that cluster B represents phenotype TBQ (Triggers, Background disorders, Quality of life impairment). Given the poor prognosis of this phenotype, it urges a prompt and comprehensive clinical evaluation regardless of the duration of the cough episode. |
format | Online Article Text |
id | pubmed-7441640 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74416402020-08-24 Cluster analysis in 975 patients with current cough identifies a phenotype with several cough triggers, many background disorders, and low quality of life Koskela, Heikki O. Selander, Tuomas A. Lätti, Anne M. Respir Res Research BACKGROUND: Recognition of disorder phenotypes may help to estimate prognosis and to guide the clinical management. Current cough management guidelines classify patients according to the duration of the cough episode. However, this classification is not based on phenotype analyses. The present study aimed to identify cough phenotypes by clustering. METHODS: An email survey among employed, working-age subjects identified 975 patients with current cough. All filled in a comprehensive 80-item questionnaire including the Leicester Cough Questionnaire. Phenotypes were identified utilizing K-means partitional clustering. A subgroup filled in a follow-up questionnaire 12 months later to investigate the possible differences in the prognosis between the phenotypes. RESULTS: Two clusters were found. The cluster A included 608 patients (62.4% of the population) and the cluster B 367 patients (37.6%). The three most important variables to separate the clusters were the number of the triggers of cough (mean 2.63 (SD 2.22) vs. 6.95 (2.30), respectively, p < 0.001), the number of the cough background disorders (chronic rhinosinusitis, current asthma, gastroesophageal reflux disease, 0.29 (0.50) vs. 1.28 (0.75), respectively, p < 0.001), and the Leicester Cough Questionnaire physical domain (5.33 (0.76) vs. 4.25 (0.84), respectively, p < 0.001). There were significant interrelationships between these three variables (each p < 0.001). Duration of the episode was not among the most important variables to separate the clusters. At 12 months, 27.0% of the patients of the cluster A and 46.1% of the patients of the cluster B suffered from cough that had continued without interruptions from the first survey (p < 0.001). CONCLUSIONS: Two cough phenotypes could be identified. Cluster A represents phenotype A, which includes the majority of patients and has a tendency to heal by itself. The authors propose that cluster B represents phenotype TBQ (Triggers, Background disorders, Quality of life impairment). Given the poor prognosis of this phenotype, it urges a prompt and comprehensive clinical evaluation regardless of the duration of the cough episode. BioMed Central 2020-08-20 2020 /pmc/articles/PMC7441640/ /pubmed/32819357 http://dx.doi.org/10.1186/s12931-020-01485-y Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Koskela, Heikki O. Selander, Tuomas A. Lätti, Anne M. Cluster analysis in 975 patients with current cough identifies a phenotype with several cough triggers, many background disorders, and low quality of life |
title | Cluster analysis in 975 patients with current cough identifies a phenotype with several cough triggers, many background disorders, and low quality of life |
title_full | Cluster analysis in 975 patients with current cough identifies a phenotype with several cough triggers, many background disorders, and low quality of life |
title_fullStr | Cluster analysis in 975 patients with current cough identifies a phenotype with several cough triggers, many background disorders, and low quality of life |
title_full_unstemmed | Cluster analysis in 975 patients with current cough identifies a phenotype with several cough triggers, many background disorders, and low quality of life |
title_short | Cluster analysis in 975 patients with current cough identifies a phenotype with several cough triggers, many background disorders, and low quality of life |
title_sort | cluster analysis in 975 patients with current cough identifies a phenotype with several cough triggers, many background disorders, and low quality of life |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7441640/ https://www.ncbi.nlm.nih.gov/pubmed/32819357 http://dx.doi.org/10.1186/s12931-020-01485-y |
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