Optimization of GDMT for patients with heart failure and reduced ejection fraction: can physiological and biological barriers explain the gaps in adherence to heart failure guidelines?
Bibliographic record
Abstract
Heart failure is a growing epidemic with high mortality rates and recurrent hospital admissions that creates a burden on affected individuals, their caregivers and the whole healthcare system. Throughout the years, many randomized trials have established the effectiveness of several pharmacological therapies and electrophysiological devices to reduce hospitalizations and improve quality of life and survival, mostly for patients with heart failure with reduced ejection fraction (HFrEF). These studies led to the publication of national societies' recommendations to guide clinicians in the management of HFrEF. Yet, many reports have shown significant care gaps in adherence to these recommendations in clinical practice, highlighting suboptimal use and/or dosing of evidence-based therapies. Adherence to guidelines has been shown to be associated with the best prognosis in HFrEF, with patients presenting with intolerances or contraindications having the highest risk of events; however, it remains unclear whether this association is causal or merely a marker of more advanced disease. Furthermore, individual characteristics may limit the possibility of reaching the targeted dosage of specific agents. Herein, we provide a comprehensive overview of clinicians' adherence to heart failure guidelines in a specialized real-life setting, particularly regarding use and optimization of guideline-derived medical therapies, as well as the implementation of more recent agents such as sacubitril/valsartan and SGLT2 inhibitors. We seek potential explanations for suboptimal treatment and its impact on patient outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".