Oxidative stress markers-driven prognostic model to predict post-discharge mortality in heart failure with reduced ejection fraction

Imen Gtif; Rania Abdelhedi; Wael Ouarda; Fériel Bouzid; Salma Charfeddine; Fatma Zouari; Leila Abid; Ahmed Rebai; Najla Kharrat

doi:10.3389/fcvm.2022.1017673

Oxidative stress markers-driven prognostic model to predict post-discharge mortality in heart failure with reduced ejection fraction

Front Cardiovasc Med. 2022 Nov 7:9:1017673. doi: 10.3389/fcvm.2022.1017673. eCollection 2022.

Authors

Imen Gtif¹, Rania Abdelhedi¹, Wael Ouarda², Fériel Bouzid¹, Salma Charfeddine³, Fatma Zouari³, Leila Abid³, Ahmed Rebai¹, Najla Kharrat¹

Affiliations

¹ Laboratory of Screening Cellular and Molecular Process, Centre of Biotechnology of Sfax, University of Sfax, Sfax, Tunisia.
² Digital Research Center of Sfax, University of Sfax, Sfax, Tunisia.
³ Unit of Cardiology in Hospital of Hedi Chaker, Faculty of Medicine, University of Sfax, Sfax, Tunisia.

Abstract

Background: Current predictive models based on biomarkers reflective of different pathways of heart failure with reduced ejection fraction (HFrEF) pathogenesis constitute a useful tool for predicting death risk among HFrEF patients. The purpose of the study was to develop a new predictive model for post-discharge mortality risk among HFrEF patients, based on a combination of clinical patients' characteristics, N-terminal pro-B-type Natriuretic peptide (NT-proBNP) and oxidative stress markers as a potentially valuable tool for routine clinical practice.

Methods: 116 patients with stable HFrEF were recruited in a prospective single-center study. Plasma levels of NT-proBNP and oxidative stress markers [superoxide dismutase (SOD), glutathione peroxidase (GPX), uric acid (UA), total bilirubin (TB), gamma-glutamyl transferase (GGT) and total antioxidant capacity (TAC)] were measured in the stable predischarge condition. Generalized linear model (GLM), random forest and extreme gradient boosting models were developed to predict post-discharge mortality risk using clinical and laboratory data. Through comprehensive evaluation, the most performant model was selected.

Results: During a median follow-up of 525 days (7-930), 33 (28%) patients died. Among the three created models, the GLM presented the best performance for post-discharge death prediction in HFrEF. The predictors included in the GLM model were age, female sex, beta blockers, NT-proBNP, left ventricular ejection fraction (LVEF), TAC levels, admission systolic blood pressure (SBP), angiotensin-converting enzyme inhibitors/angiotensin receptor II blockers (ACEI/ARBs) and UA levels. Our model had a good discriminatory power for post-discharge mortality [The area under the curve (AUC) = 74.5%]. Based on the retained model, an online calculator was developed to allow the identification of patients with heightened post-discharge death risk.

Conclusion: In conclusion, we created a new and simple tool that may allow the identification of patients at heightened post-discharge mortality risk and could assist the treatment decision-making.

Keywords: heart failure; models; mortality; oxidative stress; prediction.