Clinical Management and Therapeutic Optimization of Patients with Heart Failure with Reduced Ejection Fraction and Low Blood Pressure. A Clinical Consensus Statement of the Heart Failure Association (HFA) of the ESC
Bibliographic record
Abstract
Despite major advancements in heart failure (HF) management and guideline recommendations over the past two decades, real-world evidence highlights suboptimal implementation of guideline-directed medical therapy (GDMT) for HF with reduced ejection fraction (HFrEF). Low blood pressure (BP) is common in HFrEF patients and represents a major perceived barrier to implementing life-saving treatments in clinical practice, as physicians are often concerned about symptomatic hypotension and its consequences. Although low BP can be seen in those hospitalized with signs of shock, the most common scenario involves non-severe, asymptomatic hypotension in patients receiving foundational therapy for HFrEF, where premature down-titration or discontinuation of GDMT should be avoided. This clinical consensus statement provides a comprehensive overview of low BP in HFrEF, including its definition, risk factors, and effects of HF therapies on BP. We propose management pathways to optimize HFrEF treatment in the context of low BP, ultimately aiming to improve patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".