Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning

Abstract COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the c...

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Autores principales:	Wan Xu, Nan-Nan Sun, Hai-Nv Gao, Zhi-Yuan Chen, Ya Yang, Bin Ju, Ling-Ling Tang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c661aa1aa333419daf9193c98167ac8b
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spelling	oai:doaj.org-article:c661aa1aa333419daf9193c98167ac8b2021-12-02T10:44:21ZRisk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning10.1038/s41598-021-82492-x2045-2322https://doaj.org/article/c661aa1aa333419daf9193c98167ac8b2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82492-xhttps://doaj.org/toc/2045-2322Abstract COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.Wan XuNan-Nan SunHai-Nv GaoZhi-Yuan ChenYa YangBin JuLing-Ling TangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wan Xu Nan-Nan Sun Hai-Nv Gao Zhi-Yuan Chen Ya Yang Bin Ju Ling-Ling Tang Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning
description	Abstract COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.
format	article
author	Wan Xu Nan-Nan Sun Hai-Nv Gao Zhi-Yuan Chen Ya Yang Bin Ju Ling-Ling Tang
author_facet	Wan Xu Nan-Nan Sun Hai-Nv Gao Zhi-Yuan Chen Ya Yang Bin Ju Ling-Ling Tang
author_sort	Wan Xu
title	Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning
title_short	Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning
title_full	Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning
title_fullStr	Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning
title_full_unstemmed	Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning
title_sort	risk factors analysis of covid-19 patients with ards and prediction based on machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c661aa1aa333419daf9193c98167ac8b
work_keys_str_mv	AT wanxu riskfactorsanalysisofcovid19patientswithardsandpredictionbasedonmachinelearning AT nannansun riskfactorsanalysisofcovid19patientswithardsandpredictionbasedonmachinelearning AT hainvgao riskfactorsanalysisofcovid19patientswithardsandpredictionbasedonmachinelearning AT zhiyuanchen riskfactorsanalysisofcovid19patientswithardsandpredictionbasedonmachinelearning AT yayang riskfactorsanalysisofcovid19patientswithardsandpredictionbasedonmachinelearning AT binju riskfactorsanalysisofcovid19patientswithardsandpredictionbasedonmachinelearning AT linglingtang riskfactorsanalysisofcovid19patientswithardsandpredictionbasedonmachinelearning
_version_	1718396788591820800

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning

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