COVID-19 危重症患者的凝血、血管内皮功能和纤溶生物标志物：一项单中心前瞻性纵向研究。
Biomarkers of coagulation, endothelial function, and fibrinolysis in critically ill patients with COVID-19: A single-center prospective longitudinal study.
机构信息
Thrombosis and Atherosclerosis Research Institute, Hamilton, ON, Canada.
Department of Medical Sciences, McMaster University, Hamilton, ON, Canada.
出版信息
J Thromb Haemost. 2021 Jun;19(6):1546-1557. doi: 10.1111/jth.15327. Epub 2021 May 1.
BACKGROUND
Immunothrombosis and coagulopathy in the lung microvasculature may lead to lung injury and disease progression in coronavirus disease 2019 (COVID-19). We aim to identify biomarkers of coagulation, endothelial function, and fibrinolysis that are associated with disease severity and may have prognostic potential.
METHODS
We performed a single-center prospective study of 14 adult COVID-19(+) intensive care unit patients who were age- and sex-matched to 14 COVID-19(-) intensive care unit patients, and healthy controls. Daily blood draws, clinical data, and patient characteristics were collected. Baseline values for 10 biomarkers of interest were compared between the three groups, and visualized using Fisher's linear discriminant function. Linear repeated-measures mixed models were used to screen biomarkers for associations with mortality. Selected biomarkers were further explored and entered into an unsupervised longitudinal clustering machine learning algorithm to identify trends and targets that may be used for future predictive modelling efforts.
RESULTS
Elevated D-dimer was the strongest contributor in distinguishing COVID-19 status; however, D-dimer was not associated with survival. Variable selection identified clot lysis time, and antigen levels of soluble thrombomodulin (sTM), plasminogen activator inhibitor-1 (PAI-1), and plasminogen as biomarkers associated with death. Longitudinal multivariate k-means clustering on these biomarkers alone identified two clusters of COVID-19(+) patients: low (30%) and high (100%) mortality groups. Biomarker trajectories that characterized the high mortality cluster were higher clot lysis times (inhibited fibrinolysis), higher sTM and PAI-1 levels, and lower plasminogen levels.
CONCLUSIONS
Longitudinal trajectories of clot lysis time, sTM, PAI-1, and plasminogen may have predictive ability for mortality in COVID-19.
背景
肺微血管中的免疫血栓形成和凝血功能障碍可能导致 2019 年冠状病毒病（COVID-19）的肺损伤和疾病进展。我们旨在确定与疾病严重程度相关且可能具有预后潜力的凝血、内皮功能和纤溶的生物标志物。
方法
我们对 14 名成年 COVID-19（+）重症监护病房患者进行了单中心前瞻性研究，这些患者与 14 名 COVID-19（-）重症监护病房患者和健康对照者年龄和性别匹配。每天采集血液样本、临床数据和患者特征。比较三组之间 10 个感兴趣的生物标志物的基线值，并使用 Fisher 线性判别函数进行可视化。线性重复测量混合模型用于筛选与死亡率相关的生物标志物。选择的生物标志物进一步进行探索，并输入无监督纵向聚类机器学习算法，以确定可能用于未来预测模型研究的趋势和目标。
结果
升高的 D-二聚体是区分 COVID-19 状态的最强因素；然而，D-二聚体与存活无关。变量选择确定了纤维蛋白溶解时间，以及可溶性血栓调节蛋白（sTM）、纤溶酶原激活物抑制剂-1（PAI-1）和纤溶酶原的抗原水平作为与死亡相关的生物标志物。仅基于这些生物标志物的纵向多元 k-均值聚类确定了 COVID-19（+）患者的两个聚类：低（30%）和高（100%）死亡率组。特征为高死亡率组的生物标志物轨迹是更长的纤维蛋白溶解时间（抑制纤溶）、更高的 sTM 和 PAI-1 水平以及更低的纤溶酶原水平。
结论
纤维蛋白溶解时间、sTM、PAI-1 和纤溶酶原的纵向轨迹可能对 COVID-19 的死亡率具有预测能力。
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