Coagulation parameters abnormalities and their relation to clinical outcomes in hospitalized and severe COVID-19 patients: prospective study

SCIENTIFIC REPORTS(2022)

引用 3|浏览10
暂无评分
摘要
There has been growing attention toward the predictive value of the coagulation parameters abnormalities in COVID-19. The aim of the study was to investigate the role of coagulation parameters namely Prothrombin concentration (PC), activated Partial thromboplastin Time (aPTT), D-Dimer (DD), Anti Thrombin III (ATIII) and fibrinogen (Fg) together with hematological, and biochemical parameters in predicting the severity of COVID-19 patients and estimating their relation to clinical outcomes in hospitalized and severe COVID-19 Patients. In a prospective study, a total of 267 newly diagnosed COVID-19 patients were enrolled. They were divided into two groups; hospitalized group which included 144 patients and non-hospitalized group that included 123 patients. According to severity, the patients were divided into severe group which included 71 patients and non-severe group that included 196 patients who were admitted to ward or not hospitalized. Clinical evaluation, measurement of coagulation parameters, biochemical indices, outcome and survival data were recorded. Hospitalized and severe patients were older and commonly presented with dyspnea (P ≤ 0.001). Differences in coagulation parameters were highly significant in hospitalized and severe groups in almost all parameters, same for inflammatory markers. D-dimer, AT-III and LDH showed excellent independently prediction of severity risk. With a cut-off of > 2.0 ng/L, the sensitivity and specificity of D dimer in predicting severity were 76% and 93%, respectively. Patients with coagulation abnormalities showed worse survival than those without (p = 0.002). Early assessment and dynamic monitoring of coagulation parameters may be a benchmark in the prediction of COVID-19 severity and death.
更多
查看译文
关键词
Medical research,Prognostic markers,Science,Humanities and Social Sciences,multidisciplinary
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要