Moatar Alexandra Ioana, Chis Aimee Rodica, Nitusca Diana, Oancea Cristian, Marian Catalin, Sirbu Ioan-Ovidiu
Doctoral School, University of Medicine and Pharmacy "Victor Babes", 300041 Timisoara, Romania.
Department of Biochemistry, University of Medicine and Pharmacy "Victor Babes", 300041 Timisoara, Romania.
Biomedicines. 2024 Feb 5;12(2):373. doi: 10.3390/biomedicines12020373.
(1) Background: Heparin-Binding Epidermal Growth Factor-like Growth Factor (HB-EGF) is involved in wound healing, cardiac hypertrophy, and heart development processes. Recently, circulant HB-EGF was reported upregulated in severely hospitalized COVID-19 patients. However, the clinical correlations of HB-EGF plasma levels with COVID-19 patients' characteristics have not been defined yet. In this study, we assessed the plasma HB-EGF correlations with the clinical and paraclinical patients' data, evaluated its predictive clinical value, and built a risk prediction model for severe COVID-19 cases based on the resulting significant prognostic markers. (2) Methods: Our retrospective study enrolled 75 COVID-19 patients and 17 control cases from May 2020 to September 2020. We quantified plasma HB-EGF levels using the sandwich ELISA technique. Correlations between HB-EGF plasma levels with clinical and paraclinical patients' data were calculated using two-tailed Spearman and Point-Biserial tests. Significantly upregulated parameters for severe COVID-19 cases were identified and selected to build a multivariate logistic regression prediction model. The clinical significance of the prediction model was assessed by risk prediction nomogram and decision curve analyses. (3) Results: HB-EGF plasma levels were significantly higher in the severe COVID-19 subgroup compared to the controls ( = 0.004) and moderate cases ( = 0.037). In the severe COVID-19 group, HB-EGF correlated with age ( = 0.028), pulse ( = 0.016), dyspnea ( = 0.014) and prothrombin time (PT) ( = 0.04). The multivariate risk prediction model built on seven identified risk parameters (age = 0.043, HB-EGF = 0.0374, Fibrinogen = 0.009, PT = 0.008, Creatinine = 0.026, D-Dimers = 0.024 and delta miR-195 < 0.0001) identifies severe COVID-19 with AUC = 0.9556 ( < 0.0001). The decision curve analysis revealed that the nomogram model is clinically relevant throughout a wide threshold probability range. (4) Conclusions: Upregulated HB-EGF plasma levels might serve as a prognostic factor for severe COVID-19 and help build a reliable risk prediction nomogram that improves the identification of high-risk patients at an early stage of COVID-19.
（1）背景：肝素结合表皮生长因子样生长因子（HB-EGF）参与伤口愈合、心脏肥大和心脏发育过程。最近，有报道称重症住院的COVID-19患者循环中的HB-EGF上调。然而，HB-EGF血浆水平与COVID-19患者特征的临床相关性尚未明确。在本研究中，我们评估了血浆HB-EGF与患者临床及辅助检查数据的相关性，评估其临床预测价值，并基于所得出的显著预后标志物建立了重症COVID-19病例的风险预测模型。（2）方法：我们的回顾性研究纳入了2020年5月至2020年9月期间的75例COVID-19患者和17例对照病例。我们使用夹心ELISA技术定量血浆HB-EGF水平。采用双尾Spearman检验和点二列相关检验计算HB-EGF血浆水平与患者临床及辅助检查数据之间的相关性。确定并选择重症COVID-19病例中显著上调的参数，以建立多因素逻辑回归预测模型。通过风险预测列线图和决策曲线分析评估预测模型的临床意义。（3）结果：与对照组（P = 0.004）和中度病例组（P = 0.037）相比，重症COVID-19亚组的血浆HB-EGF水平显著更高。在重症COVID-19组中，HB-EGF与年龄（P = 0.028）、脉搏（P = 0.016）、呼吸困难（P = 0.014）和凝血酶原时间（PT）（P = 0.04）相关。基于七个确定的风险参数（年龄P = 0.043、HB-EGF P = 0.0374、纤维蛋白原P = 0.009、PT P = 0.008、肌酐P = 0.026、D-二聚体P = 0.024和δmiR-195 P < 0.0001）建立的多因素风险预测模型识别重症COVID-19的曲线下面积（AUC）= 0.9556（P < 0.0001）。决策曲线分析表明，列线图模型在较宽的阈值概率范围内具有临床相关性。（4）结论：血浆HB-EGF水平上调可能是重症COVID-19的一个预后因素，并有助于建立一个可靠的风险预测列线图，以改善COVID-19早期高危患者的识别。
