中性粒细胞/淋巴细胞比率作为预测 COVID-19 感染严重程度的生物标志物的作用

E. Borisova, S. Ivankovska, P. Pavlov, Asparuh Nikolov, P. Glogovska
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摘要

2019 年底,中国发现了几例由不明病原体引发的肺炎病例。2020 年 3 月,世界卫生组织宣布一种新的冠状病毒--SARS-CoV-2 病毒大流行。中性粒细胞/淋巴细胞比率(NLR)是一种生物标志物,近年来已成为许多疾病的重要诊断和预后指标。这项研究的目的是寻找 NLR 与 Covid-19 感染结果之间的关系。我们回顾性地研究了自 2021 年 8 月 1 日以来的 10 个月中在肺科重症监护室接受治疗的 288 名患者(平均年龄为 69.1 ± 12.5)。经聚合酶链反应(PCR)检测证实,所有患者均感染了由 SARS-CoV-2 引起的重症肺炎。对白细胞、淋巴细胞、中性粒细胞和 NLR 进行了追踪。174名患者(60.4%)死亡,其余患者好转出院。关于 NLR,我们发现死亡组和出院组的 NLR 值分别为 9.52 和 7.87。最后,这一指标在死亡组中上升到 15.04,而在出院组中则下降到 7.3。在统计学上,我们发现疾病的结局与 NLR 值的动态变化存在明显差异(P = 0.005)。NLR 是一种廉价而可靠的生物标志物,可以预测 Covid-19 感染的预后。
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The role of the neutrophil/lymphocyte ratio as a biomarker predicting the severity of COVID-19 infection
At the end of 2019, several cases of pneumonia provoked by an unknown causative agent were identified in China. In March 2020, the World Health Organization announced a pandemic of a new strain of Coronavirus - SARS-CoV-2. The neutrophil/lymphocyte ratio (NLR) is a biomarker that has recently become an important diagnostic and prognostic indicator in many diseases. The study‘s objective was to look for a relationship between NLR and the outcome of Covid-19 infection. Retrospectively, we studied 288 patients (mean age 69.1 ± 12.5) treated in an intensive pulmonology unit over ten months since 01.08.2021. All patients had severe pneumonia caused by SARS-CoV-2 infection, proven by a polymerase chain reaction (PCR) test. Leukocytes, lymphocytes, neutrophils, and NLR were traced. Hundred and seventy-four (60.4%) of the patients died, and the rest were discharged with improvement. Concerning NLR, we found values of 9.52 in the group of the deceased and 7.87 in those discharged, respectively, at the beginning. In the end, this indicator increased in the group of deceased to 15.04, while we found a drop to 7.3 in those discharged. A statistically significant difference was found regarding the outcome of the disease and the change in the NLR values in dynamics (p = 0.005). NLR is a cheap and reliable biomarker that can predict the outcome of Covid-19 infection.
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