Acute Coronary Syndromes and Acute Heart Failure: A Diagnostic Dilemma and High-Risk Combination. A Statement from the Acute Heart Failure Committee of the Heart Failure Association of the European Society of Cardiology
Bibliographic record
Abstract
Acute coronary syndrome is a precipitant of acute heart failure in a substantial proportion of cases, and the presence of both conditions is associated with a higher risk of short-term mortality compared to acute coronary syndrome alone. The diagnosis of acute coronary syndrome in the setting of acute heart failure can be challenging. Patients may present with atypical or absent chest pain, electrocardiograms can be confounded by pre-existing abnormalities, and cardiac biomarkers are frequently elevated in patients with chronic or acute heart failure, independently of acute coronary syndrome. It is important to distinguish transient or limited myocardial injury from primary myocardial infarction due to vascular events in patients presenting with acute heart failure. This paper outlines various clinical scenarios to help differentiate between these conditions and aims to provide clinicians with tools to aid in the recognition of acute coronary syndrome as a cause of acute heart failure. Interpretation of electrocardiogram and biomarker findings, and imaging techniques that may be helpful in the diagnostic work-up are described. Guidelines recommend an immediate invasive strategy for patients with acute heart failure and acute coronary syndrome, regardless of electrocardiographic or biomarker findings. Pharmacological management of patients with acute coronary syndrome and acute heart failure should follow guidelines for each of these syndromes, with priority given to time-sensitive therapies for both. Studies conducted specifically in patients with the combination of acute coronary syndrome and acute heart failure are needed to better define the management of these patients.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".