Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19

Jinyan Lyu; Wanting Cui; Joseph Finkelstein

doi:10.3233/SHTI220473

Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19

Jinyan Lyu, Wanting Cui, Joseph Finkelstein

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

Abstract

The goal of this paper was to assess if mortality in COVID-19 positive patients is affected by a history of asthma in anamnesis. A total of 48,640 COVID-19 positive patients were included in our analysis. A propensity score matching was carried out to match each asthma patient with two patients without history of chronic respiratory diseases in one stratum. Matching was based on age, comorbidity score, and gender. Conditional logistics regression was used to compute within each strata. There were 5,557 strata in this model. We included asthma, ethnicity, race, and BMI as risk factors. The results showed that the presence of asthma in anamnesis is a statistically significant protective factor from mortality in COVID-19 positive patients.

Original language	English
Title of host publication	Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022
Editors	Brigitte Seroussi, Patrick Weber, Ferdinand Dhombres, Cyril Grouin, Jan-David Liebe, Jan-David Liebe, Jan-David Liebe, Sylvia Pelayo, Andrea Pinna, Bastien Rance, Bastien Rance, Lucia Sacchi, Adrien Ugon, Adrien Ugon, Arriel Benis, Parisis Gallos
Publisher	IOS Press BV
Pages	352-356
Number of pages	5
ISBN (Electronic)	9781643682846
DOIs	https://doi.org/10.3233/SHTI220473
State	Published - 25 May 2022
Event	32nd Medical Informatics Europe Conference, MIE 2022 - Nice, France Duration: 27 May 2022 → 30 May 2022

Publication series

Name	Studies in Health Technology and Informatics
Volume	294
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	32nd Medical Informatics Europe Conference, MIE 2022
Country/Territory	France
City	Nice
Period	27/05/22 → 30/05/22

Keywords

COVID-19
Conditional Logistic Regression
Mortality

Access to Document

10.3233/SHTI220473

Cite this

Lyu, J., Cui, W., & Finkelstein, J. (2022). Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19. In B. Seroussi, P. Weber, F. Dhombres, C. Grouin, J-D. Liebe, J-D. Liebe, J-D. Liebe, S. Pelayo, A. Pinna, B. Rance, B. Rance, L. Sacchi, A. Ugon, A. Ugon, A. Benis, & P. Gallos (Eds.), Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022 (pp. 352-356). (Studies in Health Technology and Informatics; Vol. 294). IOS Press BV. https://doi.org/10.3233/SHTI220473

Lyu, Jinyan ; Cui, Wanting ; Finkelstein, Joseph. / Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19. Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. editor / Brigitte Seroussi ; Patrick Weber ; Ferdinand Dhombres ; Cyril Grouin ; Jan-David Liebe ; Jan-David Liebe ; Jan-David Liebe ; Sylvia Pelayo ; Andrea Pinna ; Bastien Rance ; Bastien Rance ; Lucia Sacchi ; Adrien Ugon ; Adrien Ugon ; Arriel Benis ; Parisis Gallos. IOS Press BV, 2022. pp. 352-356 (Studies in Health Technology and Informatics).

@inproceedings{85c8d2e2648f44ef8911a8b2ed4d5267,

title = "Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19",

abstract = "The goal of this paper was to assess if mortality in COVID-19 positive patients is affected by a history of asthma in anamnesis. A total of 48,640 COVID-19 positive patients were included in our analysis. A propensity score matching was carried out to match each asthma patient with two patients without history of chronic respiratory diseases in one stratum. Matching was based on age, comorbidity score, and gender. Conditional logistics regression was used to compute within each strata. There were 5,557 strata in this model. We included asthma, ethnicity, race, and BMI as risk factors. The results showed that the presence of asthma in anamnesis is a statistically significant protective factor from mortality in COVID-19 positive patients.",

keywords = "COVID-19, Conditional Logistic Regression, Mortality",

author = "Jinyan Lyu and Wanting Cui and Joseph Finkelstein",

note = "Publisher Copyright: {\textcopyright} 2022 European Federation for Medical Informatics (EFMI) and IOS Press.; 32nd Medical Informatics Europe Conference, MIE 2022 ; Conference date: 27-05-2022 Through 30-05-2022",

year = "2022",

month = may,

day = "25",

doi = "10.3233/SHTI220473",

language = "English",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press BV",

pages = "352--356",

editor = "Brigitte Seroussi and Patrick Weber and Ferdinand Dhombres and Cyril Grouin and Jan-David Liebe and Jan-David Liebe and Jan-David Liebe and Sylvia Pelayo and Andrea Pinna and Bastien Rance and Bastien Rance and Lucia Sacchi and Adrien Ugon and Adrien Ugon and Arriel Benis and Parisis Gallos",

booktitle = "Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022",

address = "Netherlands",

}

Lyu, J, Cui, W & Finkelstein, J 2022, Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19. in B Seroussi, P Weber, F Dhombres, C Grouin, J-D Liebe, J-D Liebe, J-D Liebe, S Pelayo, A Pinna, B Rance, B Rance, L Sacchi, A Ugon, A Ugon, A Benis & P Gallos (eds), Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. Studies in Health Technology and Informatics, vol. 294, IOS Press BV, pp. 352-356, 32nd Medical Informatics Europe Conference, MIE 2022, Nice, France, 27/05/22. https://doi.org/10.3233/SHTI220473

Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19. / Lyu, Jinyan; Cui, Wanting; Finkelstein, Joseph.
Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. ed. / Brigitte Seroussi; Patrick Weber; Ferdinand Dhombres; Cyril Grouin; Jan-David Liebe; Jan-David Liebe; Jan-David Liebe; Sylvia Pelayo; Andrea Pinna; Bastien Rance; Bastien Rance; Lucia Sacchi; Adrien Ugon; Adrien Ugon; Arriel Benis; Parisis Gallos. IOS Press BV, 2022. p. 352-356 (Studies in Health Technology and Informatics; Vol. 294).

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

TY - GEN

T1 - Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19

AU - Lyu, Jinyan

AU - Cui, Wanting

AU - Finkelstein, Joseph

PY - 2022/5/25

Y1 - 2022/5/25

N2 - The goal of this paper was to assess if mortality in COVID-19 positive patients is affected by a history of asthma in anamnesis. A total of 48,640 COVID-19 positive patients were included in our analysis. A propensity score matching was carried out to match each asthma patient with two patients without history of chronic respiratory diseases in one stratum. Matching was based on age, comorbidity score, and gender. Conditional logistics regression was used to compute within each strata. There were 5,557 strata in this model. We included asthma, ethnicity, race, and BMI as risk factors. The results showed that the presence of asthma in anamnesis is a statistically significant protective factor from mortality in COVID-19 positive patients.

AB - The goal of this paper was to assess if mortality in COVID-19 positive patients is affected by a history of asthma in anamnesis. A total of 48,640 COVID-19 positive patients were included in our analysis. A propensity score matching was carried out to match each asthma patient with two patients without history of chronic respiratory diseases in one stratum. Matching was based on age, comorbidity score, and gender. Conditional logistics regression was used to compute within each strata. There were 5,557 strata in this model. We included asthma, ethnicity, race, and BMI as risk factors. The results showed that the presence of asthma in anamnesis is a statistically significant protective factor from mortality in COVID-19 positive patients.

KW - COVID-19

KW - Conditional Logistic Regression

KW - Mortality

UR - http://www.scopus.com/inward/record.url?scp=85131107499&partnerID=8YFLogxK

U2 - 10.3233/SHTI220473

DO - 10.3233/SHTI220473

M3 - Conference contribution

C2 - 35612095

AN - SCOPUS:85131107499

T3 - Studies in Health Technology and Informatics

SP - 352

EP - 356

BT - Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022

A2 - Seroussi, Brigitte

A2 - Weber, Patrick

A2 - Dhombres, Ferdinand

A2 - Grouin, Cyril

A2 - Liebe, Jan-David

A2 - Pelayo, Sylvia

A2 - Pinna, Andrea

A2 - Rance, Bastien

A2 - Sacchi, Lucia

A2 - Ugon, Adrien

A2 - Benis, Arriel

A2 - Gallos, Parisis

PB - IOS Press BV

T2 - 32nd Medical Informatics Europe Conference, MIE 2022

Y2 - 27 May 2022 through 30 May 2022

ER -

Lyu J, Cui W, Finkelstein J. Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19. In Seroussi B, Weber P, Dhombres F, Grouin C, Liebe J-D, Liebe J-D, Liebe J-D, Pelayo S, Pinna A, Rance B, Rance B, Sacchi L, Ugon A, Ugon A, Benis A, Gallos P, editors, Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. IOS Press BV. 2022. p. 352-356. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI220473

Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Cite this