Siegal Deborah M, Barnes Geoffrey D, Langlois Nicole J, Lee Adrienne, Middeldorp Saskia, Skeith Leslie, Wood William A, Le Gal Grégoire
Department of Medicine, University of Ottawa, Ottawa, ON, Canada.
Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Blood Adv. 2020 Dec 22;4(24):6259-6273. doi: 10.1182/bloodadvances.2020003269.
Thrombosis has emerged as an important complication of coronavirus disease 2019 (COVID-19), particularly among individuals with severe illness. However, the precise incidence of thrombotic events remains uncertain due to differences in study design, patient populations, outcome ascertainment, event definitions, and reporting. In an effort to overcome some of these challenges and promote standardized data collection and reporting in clinical studies, the American Society of Hematology Research Collaborative COVID-19 Non-Malignant Hematology Task Force, in collaboration with the International Society on Thrombosis and Haemostasis COVID-19 Task Force, developed sets of data elements in the following domains: venous thromboembolism, myocardial infarction, stroke/transient ischemic attack, peripheral arterial thrombosis, bleeding, laboratory investigations, and antithrombotic therapy. Data elements in each of these domains were developed with 3 levels of detail to facilitate their incorporation into studies evaluating a range of interventions and outcomes. Previously published data elements were included where possible. The use of standardized variables in a range of clinical studies can enhance the quality of data collection, create efficiency, enhance comparison of results across studies, and facilitate future pooling of data sets.
血栓形成已成为2019冠状病毒病(COVID-19)的一种重要并发症,尤其是在重症患者中。然而,由于研究设计、患者群体、结果确定、事件定义和报告方面的差异,血栓形成事件的确切发生率仍不确定。为了克服其中一些挑战,并促进临床研究中标准化的数据收集和报告,美国血液学会研究协作COVID-19非恶性血液学特别工作组与国际血栓与止血学会COVID-19特别工作组合作,在以下领域制定了一系列数据元素:静脉血栓栓塞、心肌梗死、中风/短暂性脑缺血发作、外周动脉血栓形成、出血、实验室检查和抗血栓治疗。这些领域中的每个数据元素都有3个详细程度级别,以方便将其纳入评估一系列干预措施和结果的研究中。在可能的情况下纳入了先前发表的数据元素。在一系列临床研究中使用标准化变量可以提高数据收集的质量、提高效率、增强不同研究结果的可比性,并便于未来对数据集进行汇总。