Determinants and Prognostic Impact of Heart Failure Complicating Acute Coronary Syndromes
Bibliographic record
Abstract
BACKGROUND: Few data are available on the impact of heart failure (HF) across all types of acute coronary syndromes (ACS). METHODS AND RESULTS: The Global Registry of Acute Coronary Events (GRACE) is a prospective study of patients hospitalized with ACS. Data from 16 166 patients were analyzed: 13 707 patients without prior HF or cardiogenic shock at presentation were identified. Of these, 1778 (13%) had an admission diagnosis of HF (Killip class II or III). HF on admission was associated with a marked increase in mortality rates during hospitalization (12.0% versus 2.9% [with versus without HF], P<0.0001) and at 6 months after discharge (8.5% versus 2.8%, P<0.0001). Of note, HF increased mortality rates in patients with unstable angina (defined as ACS with normal biochemical markers of necrosis; mortality rates: 6.7% with versus 1.6% without HF at admission, P<0.0001). By logistic regression analysis, admission HF was an independent predictor of hospital death (odds ratio, 2.2; P<0.0001). Admission HF was associated with longer hospital stay and higher readmission rates. Patients with HF had lower rates of catheterization and percutaneous cardiac intervention, and fewer received beta-blockers and statins. Hospital development of HF (versus HF on presentation) was associated with an even higher in-hospital mortality rate (17.8% versus 12.0%, P<0.0001). In patients with HF, in-hospital revascularization was associated with lower 6-month death rates (14.0% versus 23.7%, P<0.0001; adjusted hazard ratio, 0.5; 95% CI, 0.37 to 0.68, P<0.0001). CONCLUSIONS: In this observational registry, heart failure was associated with reduced hospital and 6-month survival across all ACS subsets, including patients with normal markers of necrosis. More aggressive treatment of these patients may be warranted to improve prognosis.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".