Advanced Heart Failure: A Position Statement of the Heart Failure Association of the European Society of Cardiology
Bibliographic record
Abstract
This article updates the Heart Failure Association of the European Society of Cardiology (ESC) 2007 classification of advanced heart failure and describes new diagnostic and treatment options for these patients. Recognizing the patient with advanced heart failure is critical to facilitate timely referral to advanced heart failure centres. Unplanned visits for heart failure decompensation, malignant arrhythmias, co-morbidities, and the 2016 ESC guidelines criteria for the diagnosis of heart failure with preserved ejection fraction are included in this updated definition. Standard treatment is, by definition, insufficient in these patients. Inotropic therapy may be used as a bridge strategy, but it is only a palliative measure when used on its own, because of the lack of outcomes data. Major progress has occurred with short-term mechanical circulatory support devices for immediate management of cardiogenic shock and long-term mechanical circulatory support for either a bridge to transplantation or as destination therapy. Heart transplantation remains the treatment of choice for patients without contraindications. Some patients will not be candidates for advanced heart failure therapies. For these patients, who are often elderly with multiple co-morbidities, management of advanced heart failure to reduce symptoms and improve quality of life should be emphasized. Robust evidence from prospective studies is lacking for most therapies for advanced heart failure. There is an urgent need to develop evidence-based treatment algorithms to prolong life when possible and in accordance with patient preferences, increase life quality, and reduce the burden of hospitalization in this vulnerable patient 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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".