HB-EGF Plasmatic Level Contributes to the Development of Early Risk Prediction Nomogram for Severe COVID-19 Cases

Alexandra Ioana Moatar; Aimee Rodica Chis; Diana Nitusca; Cristian Oancea; Catalin Marian; Ioan-Ovidiu Sirbu

doi:10.3390/biomedicines12020373

HB-EGF Plasmatic Level Contributes to the Development of Early Risk Prediction Nomogram for Severe COVID-19 Cases

Biomedicines. 2024 Feb 5;12(2):373. doi: 10.3390/biomedicines12020373.

Authors

Alexandra Ioana Moatar^{1

2

3}, Aimee Rodica Chis^{2

3}, Diana Nitusca^{1

2

3}, Cristian Oancea⁴, Catalin Marian^{2

3}, Ioan-Ovidiu Sirbu^{2

3}

Affiliations

¹ Doctoral School, University of Medicine and Pharmacy "Victor Babes", 300041 Timisoara, Romania.
² Department of Biochemistry, University of Medicine and Pharmacy "Victor Babes", 300041 Timisoara, Romania.
³ Center for Complex Network Science, University of Medicine and Pharmacy "Victor Babes", 300041 Timisoara, Romania.
⁴ Department of Pneumology, University of Medicine and Pharmacy "Victor Babes", 300041 Timisoara, Romania.

Abstract

(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 (p = 0.004) and moderate cases (p = 0.037). In the severe COVID-19 group, HB-EGF correlated with age (p = 0.028), pulse (p = 0.016), dyspnea (p = 0.014) and prothrombin time (PT) (p = 0.04). The multivariate risk prediction model built on seven identified risk parameters (age p = 0.043, HB-EGF p = 0.0374, Fibrinogen p = 0.009, PT p = 0.008, Creatinine p = 0.026, D-Dimers p = 0.024 and delta miR-195 p < 0.0001) identifies severe COVID-19 with AUC = 0.9556 (p < 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.

Keywords: COVID-19 severe; HB-EGF; clinical correlations; prediction marker.

Grants and funding

This work was supported by the Romanian National Council for Higher Education Funding, CNFIS (Grant No. CNFIS-FDI-2021-0484), and the Romanian Ministry of Education and Research, UEFISCDI (Grant No. PN-III-P2-2.1-SOL-2020-0142).