肝密度降低是严重新冠肺炎的潜在预测因素:一项回顾性队列研究

Y. Shumskaya, D. A. Akhmedzyanova, M. Mnatsakanyan
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引用次数: 1

摘要

背景。为了对COVID-19患者的风险进行分层,重要的是要了解易导致严重病程的参数。以下危险因素描述:年龄超过60岁,超重,男性,慢性疾病:高血压,糖尿病。计算机断层扫描(CT)显示的低肝密度也被认为是潜在的危险因素。的目标。目的:评价低肝密度是否可以作为COVID-19严重程度的预测指标。材料和方法。回顾性单中心队列研究。包括在医院接受治疗的COVID-19患者,他们对胸部器官进行了两次动态CT扫描。根据病程严重程度将患者分为中度组、重度组和致死组。用回归分析估计各因素之间的关系。结果:99例患者入组;分为3组(中重度病程n=37,重度病程n=52,致死结局n=8)。各组c反应蛋白水平差异有统计学意义。多因素回归分析显示,入院时CT扫描肝脾密度比影响患者COVID-19严重程度[优势比12.18(95%可信区间1.6789.07);p = 0.008)。结论。CT扫描显示COVID-19患者肝脏密度降低可能是新型冠状病毒感染严重病程的预测指标。
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Decreased liver density as a potential predictor of severe COVID-19: a retrospective cohort study
Background. To stratify the risk in patients with COVID-19, it is important to understand the parameters that predispose to a severe course. Following risk factors were described: age over 60 years, overweight, male gender, chronic diseases: hypertension, diabetes mellitus. Low liver density on computed tomography (CT) is also considered as a potential risk factor. Aim. To evaluation whether low liver density can be used as a predictor of severe COVID-19. Materials and methods. Retrospective single-center cohort study. Patients with COVID-19 treated in a hospital setting, who underwent two CT scans of the thoracic organs in dynamics, were included. The patients were divided into groups according to the severity of the course (groups of moderate course, severe course and lethal outcome). Relation of the investigated factors was estimated using regression analysis. Results. 99 patients were enrolled; 3 comparison groups were formed (moderate-severe course n=37, severe course n=52, lethal outcome n=8). All groups significantly differed in C-reactive protein levels. According to multivariate regression analysis, COVID-19 severity was influenced by the liver to spleen density ratio as measured by CT scan on admission [odds ratio 12.18 (95% confidence interval 1.6789.07); p=0.008]. Conclusion. Reduced liver density on CT scan in a patient with COVID-19 may be a predictor of severe course of novel coronavirus infection.
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