Reflecting on the advancements of HFrEF therapies over the last two decades and predicting what is yet to come
Bibliographic record
Abstract
What was once considered a topic best avoided, managing heart failure with reduced ejection fraction (HFrEF) has become the focus of many drug and device therapies. While the four pillars of guideline-directed medical therapies have successfully reduced heart failure hospitalizations, and some have even impacted cardiovascular mortality in randomized controlled trials (RCTs), patient-reported outcomes have emerged as important endpoints that merit greater emphasis in future studies. The prospect of an oral inotrope seems more probable now as targets for drug therapies have moved from neurohormonal modulation to intracellular mechanisms and direct cardiac myosin stimulation. While we have come a long way in safely providing durable mechanical circulatory support to patients with advanced HFrEF, several percutaneous device therapies have emerged, and many are under investigation. Biomarkers have shown promise in not only improving our ability to diagnose incident heart failure but also our potential to implicate specific pathophysiological pathways. The once-forgotten concept of discordance between pressure and volume, the forgotten splanchnic venous and lymphatic compartments, have all emerged as promising targets for diagnosing and treating heart failure in the not-so-distant future. The increase in heart failure-related cardiogenic shock (CS) has revived interest in defining optimal perfusion targets and designing RCTs in CS. Rapid developments in remote monitoring, telemedicine, and artificial intelligence promise to change the face of heart failure care. In this state-of-the-art review, we reminisce about the past, highlight the present, and predict what might be the future of HFrEF therapies.
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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 | 0.002 | 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 | 0.001 | 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) | 0.002 | 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 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".