Abstract 10426: Worse Clinical Outcomes in Patients with Acute Coronary Syndrome and Prior or New Onset Heart Failure: Insights from 47,474 Patients in a Pooled Analysis of Randomized Clinical Trials
Bibliographic record
Abstract
Introduction: Heart failure (HF) frequently complicates acute coronary syndrome (ACS). Despite known associated high rates of mortality, there are only limited data regarding recurrent ischemic events and re-hospitalization for HF (HHF) in those with HF and ACS. Methods: We used a pooled dataset of four randomized clinical ACS trials (Platelet Inhibition and Patient Outcomes [PLATO], Apixaban for Prevention of Acute Ischemic Events 2 [APPRAISE-2], Thrombin Receptor Antagonist for Clinical Event Reduction in Acute Coronary Syndrome [TRA-CER], and Targeted Platelet Inhibition to Clarify the Optimal Strategy to Medically Manage Acute Coronary Syndrome [TRILOGY ACS]). We assessed the association between HF status (history of HF, de novo HF, no HF) at presentation for ACS on death, future ischemic events, and HHF at one year following hospital discharge using Cox proportional hazards analysis and estimated cumulative event rates using cumulative incidence function. Results: Of 47,474 patients presenting with ACS, 11.1% showed evidence of acute HF, 55.0% of whom had no previous history of HF. Patients with prior/chronic HF were more likely to present with acute HF than those with no previous HF (40.3% vs 6.9%). Compared to those without HF, patients with prior and de novo HF were at a significantly increased risk at one year of all-cause mortality (adjusted HR (aHR) 2.10, 95% Confidence Interval [CI] 1.80-2.45, and aHR 1.51, CI 1.18-1.93, respectively), MACE (aHR 1.50, CI 1.34-1.69, and aHR 1.40, CI 1.14-1.72), recurrent MI (aHR 1.58, CI 1.41-1.78, and aHR 1.40, CI 1.15-1.70), stroke (aHR 1.93, CI 1.67-2.32, and aHR 1.38, CI 1.09-1.75), and HHF (aHR 2.38, CI 2.09-2.71, and aHR 1.53, CI 1.24-1.88). Conclusion: Following an ACS event, both prior and de novo HF were found to be major predictors of death, recurrent ischemic events, and HHF by one year. These findings highlight the need for improved strategies to prevent and manage adverse outcomes in this high-risk population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".