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
Department of Emergency Medicine, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China.
Department of Critical Care Medicine, Tongji Medical College, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China.
Front Med (Lausanne). 2021 Dec 24;8:786414. doi: 10.3389/fmed.2021.786414. eCollection 2021.
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. 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. 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. 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.
为探讨抗凝治疗对经临床分层和无监督机器学习确定的亚组中2019冠状病毒病(COVID-19)患者结局和安全性的改善效果。这项单中心回顾性队列研究对2020年1月25日至3月23日期间收治于同济医院的2272例COVID-19患者进行了非选择性回顾。通过倾向评分(PS)匹配队列和全队列的治疗权重逆概率(IPTW)分析,研究了抗凝治疗与结局之间的关联。通过基于临床的分层或无监督机器学习进行亚组分析,以识别具有有意义临床特征的亚表型以及最能从抗凝治疗中获益的目标患者。在PS匹配队列或全队列的IPTW分析中,抗凝治疗均与较低的院内死亡风险相关。抗凝治疗组观察到临床相关非大出血(CRNMB)的发生率较高,但大出血发生率未升高。临床亚组分析表明,入院时,COVID-19临床分类的重症病例、轻度急性呼吸窘迫综合征(ARDS)病例以及D-二聚体水平≥0.5μg/mL的患者可能从抗凝治疗中获益。住院期间,危重症病例和重度ARDS病例可能从抗凝治疗中获益。无监督机器学习分析建立了一个四类聚类模型。聚类1和聚类2为非危重症病例,可能无法从抗凝治疗中获益,而聚类3和聚类4为危重症患者。聚类3中的患者可能从抗凝治疗中获益且出血事件未增加。而聚类4中的患者以多器官功能障碍(神经、循环、凝血、肾脏和肝功能障碍)和炎症生物标志物升高为特征,未从抗凝治疗中获益。抗凝治疗与较低的院内死亡风险相关,尤其是在重症COVID-19患者中。无监督学习分析表明,合并多器官功能障碍和炎症过度的最重症患者可能无法从抗凝治疗中获益。使用抗凝治疗时应更多关注出血事件(尤其是CRNMB)。