Identifying and Overcoming Barriers to Referral in Advanced Heart Failure. A Scientific Statement of the Heart Failure Association (HFA) of the ESC
Bibliographic record
Abstract
The identification of patients with advanced heart failure (HF) remains challenging, often leading to delayed referrals and suboptimal use of advanced therapies such as long-term mechanical circulatory support (MCS) or heart transplantation (HT). This delay contributes to worse outcomes and missed opportunities for timely intervention. Many eligible patients are not recognized early enough in their clinical trajectory, either due to the complexity of the condition, overlapping HF phenotypes, or limited awareness of referral criteria among non-specialist clinicians. In this context, the aim of this scientific statement from the Heart Failure Association (HFA) of the ESC is to systematically identify and address the multifaceted barriers that hinder early recognition and referral for advanced HF care. These barriers span across different stakeholders-patients, caregivers, referring physicians, HF specialists, the academic community, and health authorities. The document proposes practical, stakeholder-specific solutions to improve awareness, standardize referral criteria, integrate digital decision-support tools, and structure care networks. Ultimately, the goal is to enable earlier access to specialized evaluation, ensure equitable use of HT and MCS when appropriate, and improve both survival and quality of life for patients living with advanced HF.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".