tetano
Editor, Senior Moderator
Front Med (Lausanne)
. 2021 Dec 24;8:786414.
doi: 10.3389/fmed.2021.786414. eCollection 2021.
Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study
Yi Bian[SUP] 1 2 [/SUP], Yue Le[SUP] 1 2 [/SUP], Han Du[SUP] 3 [/SUP], Junfang Chen[SUP] 4 [/SUP], Ping Zhang[SUP] 5 [/SUP], Zhigang He[SUP] 1 2 [/SUP], Ye Wang[SUP] 1 2 [/SUP], Shanshan Yu[SUP] 1 2 [/SUP], Yu Fang[SUP] 1 2 [/SUP], Gang Yu[SUP] 1 2 [/SUP], Jianmin Ling[SUP] 1 2 [/SUP], Yikuan Feng[SUP] 1 2 [/SUP], Sheng Wei[SUP] 6 [/SUP], Jiao Huang[SUP] 7 [/SUP], Liuniu Xiao[SUP] 1 2 [/SUP], Yingfang Zheng[SUP] 1 2 [/SUP], Zhen Yu[SUP] 8 [/SUP], Shusheng Li[SUP] 1 2 [/SUP]
Affiliations
Abstract
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.
Keywords: COVID-19; anticoagulation; bleeding events; mortality; outcomes; unsupervised machine learning.
. 2021 Dec 24;8:786414.
doi: 10.3389/fmed.2021.786414. eCollection 2021.
Efficacy and Safety of Anticoagulation Treatment in COVID-19 Patient Subgroups Identified by Clinical-Based Stratification and Unsupervised Machine Learning: A Matched Cohort Study
Yi Bian[SUP] 1 2 [/SUP], Yue Le[SUP] 1 2 [/SUP], Han Du[SUP] 3 [/SUP], Junfang Chen[SUP] 4 [/SUP], Ping Zhang[SUP] 5 [/SUP], Zhigang He[SUP] 1 2 [/SUP], Ye Wang[SUP] 1 2 [/SUP], Shanshan Yu[SUP] 1 2 [/SUP], Yu Fang[SUP] 1 2 [/SUP], Gang Yu[SUP] 1 2 [/SUP], Jianmin Ling[SUP] 1 2 [/SUP], Yikuan Feng[SUP] 1 2 [/SUP], Sheng Wei[SUP] 6 [/SUP], Jiao Huang[SUP] 7 [/SUP], Liuniu Xiao[SUP] 1 2 [/SUP], Yingfang Zheng[SUP] 1 2 [/SUP], Zhen Yu[SUP] 8 [/SUP], Shusheng Li[SUP] 1 2 [/SUP]
Affiliations
- PMID: 35004751
- PMCID: PMC8740912
- DOI: 10.3389/fmed.2021.786414
Abstract
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.
Keywords: COVID-19; anticoagulation; bleeding events; mortality; outcomes; unsupervised machine learning.