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Long-term PM(2.5) exposure and the clinical application of machine learning for predicting incident atrial fibrillation

Clinical impact of fine particulate matter (PM(2.5)) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurements of PM(2.5) for the 432,587 subjects of K...

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Autores principales: Kim, In-Soo, Yang, Pil-Sung, Jang, Eunsun, Jung, Hyunjean, You, Seng Chan, Yu, Hee Tae, Kim, Tae-Hoon, Uhm, Jae-Sun, Pak, Hui-Nam, Lee, Moon-Hyoung, Kim, Jong-Youn, Joung, Boyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7530980/
https://www.ncbi.nlm.nih.gov/pubmed/33004983
http://dx.doi.org/10.1038/s41598-020-73537-8
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author Kim, In-Soo
Yang, Pil-Sung
Jang, Eunsun
Jung, Hyunjean
You, Seng Chan
Yu, Hee Tae
Kim, Tae-Hoon
Uhm, Jae-Sun
Pak, Hui-Nam
Lee, Moon-Hyoung
Kim, Jong-Youn
Joung, Boyoung
author_facet Kim, In-Soo
Yang, Pil-Sung
Jang, Eunsun
Jung, Hyunjean
You, Seng Chan
Yu, Hee Tae
Kim, Tae-Hoon
Uhm, Jae-Sun
Pak, Hui-Nam
Lee, Moon-Hyoung
Kim, Jong-Youn
Joung, Boyoung
author_sort Kim, In-Soo
collection PubMed
description Clinical impact of fine particulate matter (PM(2.5)) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurements of PM(2.5) for the 432,587 subjects of Korean general population. We compared these incident AF prediction models using c-index, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). ML using the boosted ensemble method exhibited a higher c-index (0.845 [0.837–0.853]) than existing traditional regression models using CHA(2)DS(2)-VASc (0.654 [0.646–0.661]), CHADS(2) (0.652 [0.646–0.657]), or HATCH (0.669 [0.661–0.676]) scores (each p < 0.001) for predicting incident AF. As feature selection algorithms identified PM(2.5) as a highly important variable, we applied PM(2.5) for predicting incident AF and constructed scoring systems. The prediction performances significantly increased compared with models without PM(2.5) (c-indices: boosted ensemble ML, 0.954 [0.949–0.959]; PM-CHA(2)DS(2)-VASc, 0.859 [0.848–0.870]; PM-CHADS(2), 0.823 [0.810–0.836]; or PM-HATCH score, 0.849 [0.837–0.860]; each interaction, p < 0.001; NRI and IDI were also positive). ML combining readily available clinical variables and PM(2.5) data was found to predict incident AF better than models without PM(2.5) or even established risk prediction approaches in the general population exposed to high air pollution levels.
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spelling pubmed-75309802020-10-06 Long-term PM(2.5) exposure and the clinical application of machine learning for predicting incident atrial fibrillation Kim, In-Soo Yang, Pil-Sung Jang, Eunsun Jung, Hyunjean You, Seng Chan Yu, Hee Tae Kim, Tae-Hoon Uhm, Jae-Sun Pak, Hui-Nam Lee, Moon-Hyoung Kim, Jong-Youn Joung, Boyoung Sci Rep Article Clinical impact of fine particulate matter (PM(2.5)) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurements of PM(2.5) for the 432,587 subjects of Korean general population. We compared these incident AF prediction models using c-index, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). ML using the boosted ensemble method exhibited a higher c-index (0.845 [0.837–0.853]) than existing traditional regression models using CHA(2)DS(2)-VASc (0.654 [0.646–0.661]), CHADS(2) (0.652 [0.646–0.657]), or HATCH (0.669 [0.661–0.676]) scores (each p < 0.001) for predicting incident AF. As feature selection algorithms identified PM(2.5) as a highly important variable, we applied PM(2.5) for predicting incident AF and constructed scoring systems. The prediction performances significantly increased compared with models without PM(2.5) (c-indices: boosted ensemble ML, 0.954 [0.949–0.959]; PM-CHA(2)DS(2)-VASc, 0.859 [0.848–0.870]; PM-CHADS(2), 0.823 [0.810–0.836]; or PM-HATCH score, 0.849 [0.837–0.860]; each interaction, p < 0.001; NRI and IDI were also positive). ML combining readily available clinical variables and PM(2.5) data was found to predict incident AF better than models without PM(2.5) or even established risk prediction approaches in the general population exposed to high air pollution levels. Nature Publishing Group UK 2020-10-01 /pmc/articles/PMC7530980/ /pubmed/33004983 http://dx.doi.org/10.1038/s41598-020-73537-8 Text en © The Author(s) 2020 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 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/.
spellingShingle Article
Kim, In-Soo
Yang, Pil-Sung
Jang, Eunsun
Jung, Hyunjean
You, Seng Chan
Yu, Hee Tae
Kim, Tae-Hoon
Uhm, Jae-Sun
Pak, Hui-Nam
Lee, Moon-Hyoung
Kim, Jong-Youn
Joung, Boyoung
Long-term PM(2.5) exposure and the clinical application of machine learning for predicting incident atrial fibrillation
title Long-term PM(2.5) exposure and the clinical application of machine learning for predicting incident atrial fibrillation
title_full Long-term PM(2.5) exposure and the clinical application of machine learning for predicting incident atrial fibrillation
title_fullStr Long-term PM(2.5) exposure and the clinical application of machine learning for predicting incident atrial fibrillation
title_full_unstemmed Long-term PM(2.5) exposure and the clinical application of machine learning for predicting incident atrial fibrillation
title_short Long-term PM(2.5) exposure and the clinical application of machine learning for predicting incident atrial fibrillation
title_sort long-term pm(2.5) exposure and the clinical application of machine learning for predicting incident atrial fibrillation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7530980/
https://www.ncbi.nlm.nih.gov/pubmed/33004983
http://dx.doi.org/10.1038/s41598-020-73537-8
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