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Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk...

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Autores principales: Aktar, Sakifa, Talukder, Ashis, Ahamad, Md. Martuza, Kamal, A. H. M., Khan, Jahidur Rahman, Protikuzzaman, Md., Hossain, Nasif, Azad, A. K. M., Quinn, Julian M. W., Summers, Mathew A., Liaw, Teng, Eapen, Valsamma, Moni, Mohammad Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8393412/
https://www.ncbi.nlm.nih.gov/pubmed/34441317
http://dx.doi.org/10.3390/diagnostics11081383
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author Aktar, Sakifa
Talukder, Ashis
Ahamad, Md. Martuza
Kamal, A. H. M.
Khan, Jahidur Rahman
Protikuzzaman, Md.
Hossain, Nasif
Azad, A. K. M.
Quinn, Julian M. W.
Summers, Mathew A.
Liaw, Teng
Eapen, Valsamma
Moni, Mohammad Ali
author_facet Aktar, Sakifa
Talukder, Ashis
Ahamad, Md. Martuza
Kamal, A. H. M.
Khan, Jahidur Rahman
Protikuzzaman, Md.
Hossain, Nasif
Azad, A. K. M.
Quinn, Julian M. W.
Summers, Mathew A.
Liaw, Teng
Eapen, Valsamma
Moni, Mohammad Ali
author_sort Aktar, Sakifa
collection PubMed
description Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom–comorbidity combinations, it was observed that Pneumonia–Hypertension, Pneumonia–Diabetes, and Acute Respiratory Distress Syndrome (ARDS)–Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resources.
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spelling pubmed-83934122021-08-28 Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19 Aktar, Sakifa Talukder, Ashis Ahamad, Md. Martuza Kamal, A. H. M. Khan, Jahidur Rahman Protikuzzaman, Md. Hossain, Nasif Azad, A. K. M. Quinn, Julian M. W. Summers, Mathew A. Liaw, Teng Eapen, Valsamma Moni, Mohammad Ali Diagnostics (Basel) Article Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom–comorbidity combinations, it was observed that Pneumonia–Hypertension, Pneumonia–Diabetes, and Acute Respiratory Distress Syndrome (ARDS)–Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resources. MDPI 2021-07-31 /pmc/articles/PMC8393412/ /pubmed/34441317 http://dx.doi.org/10.3390/diagnostics11081383 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Aktar, Sakifa
Talukder, Ashis
Ahamad, Md. Martuza
Kamal, A. H. M.
Khan, Jahidur Rahman
Protikuzzaman, Md.
Hossain, Nasif
Azad, A. K. M.
Quinn, Julian M. W.
Summers, Mathew A.
Liaw, Teng
Eapen, Valsamma
Moni, Mohammad Ali
Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19
title Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19
title_full Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19
title_fullStr Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19
title_full_unstemmed Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19
title_short Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19
title_sort machine learning approaches to identify patient comorbidities and symptoms that increased risk of mortality in covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8393412/
https://www.ncbi.nlm.nih.gov/pubmed/34441317
http://dx.doi.org/10.3390/diagnostics11081383
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