Long term prognosis of heart failure after acute coronary syndromes without ST elevation
Bibliographic record
Abstract
BACKGROUND: Information about long term outcomes of patients with acute coronary syndromes (ACS) who have clinically diagnosed heart failure is scarce. METHODS: In a UK registry, this study evaluated patients with non-ST elevation ACS, recording treatment, and clinical outcomes for six months. In a subgroup, a four year mortality follow up was performed to estimate the impact of the clinical diagnosis of heart failure on survival. RESULTS: Of 1046 patients, 139 (13%) had a history of clinically diagnosed heart failure. At discharge, ACE inhibitors were prescribed for 58% and 28%, of those with and without a history of heart failure respectively (p<0.001). Rates of angiography, percutaneous intervention, and coronary artery bypass graft were 17.3% and 29.2% (p = 0.003), 5.0% and 8.4% (p = 0.17), and 5.0% and 7.5% (p = 0.3) for these groups respectively. Death or new myocardial infarction at six months occurred in 22% and 10% (p<0.001) and at four years death occurred in 60% and 20% of these groups respectively (p<0.001). In a multivariate analysis prior heart failure carried an odds ratio of 2.0 (p = 0.001) for death or myocardial infarction at six months and 2.4 (p<0.001) for death over four years. New heart failure was associated with an increased risk of death at six months (20% compared with 5%, p<0.001). CONCLUSION: A clinical history of heart failure carries a substantial risk of death in patients admitted with ACS without ST elevation. Nearly 60% of those with prior heart failure are dead after four years. After adjustment for confounding factors, prior heart failure more than doubles the risk compared with those with no history.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".