Prognostic Factors for Covid-19 on Admission Profile and Air Pollutants

Kikue Sato; Taiki Furukawa; Satoshi Yamashita; Daisuke Kobayashi; Shintaro Oyama; Yoshimune Shiratori

doi:10.3233/SHTI230301

Prognostic Factors for Covid-19 on Admission Profile and Air Pollutants

Kikue Sato^*, Taiki Furukawa, Satoshi Yamashita, Daisuke Kobayashi, Shintaro Oyama, Yoshimune Shiratori

^*Corresponding author for this work

Department of Community Medical Support

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

It has been reported that the severity and lethality of Covid-19 are associated with coexisting underlying diseases (hypertension, diabetes, etc.) and cardiovascular diseases (coronary artery disease, atrial fibrillation, heart failure, etc.) that increase with age, but environmental exposure such as air pollutants may also be a risk factor for mortality. In this study, we investigated patient characteristics at admission and prognostic factors of air pollutants in Covid-19 patients using a machine learning (random forest) prediction model. Age, Photochemical oxidant concentration one month prior to admission, and level of care required were shown to be highly important for the characteristics, while the cumulative concentrations of air pollutants SPM, NO2, and PM2.5 one year prior to admission were the most important characteristics for patients aged 65 years and older, suggesting the influence of long-term exposure.

Original language	English
Title of host publication	Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023
Editors	Maria Hagglund, Madeleine Blusi, Stefano Bonacina, Lina Nilsson, Inge Cort Madsen, Sylvia Pelayo, Anne Moen, Arriel Benis, Lars Lindskold, Parisis Gallos
Publisher	IOS Press BV
Pages	901-902
Number of pages	2
ISBN (Electronic)	9781643683881
DOIs	https://doi.org/10.3233/SHTI230301
State	Published - 2023/05/18
Event	33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023 - Gothenburg, Sweden Duration: 2023/05/22 → 2023/05/25

Publication series

Name	Studies in Health Technology and Informatics
Volume	302
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023
Country/Territory	Sweden
City	Gothenburg
Period	2023/05/22 → 2023/05/25

Keywords

Air Pollution Exposure
Covid-19
Machine Learning
Prognostic Factors

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3233/SHTI230301

Cite this

Sato, K., Furukawa, T., Yamashita, S., Kobayashi, D., Oyama, S., & Shiratori, Y. (2023). Prognostic Factors for Covid-19 on Admission Profile and Air Pollutants. In M. Hagglund, M. Blusi, S. Bonacina, L. Nilsson, I. C. Madsen, S. Pelayo, A. Moen, A. Benis, L. Lindskold, & P. Gallos (Eds.), Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023 (pp. 901-902). (Studies in Health Technology and Informatics; Vol. 302). IOS Press BV. https://doi.org/10.3233/SHTI230301

Sato, Kikue ; Furukawa, Taiki ; Yamashita, Satoshi et al. / Prognostic Factors for Covid-19 on Admission Profile and Air Pollutants. Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023. editor / Maria Hagglund ; Madeleine Blusi ; Stefano Bonacina ; Lina Nilsson ; Inge Cort Madsen ; Sylvia Pelayo ; Anne Moen ; Arriel Benis ; Lars Lindskold ; Parisis Gallos. IOS Press BV, 2023. pp. 901-902 (Studies in Health Technology and Informatics).

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title = "Prognostic Factors for Covid-19 on Admission Profile and Air Pollutants",

abstract = "It has been reported that the severity and lethality of Covid-19 are associated with coexisting underlying diseases (hypertension, diabetes, etc.) and cardiovascular diseases (coronary artery disease, atrial fibrillation, heart failure, etc.) that increase with age, but environmental exposure such as air pollutants may also be a risk factor for mortality. In this study, we investigated patient characteristics at admission and prognostic factors of air pollutants in Covid-19 patients using a machine learning (random forest) prediction model. Age, Photochemical oxidant concentration one month prior to admission, and level of care required were shown to be highly important for the characteristics, while the cumulative concentrations of air pollutants SPM, NO2, and PM2.5 one year prior to admission were the most important characteristics for patients aged 65 years and older, suggesting the influence of long-term exposure.",

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Sato, K, Furukawa, T, Yamashita, S, Kobayashi, D, Oyama, S & Shiratori, Y 2023, Prognostic Factors for Covid-19 on Admission Profile and Air Pollutants. in M Hagglund, M Blusi, S Bonacina, L Nilsson, IC Madsen, S Pelayo, A Moen, A Benis, L Lindskold & P Gallos (eds), Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023. Studies in Health Technology and Informatics, vol. 302, IOS Press BV, pp. 901-902, 33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023, Gothenburg, Sweden, 2023/05/22. https://doi.org/10.3233/SHTI230301

Prognostic Factors for Covid-19 on Admission Profile and Air Pollutants. / Sato, Kikue; Furukawa, Taiki; Yamashita, Satoshi et al.
Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023. ed. / Maria Hagglund; Madeleine Blusi; Stefano Bonacina; Lina Nilsson; Inge Cort Madsen; Sylvia Pelayo; Anne Moen; Arriel Benis; Lars Lindskold; Parisis Gallos. IOS Press BV, 2023. p. 901-902 (Studies in Health Technology and Informatics; Vol. 302).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Sato, Kikue

AU - Furukawa, Taiki

AU - Yamashita, Satoshi

AU - Kobayashi, Daisuke

AU - Oyama, Shintaro

AU - Shiratori, Yoshimune

PY - 2023/5/18

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N2 - It has been reported that the severity and lethality of Covid-19 are associated with coexisting underlying diseases (hypertension, diabetes, etc.) and cardiovascular diseases (coronary artery disease, atrial fibrillation, heart failure, etc.) that increase with age, but environmental exposure such as air pollutants may also be a risk factor for mortality. In this study, we investigated patient characteristics at admission and prognostic factors of air pollutants in Covid-19 patients using a machine learning (random forest) prediction model. Age, Photochemical oxidant concentration one month prior to admission, and level of care required were shown to be highly important for the characteristics, while the cumulative concentrations of air pollutants SPM, NO2, and PM2.5 one year prior to admission were the most important characteristics for patients aged 65 years and older, suggesting the influence of long-term exposure.

AB - It has been reported that the severity and lethality of Covid-19 are associated with coexisting underlying diseases (hypertension, diabetes, etc.) and cardiovascular diseases (coronary artery disease, atrial fibrillation, heart failure, etc.) that increase with age, but environmental exposure such as air pollutants may also be a risk factor for mortality. In this study, we investigated patient characteristics at admission and prognostic factors of air pollutants in Covid-19 patients using a machine learning (random forest) prediction model. Age, Photochemical oxidant concentration one month prior to admission, and level of care required were shown to be highly important for the characteristics, while the cumulative concentrations of air pollutants SPM, NO2, and PM2.5 one year prior to admission were the most important characteristics for patients aged 65 years and older, suggesting the influence of long-term exposure.

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ER -

Sato K, Furukawa T, Yamashita S, Kobayashi D, Oyama S, Shiratori Y. Prognostic Factors for Covid-19 on Admission Profile and Air Pollutants. In Hagglund M, Blusi M, Bonacina S, Nilsson L, Madsen IC, Pelayo S, Moen A, Benis A, Lindskold L, Gallos P, editors, Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023. IOS Press BV. 2023. p. 901-902. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI230301

Prognostic Factors for Covid-19 on Admission Profile and Air Pollutants

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this