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

Author:

Moatar Alexandra Ioana123,Chis Aimee Rodica23,Nitusca Diana123ORCID,Oancea Cristian4ORCID,Marian Catalin23ORCID,Sirbu Ioan-Ovidiu23ORCID

Affiliation:

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.

Funder

Romanian National Council for Higher Education Funding, CNFIS

Romanian Ministry of Education and Research, UEFISCDI

Publisher

MDPI AG

Subject

General Biochemistry, Genetics and Molecular Biology,Medicine (miscellaneous)

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