Knowledge and Application of ESC/HFA Guidelines in the Management of Advanced Heart Failure
Bibliographic record
Abstract
AIMS: Management of advanced heart failure (HF) remains challenging despite specific sections in the 2021 European Society of Cardiology/Heart Failure Association (ESC/HFA) guidelines, with delays in referrals exacerbating the issue. This study aimed to evaluate the awareness and implementation of these guidelines among cardiologists and identify barriers to effective referral. METHODS AND RESULTS: From June to October 2023, an online survey was disseminated through the ESC mailing list, targeting cardiologists across Europe. The survey investigated four areas: guideline awareness, healthcare network organization, clinical case management, and perceptions of mechanical circulatory support (MCS) outcomes. Respondents were categorized into heart failure cardiologists (HFCs), general cardiologists (GCs), and other participants (OPs). Among 497 respondents, 25% were heart HFCs, 40% were GCs, and 35% were OPs. A total of 84% of HFCs reported a high level of guideline knowledge, compared to 57% of GCs and 62% of OPs (p < 0.001). Additionally, 76% of HFCs 'regularly or always' used ESC/HFA criteria to identify advanced HF, compared to 44% of GCs and 48% of OPs (p < 0.001). Correct responses regarding the recommendation class for heart transplantation were 84%, 55%, and 60% (p < 0.0001), and for MCS as a bridge to transplantation, 69%, 65%, and 55% (p = 0.018) among HFCs, GCs, and OPs, respectively. Referring patients with severe HF to a tertiary centre team was found to be 'very difficult' or 'difficult' by 8.4% of HFCs, 19.6% of GCs, and 18.2% of OPs (p = 0.0005). CONCLUSION: The study highlights significant disparities in knowledge and application of advanced HF guidelines among cardiologists, revealing an opportunity for educational initiatives. The difficulty in referring patients to tertiary centres underscores the need to improve the referral pathway for advanced HF patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".