Institute of Pneumophtisiology "Marius Nasta", 050159 Bucharest, Romania.
Department of Medical Genetics, University of Medicine and Pharmacy "Carol Davila", 020032 Bucharest, Romania.
Viruses. 2022 May 31;14(6):1201. doi: 10.3390/v14061201.
Our study objective was to construct models using 20 routine laboratory parameters on admission to predict disease severity and mortality risk in a group of 254 hospitalized COVID-19 patients. Considering the influence of confounding factors in this single-center study, we also retrospectively assessed the correlations between the risk of death and the routine laboratory parameters within individual comorbidity subgroups. In multivariate regression models and by ROC curve analysis, a model of three routine laboratory parameters (AUC 0.85; 95% CI: 0.79-0.91) and a model of six laboratory factors (AUC 0.86; 95% CI: 0.81-0.91) were able to predict severity and mortality of COVID-19, respectively, compared with any other individual parameter. Hierarchical cluster analysis showed that inflammatory laboratory markers grouped together in three distinct clusters including positive correlations: WBC with NEU, NEU with neutrophil-to-lymphocyte ratio (NLR), NEU with systemic immune-inflammation index (SII), NLR with SII and platelet-to-lymphocyte ratio (PLR) with SII. When analyzing the routine laboratory parameters in the subgroups of comorbidities, the risk of death was associated with a common set of laboratory markers of systemic inflammation. Our results have shown that a panel of several routine laboratory parameters recorded on admission could be helpful for early evaluation of the risk of disease severity and mortality in COVID-19 patients. Inflammatory markers for mortality risk were similar in the subgroups of comorbidities, suggesting the limited effect of confounding factors in predicting COVID-19 mortality at admission.
我们的研究目的是使用入院时的 20 项常规实验室参数构建模型,以预测 254 名住院 COVID-19 患者的疾病严重程度和死亡风险。考虑到这项单中心研究中的混杂因素的影响,我们还回顾性评估了个体合并症亚组中死亡风险与常规实验室参数之间的相关性。在多变量回归模型和 ROC 曲线分析中,三个常规实验室参数模型(AUC 0.85;95%CI:0.79-0.91)和六个实验室因素模型(AUC 0.86;95%CI:0.81-0.91)能够分别预测 COVID-19 的严重程度和死亡率,与任何其他单个参数相比。层次聚类分析显示,炎症性实验室标志物聚集在一起形成三个不同的簇,包括正相关:WBC 与 NEU、NEU 与中性粒细胞与淋巴细胞比值(NLR)、NEU 与全身免疫炎症指数(SII)、NLR 与 SII 和血小板与淋巴细胞比值(PLR)与 SII。在分析合并症亚组中的常规实验室参数时,死亡风险与系统性炎症的一组常见实验室标志物相关。我们的研究结果表明,入院时记录的一组常规实验室参数可以有助于早期评估 COVID-19 患者的疾病严重程度和死亡风险。在合并症亚组中,死亡风险的炎症标志物相似,这表明混杂因素在预测入院时 COVID-19 死亡率方面的作用有限。