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Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis

Preventing exacerbation in chronic obstructive pulmonary disease (COPD) patients is crucial, but requires identification of the exacerbating factors. To date, no integrated analysis of patient-derived and external factors has been reported. To identify factors associated with COPD exacerbation, we c...

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Autores principales: Lee, Jongmin, Jung, Hyun Myung, Kim, Sook Kyung, Yoo, Kwang Ha, Jung, Ki-Suck, Lee, Sang Haak, Rhee, Chin Kook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6491439/
https://www.ncbi.nlm.nih.gov/pubmed/31040338
http://dx.doi.org/10.1038/s41598-019-43167-w
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author Lee, Jongmin
Jung, Hyun Myung
Kim, Sook Kyung
Yoo, Kwang Ha
Jung, Ki-Suck
Lee, Sang Haak
Rhee, Chin Kook
author_facet Lee, Jongmin
Jung, Hyun Myung
Kim, Sook Kyung
Yoo, Kwang Ha
Jung, Ki-Suck
Lee, Sang Haak
Rhee, Chin Kook
author_sort Lee, Jongmin
collection PubMed
description Preventing exacerbation in chronic obstructive pulmonary disease (COPD) patients is crucial, but requires identification of the exacerbating factors. To date, no integrated analysis of patient-derived and external factors has been reported. To identify factors associated with COPD exacerbation, we collected data, including smoking status, lung function, and COPD assessment test scores, from 594 COPD patients in the Korean COPD subgroup study (KOCOSS), and merged these data with patients’ Korean Health Insurance Review and Assessment Service data for 2007–2012. We also collected primary weather variables, including levels of particulate matter <10 microns in diameter, daily minimum ambient temperature, as well as respiratory virus activities, and the logs of web queries on COPD-related issues. We then assessed the associations between these patient-derived and external factors and COPD exacerbations. Univariate analysis showed that patient factors, air pollution, various types of viruses, temperature, and the number of COPD-related web queries were associated with COPD exacerbation. Multivariate analysis revealed that the number of exacerbations in the preceding year, female sex, COPD grade, and influenza virus detection rate, and lowest temperature showed significant association with exacerbation. Our findings may help COPD patients predict when exacerbations are likely, and provide intervention as early as possible.
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spelling pubmed-64914392019-05-17 Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis Lee, Jongmin Jung, Hyun Myung Kim, Sook Kyung Yoo, Kwang Ha Jung, Ki-Suck Lee, Sang Haak Rhee, Chin Kook Sci Rep Article Preventing exacerbation in chronic obstructive pulmonary disease (COPD) patients is crucial, but requires identification of the exacerbating factors. To date, no integrated analysis of patient-derived and external factors has been reported. To identify factors associated with COPD exacerbation, we collected data, including smoking status, lung function, and COPD assessment test scores, from 594 COPD patients in the Korean COPD subgroup study (KOCOSS), and merged these data with patients’ Korean Health Insurance Review and Assessment Service data for 2007–2012. We also collected primary weather variables, including levels of particulate matter <10 microns in diameter, daily minimum ambient temperature, as well as respiratory virus activities, and the logs of web queries on COPD-related issues. We then assessed the associations between these patient-derived and external factors and COPD exacerbations. Univariate analysis showed that patient factors, air pollution, various types of viruses, temperature, and the number of COPD-related web queries were associated with COPD exacerbation. Multivariate analysis revealed that the number of exacerbations in the preceding year, female sex, COPD grade, and influenza virus detection rate, and lowest temperature showed significant association with exacerbation. Our findings may help COPD patients predict when exacerbations are likely, and provide intervention as early as possible. Nature Publishing Group UK 2019-04-30 /pmc/articles/PMC6491439/ /pubmed/31040338 http://dx.doi.org/10.1038/s41598-019-43167-w Text en © The Author(s) 2019 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Lee, Jongmin
Jung, Hyun Myung
Kim, Sook Kyung
Yoo, Kwang Ha
Jung, Ki-Suck
Lee, Sang Haak
Rhee, Chin Kook
Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis
title Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis
title_full Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis
title_fullStr Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis
title_full_unstemmed Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis
title_short Factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis
title_sort factors associated with chronic obstructive pulmonary disease exacerbation, based on big data analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6491439/
https://www.ncbi.nlm.nih.gov/pubmed/31040338
http://dx.doi.org/10.1038/s41598-019-43167-w
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