炎症生物标志物在重症COVID-19患者中的预后价值:一项单中心回顾性研究

IF 3.4 Q2 MEDICINE, RESEARCH & EXPERIMENTAL
Biomarker Insights Pub Date : 2021-06-24 eCollection Date: 2021-01-01 DOI:10.1177/11772719211027022
Gönül Açıksarı, Mehmet Koçak, Yasemin Çağ, Lütfiye Nilsun Altunal, Adem Atıcı, Fatma Betül Çelik, Furkan Bölen, Kurtuluş Açıksarı, Mustafa Çalışkan
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引用次数: 11

摘要

背景:目前对新型冠状病毒2019 (COVID-19)的认识表明,免疫系统和炎症反应在疾病的严重程度和预后中起着至关重要的作用。本研究旨在探讨c反应蛋白/白蛋白比值(CAR)、预后营养指数(PNI)、中性粒细胞与淋巴细胞比值(NLR)、淋巴细胞与单核细胞比值(LMR)和血小板与淋巴细胞比值(PLR)等系统性炎症生物标志物在重症COVID-19患者中的预后价值。方法:这项单中心、回顾性研究共纳入223例诊断为重症COVID-19的患者。主要结局指标为住院期间死亡率。进行多因素logistic回归分析,以确定与重症COVID-19患者死亡率相关的独立预测因素。使用受试者工作特征(ROC)曲线确定截止点,使用曲线下面积(AUC)值来证明生物标志物的鉴别能力。结果:与严重COVID-19幸存者相比,非幸存者的CAR、NLR和PLR较高,LMR和PNI较低(均P < 0.05)。检测预后的最佳CAR、PNI、NLR、PLR、LMR临界值分别为3.4、40.2、6。分别为27,312和1.54。CAR、PNI、NLR、PLR和LMR预测重症COVID-19患者住院死亡率的AUC值分别为0.81、0.91、0.85、0.63和0.65。在ROC分析中,CAR、PNI和NLR对医院死亡率的比较判别能力优于PLR和LMR。多变量分析显示,CAR(大于或等于0.34,P = 0.004)、NLR(大于或等于6.27,P = 0.012)和PNI(大于或等于40.2,P = 0.009)是与重症COVID-19患者死亡率相关的独立预测因子。结论:CAR、PNI和NLR是住院重症COVID-19患者死亡率的独立预测因子,与预后的相关性比PLR或LMR更密切。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prognostic Value of Inflammatory Biomarkers in Patients with Severe COVID-19: A Single-Center Retrospective Study.

Prognostic Value of Inflammatory Biomarkers in Patients with Severe COVID-19: A Single-Center Retrospective Study.

Prognostic Value of Inflammatory Biomarkers in Patients with Severe COVID-19: A Single-Center Retrospective Study.

Prognostic Value of Inflammatory Biomarkers in Patients with Severe COVID-19: A Single-Center Retrospective Study.

Background: The current knowledge about novel coronavirus-2019 (COVID-19) indicates that the immune system and inflammatory response play a crucial role in the severity and prognosis of the disease. In this study, we aimed to investigate prognostic value of systemic inflammatory biomarkers including C-reactive protein/albumin ratio (CAR), prognostic nutritional index (PNI), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) in patients with severe COVID-19.

Methods: This single-center, retrospective study included a total of 223 patients diagnosed with severe COVID-19. Primary outcome measure was mortality during hospitalization. Multivariate logistic regression analyses were performed to identify independent predictors associated with mortality in patients with severe COVID-19. Receiver operating characteristic (ROC) curve was used to determine cut-offs, and area under the curve (AUC) values were used to demonstrate discriminative ability of biomarkers.

Results: Compared to survivors of severe COVID-19, non-survivors had higher CAR, NLR, and PLR, and lower LMR and lower PNI (P < .05 for all). The optimal CAR, PNI, NLR, PLR, and LMR cut-off values for detecting prognosis were 3.4, 40.2, 6. 27, 312, and 1.54 respectively. The AUC values of CAR, PNI, NLR, PLR, and LMR for predicting hospital mortality in patients with severe COVID-19 were 0.81, 0.91, 0.85, 0.63, and 0.65, respectively. In ROC analysis, comparative discriminative ability of CAR, PNI, and NLR for hospital mortality were superior to PLR and LMR. Multivariate analysis revealed that CAR (⩾0.34, P = .004), NLR (⩾6.27, P = .012), and PNI (⩽40.2, P = .009) were independent predictors associated with mortality in severe COVID-19 patients.

Conclusions: The CAR, PNI, and NLR are independent predictors of mortality in hospitalized severe COVID-19 patients and are more closely associated with prognosis than PLR or LMR.

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来源期刊
Biomarker Insights
Biomarker Insights MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
6.00
自引率
0.00%
发文量
26
审稿时长
8 weeks
期刊介绍: An open access, peer reviewed electronic journal that covers all aspects of biomarker research and clinical applications.
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