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Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study

Objective: To explore the efficacy of anticoagulation in improving outcomes and safety of Coronavirus disease 2019 (COVID-19) patients in subgroups identified by clinical-based stratification and unsupervised machine learning. Methods: This single-center retrospective cohort study unselectively revi...

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Autores principales: Bian, Yi, Le, Yue, Du, Han, Chen, Junfang, Zhang, Ping, He, Zhigang, Wang, Ye, Yu, Shanshan, Fang, Yu, Yu, Gang, Ling, Jianmin, Feng, Yikuan, Wei, Sheng, Huang, Jiao, Xiao, Liuniu, Zheng, Yingfang, Yu, Zhen, Li, Shusheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8740912/
https://www.ncbi.nlm.nih.gov/pubmed/35004751
http://dx.doi.org/10.3389/fmed.2021.786414
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author Bian, Yi
Le, Yue
Du, Han
Chen, Junfang
Zhang, Ping
He, Zhigang
Wang, Ye
Yu, Shanshan
Fang, Yu
Yu, Gang
Ling, Jianmin
Feng, Yikuan
Wei, Sheng
Huang, Jiao
Xiao, Liuniu
Zheng, Yingfang
Yu, Zhen
Li, Shusheng
author_facet Bian, Yi
Le, Yue
Du, Han
Chen, Junfang
Zhang, Ping
He, Zhigang
Wang, Ye
Yu, Shanshan
Fang, Yu
Yu, Gang
Ling, Jianmin
Feng, Yikuan
Wei, Sheng
Huang, Jiao
Xiao, Liuniu
Zheng, Yingfang
Yu, Zhen
Li, Shusheng
author_sort Bian, Yi
collection PubMed
description Objective: To explore the efficacy of anticoagulation in improving outcomes and safety of Coronavirus disease 2019 (COVID-19) patients in subgroups identified by clinical-based stratification and unsupervised machine learning. Methods: This single-center retrospective cohort study unselectively reviewed 2,272 patients with COVID-19 admitted to the Tongji Hospital between Jan 25 and Mar 23, 2020. The association between AC treatment and outcomes was investigated in the propensity score (PS) matched cohort and the full cohort by inverse probability of treatment weighting (IPTW) analysis. Subgroup analysis, identified by clinical-based stratification or unsupervised machine learning, was used to identify sub-phenotypes with meaningful clinical features and the target patients benefiting most from AC. Results: AC treatment was associated with lower in-hospital death risk either in the PS matched cohort or by IPTW analysis in the full cohort. A higher incidence of clinically relevant non-major bleeding (CRNMB) was observed in the AC group, but not major bleeding. Clinical subgroup analysis showed that, at admission, severe cases of COVID-19 clinical classification, mild acute respiratory distress syndrome (ARDS) cases, and patients with a D-dimer level ≥0.5 μg/mL, may benefit from AC. During the hospital stay, critical cases and severe ARDS cases may benefit from AC. Unsupervised machine learning analysis established a four-class clustering model. Clusters 1 and 2 were non-critical cases and might not benefit from AC, while clusters 3 and 4 were critical patients. Patients in cluster 3 might benefit from AC with no increase in bleeding events. While patients in cluster 4, who were characterized by multiple organ dysfunction (neurologic, circulation, coagulation, kidney and liver dysfunction) and elevated inflammation biomarkers, did not benefit from AC. Conclusions: AC treatment was associated with lower in-hospital death risk, especially in critically ill COVID-19 patients. Unsupervised learning analysis revealed that the most critically ill patients with multiple organ dysfunction and excessive inflammation might not benefit from AC. More attention should be paid to bleeding events (especially CRNMB) when using AC.
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spelling pubmed-87409122022-01-08 Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study Bian, Yi Le, Yue Du, Han Chen, Junfang Zhang, Ping He, Zhigang Wang, Ye Yu, Shanshan Fang, Yu Yu, Gang Ling, Jianmin Feng, Yikuan Wei, Sheng Huang, Jiao Xiao, Liuniu Zheng, Yingfang Yu, Zhen Li, Shusheng Front Med (Lausanne) Medicine Objective: To explore the efficacy of anticoagulation in improving outcomes and safety of Coronavirus disease 2019 (COVID-19) patients in subgroups identified by clinical-based stratification and unsupervised machine learning. Methods: This single-center retrospective cohort study unselectively reviewed 2,272 patients with COVID-19 admitted to the Tongji Hospital between Jan 25 and Mar 23, 2020. The association between AC treatment and outcomes was investigated in the propensity score (PS) matched cohort and the full cohort by inverse probability of treatment weighting (IPTW) analysis. Subgroup analysis, identified by clinical-based stratification or unsupervised machine learning, was used to identify sub-phenotypes with meaningful clinical features and the target patients benefiting most from AC. Results: AC treatment was associated with lower in-hospital death risk either in the PS matched cohort or by IPTW analysis in the full cohort. A higher incidence of clinically relevant non-major bleeding (CRNMB) was observed in the AC group, but not major bleeding. Clinical subgroup analysis showed that, at admission, severe cases of COVID-19 clinical classification, mild acute respiratory distress syndrome (ARDS) cases, and patients with a D-dimer level ≥0.5 μg/mL, may benefit from AC. During the hospital stay, critical cases and severe ARDS cases may benefit from AC. Unsupervised machine learning analysis established a four-class clustering model. Clusters 1 and 2 were non-critical cases and might not benefit from AC, while clusters 3 and 4 were critical patients. Patients in cluster 3 might benefit from AC with no increase in bleeding events. While patients in cluster 4, who were characterized by multiple organ dysfunction (neurologic, circulation, coagulation, kidney and liver dysfunction) and elevated inflammation biomarkers, did not benefit from AC. Conclusions: AC treatment was associated with lower in-hospital death risk, especially in critically ill COVID-19 patients. Unsupervised learning analysis revealed that the most critically ill patients with multiple organ dysfunction and excessive inflammation might not benefit from AC. More attention should be paid to bleeding events (especially CRNMB) when using AC. Frontiers Media S.A. 2021-12-24 /pmc/articles/PMC8740912/ /pubmed/35004751 http://dx.doi.org/10.3389/fmed.2021.786414 Text en Copyright © 2021 Bian, Le, Du, Chen, Zhang, He, Wang, Yu, Fang, Yu, Ling, Feng, Wei, Huang, Xiao, Zheng, Yu and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Bian, Yi
Le, Yue
Du, Han
Chen, Junfang
Zhang, Ping
He, Zhigang
Wang, Ye
Yu, Shanshan
Fang, Yu
Yu, Gang
Ling, Jianmin
Feng, Yikuan
Wei, Sheng
Huang, Jiao
Xiao, Liuniu
Zheng, Yingfang
Yu, Zhen
Li, Shusheng
Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study
title Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study
title_full Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study
title_fullStr Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study
title_full_unstemmed Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study
title_short Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study
title_sort efficacy and safety of anticoagulation treatment in covid-19 patient subgroups identified by clinical-based stratification and unsupervised machine learning: a matched cohort study
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8740912/
https://www.ncbi.nlm.nih.gov/pubmed/35004751
http://dx.doi.org/10.3389/fmed.2021.786414
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