University of Sharjah, Research Institute of Medical and Health Sciences, Sharjah, United Arab Emirates.
Department of Medicinal Chemistry University of Sharjah, Department of Medicinal Chemistry, College of Pharmacy, Sharjah, United Arab Emirates.
PLoS One. 2023 Aug 10;18(8):e0289738. doi: 10.1371/journal.pone.0289738. eCollection 2023.
Recently, numerous studies have reported on different predictive models of disease severity in COVID-19 patients. Herein, we propose a highly predictive model of disease severity by integrating routine laboratory findings and plasma metabolites including cytosine as a potential biomarker of COVID-19 disease severity. One model was developed and internally validated on the basis of ROC-AUC values. The predictive accuracy of the model was 0.996 (95% CI: 0.989 to 1.000) with an optimal cut-off risk score of 3 from among 6 biomarkers including five lab findings (D-dimer, ferritin, neutrophil counts, Hp, and sTfR) and one metabolite (cytosine). The model is of high predictive power, needs a small number of variables that can be acquired at minimal cost and effort, and can be applied independent of non-empirical clinical data. The metabolomics profiling data and the modeling work stemming from it, as presented here, could further explain the cause of COVID-19 disease prognosis and patient management.
最近，许多研究报告了 COVID-19 患者疾病严重程度的不同预测模型。在此，我们提出了一种通过整合常规实验室发现和血浆代谢物（包括胞嘧啶作为 COVID-19 疾病严重程度的潜在生物标志物）的高度预测疾病严重程度的模型。该模型基于 ROC-AUC 值进行了开发和内部验证。该模型的预测准确性为 0.996（95%CI：0.989 至 1.000），最佳截断风险评分来自 6 个生物标志物中的 3 个，包括 5 个实验室发现（D-二聚体、铁蛋白、中性粒细胞计数、Hp 和 sTfR）和一个代谢物（胞嘧啶）。该模型具有较高的预测能力，仅需要少量可以以最小成本和努力获得的变量，并且可以独立于非经验性临床数据进行应用。此处呈现的代谢组学分析数据和建模工作可以进一步解释 COVID-19 疾病预后和患者管理的原因。
