Physician Perceptions, Attitudes, and Strategies Towards Implementing Guideline-Directed Medical Therapy in Heart Failure with Reduced Ejection Fraction. A Survey of the Heart Failure Association of the ESC and the ESC Council for Cardiology Practice
Bibliographic record
Abstract
AIMS: Recent guidelines recommend four core drug classes (renin-angiotensin system inhibitor/angiotensin receptor-neprilysin inhibitor [RASi/ARNi], beta-blocker, mineralocorticoid receptor antagonist [MRA], and sodium-glucose cotransporter 2 inhibitor [SGLT2i]) for the pharmacological management of heart failure (HF) with reduced ejection fraction (HFrEF). We assessed physicians' perceived (i) comfort with implementing the recent HFrEF guideline recommendations; (ii) status of guideline-directed medical therapy (GDMT) implementation; (iii) use of different GDMT sequencing strategies; and (iv) barriers and strategies for achieving implementation. METHODS AND RESULTS: A 26-question survey was disseminated via bulletin, e-mail and social channels directed to physicians with an interest in HF. Of 432 respondents representing 91 countries, 36% were female, 52% were aged <50 years, and 90% mainly practiced in cardiology (30% HF). Overall comfort with implementing quadruple therapy was high (87%). Only 12% estimated that >90% of patients with HFrEF without contraindications received quadruple therapy. The time required to initiate quadruple therapy was estimated at 1-2 weeks by 34% of respondents, 1 month by 36%, 3 months by 24%, and ≥6 months by 6%. The average respondent favoured traditional drug sequencing strategies (RASi/ARNi with/followed by beta-blocker, and then MRA with/followed by SGLT2i) over simultaneous initiation or SGLT2i-first sequences. The most frequently perceived clinical barriers to implementation were hypotension (70%), creatinine increase (47%), hyperkalaemia (45%) and patient adherence (42%). CONCLUSIONS: Although comfort with implementing all four core drug classes in patients with HFrEF was high among physicians, a majority estimated implementation of GDMT in HFrEF to be low. We identified several important perceived clinical and non-clinical barriers that can be targeted to improve implementation.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".