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

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 with...

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Autores principales: Jinyan, LYU, Wanting, CUI, Joseph, FINKELSTEIN
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10640797/
https://www.ncbi.nlm.nih.gov/pubmed/35612095
http://dx.doi.org/10.3233/SHTI220473
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author Jinyan, LYU
Wanting, CUI
Joseph, FINKELSTEIN
author_facet Jinyan, LYU
Wanting, CUI
Joseph, FINKELSTEIN
author_sort Jinyan, LYU
collection PubMed
description 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.
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spelling pubmed-106407972023-11-11 Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19 Jinyan, LYU Wanting, CUI Joseph, FINKELSTEIN Stud Health Technol Inform Article 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. 2022-05-25 /pmc/articles/PMC10640797/ /pubmed/35612095 http://dx.doi.org/10.3233/SHTI220473 Text en https://creativecommons.org/licenses/by/4.0/Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
spellingShingle Article
Jinyan, LYU
Wanting, CUI
Joseph, FINKELSTEIN
Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19
title Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19
title_full Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19
title_fullStr Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19
title_full_unstemmed Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19
title_short Using Big Data to Identify Impact of Asthma on Mortality in Patients with COVID-19
title_sort using big data to identify impact of asthma on mortality in patients with covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10640797/
https://www.ncbi.nlm.nih.gov/pubmed/35612095
http://dx.doi.org/10.3233/SHTI220473
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