Factors Associated With the Development of Heart Failure Following Acute Coronary Syndrome: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Acute coronary syndrome (ACS) remains a major global health burden, encompassing a spectrum of conditions from unstable angina to acute myocardial infarction. Despite advancements in early detection and management, ACS is often complicated by the development of heart failure. This systematic review and meta-analysis aimed to identify factors associated with the development of heart failure following acute coronary syndrome. A comprehensive search was conducted across PubMed, Embase, Cochrane Library, and Web of Science from January 2018 to November 2024. Studies evaluating clinical or biochemical predictors of heart failure development in adult patients with acute coronary syndrome were included. Out of the initially identified studies, nine studies met the inclusion criteria. The Newcastle-Ottawa Scale was used to assess the quality of included studies, with most studies demonstrating high quality. The pooled analysis revealed that older age, female sex, diabetes, hypertension, chronic obstructive pulmonary disease, atrial fibrillation, multivessel coronary disease, and reduced left ventricular ejection fraction were significant predictors of heart failure development following acute coronary syndrome. The presence of atrial fibrillation emerged as the strongest predictor, followed by reduced left ventricular ejection fraction and chronic obstructive pulmonary disease. While complete revascularization showed a protective trend, this association did not reach statistical significance. The findings were limited by the predominantly retrospective nature of included studies and heterogeneity in the assessment of certain risk factors. Future research should focus on prospective studies with larger cohorts and comprehensive evaluation of additional factors such as treatment delays and revascularization strategies. Understanding these predictors can facilitate early risk stratification and guide targeted interventions, potentially improving outcomes for patients with acute coronary syndrome.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".