Heart Failure Improvement, Remission, and Recovery: A <i>European Journal of Heart Failure</i> Expert Consensus Document
Bibliographic record
Abstract
Heart failure (HF) is a heterogeneous and dynamic syndrome characterized by progressive pathophysiological alterations, variable clinical trajectories, and differential responses to therapeutic interventions. The concept of HF with improved ejection fraction (HFimpEF) underscores this complexity, identifying patients who exhibit an increase in left ventricular ejection fraction (LVEF) following time and/or pharmacological and device-based therapies. However, the distinction between improvement, remission, and recovery remains inconsistently defined and is primarily LVEF-centric, lacking comprehensive assessment of structural, functional, and symptomatic HF status. This expert consensus document delineates HF trajectories, examines factors reflecting HF improvement beyond recovery of LVEF, and explores the prognostic implications of these phenotypic transitions. Emphasis is placed on the necessity of continued guideline-directed medical and device therapy to minimize the risk of relapse. While a subset of patients attains sustained myocardial and clinical recovery, others remain susceptible to relapse, necessitating individualized monitoring and long-term management. Persistent knowledge gaps regarding the safety and feasibility of treatment de-escalation, the role of genetic predisposition, and optimal therapeutic strategies underscore the need for further research to refine risk stratification and evidence-based decision-making in HFimpEF.
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 | 0.036 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".