Prediction of Sepsis in COVID-19 Using Laboratory Indicators
BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients’ condition. We aim to diagnose Viral Sepsis Caused by SARS-CoV-2 b...
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Format: | Article |
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Frontiers Media S.A.
2021-03-01
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Series: | Frontiers in Cellular and Infection Microbiology |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2020.586054/full |
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Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guoxing Tang Ying Luo Feng Lu Wei Li Xiongcheng Liu Yucen Nan Yufei Ren Xiaofei Liao Song Wu Hai Jin Albert Y. Zomaya Ziyong Sun |
spellingShingle |
Guoxing Tang Ying Luo Feng Lu Wei Li Xiongcheng Liu Yucen Nan Yufei Ren Xiaofei Liao Song Wu Hai Jin Albert Y. Zomaya Ziyong Sun Prediction of Sepsis in COVID-19 Using Laboratory Indicators Frontiers in Cellular and Infection Microbiology COVID-19 sepsis coagulation function inflammatory factor artificial intelligence |
author_facet |
Guoxing Tang Ying Luo Feng Lu Wei Li Xiongcheng Liu Yucen Nan Yufei Ren Xiaofei Liao Song Wu Hai Jin Albert Y. Zomaya Ziyong Sun |
author_sort |
Guoxing Tang |
title |
Prediction of Sepsis in COVID-19 Using Laboratory Indicators |
title_short |
Prediction of Sepsis in COVID-19 Using Laboratory Indicators |
title_full |
Prediction of Sepsis in COVID-19 Using Laboratory Indicators |
title_fullStr |
Prediction of Sepsis in COVID-19 Using Laboratory Indicators |
title_full_unstemmed |
Prediction of Sepsis in COVID-19 Using Laboratory Indicators |
title_sort |
prediction of sepsis in covid-19 using laboratory indicators |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Cellular and Infection Microbiology |
issn |
2235-2988 |
publishDate |
2021-03-01 |
description |
BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients’ condition. We aim to diagnose Viral Sepsis Caused by SARS-CoV-2 by analyzing laboratory test data of patients with COVID-19 and establish an early predictive model for sepsis risk among patients with COVID-19.MethodsThis study retrospectively investigated laboratory test data of 2,453 patients with COVID-19 from electronic health records. Extreme gradient boosting (XGBoost) was employed to build four models with different feature subsets of a total of 69 collected indicators. Meanwhile, the explainable Shapley Additive ePlanation (SHAP) method was adopted to interpret predictive results and to analyze the feature importance of risk factors.FindingsThe model for classifying COVID-19 viral sepsis with seven coagulation function indicators achieved the area under the receiver operating characteristic curve (AUC) 0.9213 (95% CI, 89.94–94.31%), sensitivity 97.17% (95% CI, 94.97–98.46%), and specificity 82.05% (95% CI, 77.24–86.06%). The model for identifying COVID-19 coagulation disorders with eight features provided an average of 3.68 (±) 4.60 days in advance for early warning prediction with 0.9298 AUC (95% CI, 86.91–99.04%), 82.22% sensitivity (95% CI, 67.41–91.49%), and 84.00% specificity (95% CI, 63.08–94.75%).InterpretationWe found that an abnormality of the coagulation function was related to the occurrence of sepsis and the other routine laboratory test represented by inflammatory factors had a moderate predictive value on coagulopathy, which indicated that early warning of sepsis in COVID-19 patients could be achieved by our established model to improve the patient’s prognosis and to reduce mortality. |
topic |
COVID-19 sepsis coagulation function inflammatory factor artificial intelligence |
url |
https://www.frontiersin.org/articles/10.3389/fcimb.2020.586054/full |
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doaj-b80ad30a43e94268968c1be593319e2c2021-03-03T07:45:36ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882021-03-011010.3389/fcimb.2020.586054586054Prediction of Sepsis in COVID-19 Using Laboratory IndicatorsGuoxing Tang0Ying Luo1Feng Lu2Wei Li3Xiongcheng Liu4Yucen Nan5Yufei Ren6Xiaofei Liao7Song Wu8Hai Jin9Albert Y. Zomaya10Ziyong Sun11Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaNational Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, ChinaThe Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies, Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney, Sydney, NSW, AustraliaNational Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, ChinaThe Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies, Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney, Sydney, NSW, AustraliaDepartment of Computer Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaNational Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, ChinaNational Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, ChinaNational Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, ChinaThe Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies, Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney, Sydney, NSW, AustraliaDepartment of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaBackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients’ condition. We aim to diagnose Viral Sepsis Caused by SARS-CoV-2 by analyzing laboratory test data of patients with COVID-19 and establish an early predictive model for sepsis risk among patients with COVID-19.MethodsThis study retrospectively investigated laboratory test data of 2,453 patients with COVID-19 from electronic health records. Extreme gradient boosting (XGBoost) was employed to build four models with different feature subsets of a total of 69 collected indicators. Meanwhile, the explainable Shapley Additive ePlanation (SHAP) method was adopted to interpret predictive results and to analyze the feature importance of risk factors.FindingsThe model for classifying COVID-19 viral sepsis with seven coagulation function indicators achieved the area under the receiver operating characteristic curve (AUC) 0.9213 (95% CI, 89.94–94.31%), sensitivity 97.17% (95% CI, 94.97–98.46%), and specificity 82.05% (95% CI, 77.24–86.06%). The model for identifying COVID-19 coagulation disorders with eight features provided an average of 3.68 (±) 4.60 days in advance for early warning prediction with 0.9298 AUC (95% CI, 86.91–99.04%), 82.22% sensitivity (95% CI, 67.41–91.49%), and 84.00% specificity (95% CI, 63.08–94.75%).InterpretationWe found that an abnormality of the coagulation function was related to the occurrence of sepsis and the other routine laboratory test represented by inflammatory factors had a moderate predictive value on coagulopathy, which indicated that early warning of sepsis in COVID-19 patients could be achieved by our established model to improve the patient’s prognosis and to reduce mortality.https://www.frontiersin.org/articles/10.3389/fcimb.2020.586054/fullCOVID-19sepsiscoagulation functioninflammatory factorartificial intelligence |