Guideline-Directed Medical Therapy in Severe Heart Failure with Reduced Ejection Fraction: An Analysis from the HELP-HF Registry
Bibliographic record
Abstract
AIM: Persistent symptoms despite guideline-directed medical therapy (GDMT) and poor tolerance of GDMT are hallmarks of patients with advanced heart failure (HF) with reduced ejection fraction (HFrEF). However, real-world data on GDMT use, dose, and prognostic implications are lacking. METHODS AND RESULTS: We included 699 consecutive patients with HFrEF and at least one 'I NEED HELP' marker for advanced HF enrolled in a multicentre registry. Beta-blockers (BB) were administered to 574 (82%) patients, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers or angiotensin receptor-neprilysin inhibitors (ACEi/ARB/ARNI) were administered to 381 (55%) patients and 416 (60%) received mineralocorticoid receptor antagonists (MRA). Overall, ≥50% of target doses were reached in 41%, 22%, and 56% of the patients on BB, ACEi/ARB/ARNI and MRA, respectively. Hypotension, bradycardia, kidney dysfunction and hyperkalaemia were the main causes of underprescription and/or underdosing, but up to a half of the patients did not receive target doses for unknown causes (51%, 41%, and 55% for BB, ACEi/ARB/ARNI and MRA, respectively). The proportions of patients receiving BB and ACEi/ARB/ARNI were lower among those fulfilling the 2018 HFA-ESC criteria for advanced HF. Treatment with BB and ACEi/ARB/ARNI were associated with a lower risk of death or HF hospitalizations (adjusted hazard ratio [HR] 0.63, 95% confidence interval [CI] 0.48-0.84, and HR 0.74, 95% CI 0.58-0.95, respectively). CONCLUSIONS: In a large, real-world, contemporary cohort of patients with severe HFrEF, with at least one marker for advanced HF, prescription and uptitration of GDMT remained limited. A significant proportion of patients were undertreated due to unknown reasons suggesting a potential role of clinical inertia either by the prescribing healthcare professional or by the patient. Treatment with BB and ACEi/ARB/ARNI was associated with lower mortality/morbidity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".