Dynamic NLR and PLR in Predicting COVID-19 Severity: A Retrospective Cohort Study

动态中性粒细胞/淋巴细胞比值(NLR)和血小板/淋巴细胞比值(PLR)在预测新冠肺炎严重程度中的应用:一项回顾性队列研究

阅读:1

Abstract

INTRODUCTION: The hyperinflammation phase of severe SARS-CoV-2 is characterised by complete blood count alterations. In this context, the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) can be used as prognostic factors. We studied NLR and PLR trends at different timepoints and computed optimal cutoffs to predict four outcomes: use of continuous positive airways pressure (CPAP), intensive care unit (ICU) admission, invasive ventilation and death. METHODS: We retrospectively included all adult patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia admitted from 23 January 2020 to 18 May 2021. Analyses included non-parametric tests to study the ability of NLR and PLR to distinguish the patients' outcomes at each timepoint. Receiver operating characteristic (ROC) curves were built for NLR and PLR at each timepoint (minus discharge) to identify cutoffs to distinguish severe and non-severe disease. Their statistical significance was assessed with the chi-square test. Collection of data under the SMACORE database was approved with protocol number 20200046877. RESULTS: We included 2169 patients. NLR and PLR were higher in severe coronavirus disease 2019 (COVID-19). Both ratios were able to distinguish the outcomes at each timepoint. For NLR, the areas under the receiver operating characteristic curve (AUROC) ranged between 0.59 and 0.81, and for PLR between 0.53 and 0.67. From each ROC curve we computed an optimal cutoff value. CONCLUSION: NLR and PLR cutoffs are able to distinguish severity grades and mortality at different timepoints during the course of disease, and, as such, they allow a tailored approach. Future prospects include validating our cutoffs in a prospective cohort and comparing their performance against other COVID-19 scores.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。