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

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

Imen Gtif  1 , Rania Abdelhedi  1 , Wael Ouarda  2 , Fériel Bouzid  1 , Salma Charfeddine  3 , Fatma Zouari  3 , Leila Abid  3 , Ahmed Rebai  1 , Najla Kharrat  1
Affiliations

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

Imen Gtif et al. Front Cardiovasc Med. .

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.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Kaplan-Meier event-free survival curves for post-discharge mortality relative to plasma levels of UA (A), TAC (B) and NT-proBNP (C) above or below cut-off values.
FIGURE 2
FIGURE 2
Multivariable Cox regression for post-discharge mortality prediction.
FIGURE 3
FIGURE 3
SHAP plots for the ML models in predicting post-discharge mortality using (A) RF and (B) XGBoost. In each variable importance row, all patients’ attribution to mortality risk was plotted using different color dots. The red dots represent the highest risk of death.
FIGURE 4
FIGURE 4
Kaplan–Meier survival analysis between the high-and low-risk groups.
FIGURE 5
FIGURE 5
Post-discharge mortality risk calculator.
See this image and copyright information in PMC

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