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

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.

Insights into COVID-19-associated critical illness: a narrative review

“…Given the remarkable contribution of coagulopathy in the pathogenesis of COVID-19 ( 108 ), anticoagulation therapy is anticipated to benefit patients with severe COVID-19. In this context, a single-center retrospective cohort study investigated the efficacy of anticoagulation therapy in patients with COVID-19, revealing that anticoagulation therapy was associated with lower in-hospital mortality in critically ill patients with COVID-19; however, unsupervised machine learning analysis revealed that the subgroup of patients with multiorgan dysfunction and excessive inflammation might not benefit from anticoagulation therapy ( 109 ).…”

Cluster Analysis to Distinguish Patients Most Likely to Benefit from Outpatient Cardiac Rehabilitation—A Prospective, Multicenter Study

“…Cluster analysis and other data-driven methods may help distinguish people who are most likely to benefit from health interventions and those who do not get any benefit [ 9 , 10 ]. Therefore, in this study, we used cluster analysis to distinguish different groups of patients eligible for outpatient cardiac rehabilitation based on several characteristics, and we compared outcomes between the groups.…”