Exploratory analysis and statistical modeling of clinical data in patients with heart failure (Research, Society and Development, 2025)
Heart failure is one of the leading causes of global mortality, requiring effective strategies for prevention and treatment. This study aims to conduct an exploratory analysis of 299 patients with heart failure and investigate the influence of different clinical variables through statistical modeling. Statistical models, including linear and logistic regression, were applied to assess the impact of variables such as age, serum creatinine, serum sodium, hypertension, anemia, and smoking on ejection fraction (stroke volume) and patient mortality. The results indicated that serum sodium levels significantly influence ejection fraction, while age, serum creatinine, and ejection fraction showed a statistically significant relationship with mortality risk. The findings of this study are consistent with previous literature, emphasizing the importance of laboratory biomarkers in the prognostic evaluation of heart failure. Identifying these factors can contribute to clinical decision-making and the development of more targeted therapeutic approaches, ultimately improving survival and quality of life for affected patients.