Congestive heart failure complicating non-ST segment elevation acute coronary syndrome: incidence, predictors, and clinical outcomes
Bibliographic record
Abstract
There are limited data regarding the incidence and clinical significance of congestive heart failure (CHF) in patients with non-ST segment elevation acute coronary syndromes (ACS). The objectives of this study were to examine the incidence, predictors, and clinical outcomes in patients with ACS without ST elevation who develop CHF. We studied patients with unstable angina or non-ST segment elevation myocardial infarction (NSTEMI) randomized to hirudin or unfractionated heparin in the Organisation to Assess Strategies for Ischemic Syndromes (OASIS-2) trial. The diagnosis of CHF was based on a combination of clinical and radiographic features. Patients were followed for 6 months. Of 10 141 randomized patients, 501 (4.9%) developed CHF within the first week and 643 (6.3%) during 6 months of followup. Independent predictors for the development of CHF were older age, female sex, diabetes, prior MI, prior CHF, and NSTEMI at presentation. Compared with patients who did not develop CHF, patients who developed CHF were at increased risk of death (odds ratio (OR) 3.4, 95% CI 2.7-4.3), new MI (OR 2.8, 95% CI 2.2-3.6), and the need for intra-aortic balloon pump insertion (OR 5.4, 95% CI 3.5-8.4) at 7 days and 6 months. There was no increase in use of cardiac catheterization (OR 0.8, 95% CI 0.7-1.0) or revascularization (OR 0.9, 95% CI 0.7-1.1) in patients who developed CHF. CHF is a common complication in patients presenting with non-ST segment elevation ACS and is strongly associated with adverse clinical outcomes including new MI and death. Despite this worse prognosis, patients with ACS developing CHF are less likely to be referred for invasive management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".