Treatment outcome and associated factors of acute heart failure in East Africa, an Ethiopian perspective: A systematic review and meta-analysis.
Bibliographic record
Abstract
Acute heart failure is a major global health issue, contributing to significant morbidity, mortality, and healthcare costs in Sub-Saharan Africa, including Ethiopia. Despite its burden, comprehensive data on acute heart failure in East Africa remains scarce. This systematic review and meta-analysis aimed to synthesize existing evidence on the treatment outcome of acute heart failure and associated factors in East Africa with a focus on Ethiopia. Relevant studies were searched in major databases, including PubMed/Medline, Hinari, Science Direct, EMBASE, Scopus, AJOL, Cochrane Library, and local sources from July 5–25, 2024. Both published and unpublished studies in English were included without restrictions on publication date, following PRISMA-2020 protocols. Data quality was assessed using the Newcastle-Ottawa Scale, and meta-analysis was conducted using Stata version 18. A total of 9 articles involving 1107 participants were included. The pooled mortality of acute heart failure was 16.36 % (95 % CI: 12.39, 20.33) with heterogeneity (I 2) value of 85.86 %. Increased blood urea nitrogen (BUN), smoking, and hypotension were the factors significantly associated with the pooled mortality rate of acute heart failure. This review reveals a high mortality of acute heart failure among hospitalized patients in Ethiopia. Factors such as increased blood urea nitrogen (BUN), smoking, and hypotension contribute to death due to acute heart failure. Therefore, it is necessary to reduce the burden of acute heart failure and improve patient survival in Ethiopia by addressing the identified predictors of poor outcomes and integrating global best practices into local healthcare systems. • This systematic review and meta-analysis reveal a high prevalence of adverse outcomes among acute heart failure patients in Ethiopia, driven by factors such as elevated BUN, smoking and hypotension. • These findings align with broader trends observed in sub-Saharan Africa and emphasizes the need for targeted interventions to improve AHF management in Ethiopia. • By addressing the identified predictors of poor outcomes and integrating global best practices into local healthcare systems, it is possible to reduce the burden of AHF and improve patient survival in Ethiopia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".