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. 2025 Aug 7:2025:3968055.
doi: 10.1155/crp/3968055. eCollection 2025.

Time to Mortality and Predictive Factors Among Adult Heart Failure Patients: Lessons From a Resource-Limited Setting

Elsah Tegene Asefa  1 , Tamirat Godebo Woyimo  1 , Hikma Fedlu Bame  2 , Eyob Girma Abera  2
Affiliations

Time to Mortality and Predictive Factors Among Adult Heart Failure Patients: Lessons From a Resource-Limited Setting

Elsah Tegene Asefa et al. Cardiol Res Pract. .

Abstract

Background: Heart failure (HF) is a major cause of morbidity and mortality in low-resource settings like Ethiopia. This study aimed to assess time to mortality and identify key predictors among adult HF patients at Jimma Medical Center (JMC). Methods: A retrospective cohort study was conducted on 356 adult HF patients admitted to JMC between 2022 and 2023. Survival probabilities were estimated using the Kaplan-Meier method, and Cox proportional hazard regression was used to identify mortality predictors. Results: Among 356 HF patients, 15.7% (95% CI: 12.2%-19.8%) died during the study period. The median hospital stay was 11 days (IQR: 7-17), and the median age was 55 years (IQR: 38-65). Key predictors of higher mortality included hypertension (AHR: 4.6, 95% CI: 1.88-11.61, p < 0.001), pneumonia (AHR: 4.3, 95% CI: 1.15-15.78, p = 0.031), anemia (AHR: 3.3, 95% CI: 1.17-9.06, p = 0.023), acute myocardial infarction (AMI) (AHR: 4.4, 95% CI: 1.9-10.09, p < 0.001), and hyponatremia (AHR: 2.9, 95% CI: 1.44-5.99, p = 0.003). Each unit increase in systolic blood pressure (SBP) and diastolic blood pressure (DBP) was linked to a 7% and 4% lower mortality risk, respectively (p = 0.035). A higher pulse rate was associated with a 4% increased mortality risk. Patients with heart failure with reduced ejection fraction (HFrEF) had a six-fold higher mortality risk compared to those with preserved ejection fraction (HFpEF) (AHR: 6.1, 95% CI: 1.79-24.4, p = 0.008). Conclusion: This study identifies key mortality predictors for HF patients in a resource-limited setting, including hypertension, pneumonia, anemia, AMI, and hyponatremia. The findings emphasize the need for targeted interventions, improved management strategies, and policies to reduce HF mortality in low-resource environments. Further research is needed to refine these findings and enhance care for HF patients in such settings.

Keywords: Cox regression; Ethiopia; Kaplan–Meier estimator; heart failure; mortality.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Kaplan–Meier survival curves for patients with heart failure, stratified by pneumonia and duration of hospitalization at Jimma Medical Center, Ethiopia, 2024.
Figure 2
Figure 2
Kaplan–Meier survival curves for patients with heart failure, stratified by anemia and duration of hospitalization at Jimma Medical Center, Ethiopia, 2024.
Figure 3
Figure 3
Kaplan–Meier survival curves for patients with heart failure, stratified by type of heart failure and duration of hospitalization at Jimma Medical Center, Ethiopia, 2024.
Figure 4
Figure 4
Kaplan–Meier survival curves for patients with heart failure, stratified by stage of heart failure and duration of hospitalization at Jimma Medical Center, Ethiopia, 2024.
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