Supervised Exercise Training for Chronic Heart Failure With Preserved Ejection Fraction: A Scientific Statement From the American Heart Association and American College of Cardiology
Bibliographic record
Abstract
Heart failure with preserved ejection fraction (HFpEF) is one of the most common forms of heart failure; its prevalence is increasing, and outcomes are worsening. Affected patients often experience severe exertional dyspnea and debilitating fatigue, as well as poor quality of life, frequent hospitalizations, and a high mortality rate. Until recently, most pharmacological intervention trials for HFpEF yielded neutral primary outcomes. In contrast, trials of exercise-based interventions have consistently demonstrated large, significant, clinically meaningful improvements in symptoms, objectively determined exercise capacity, and usually quality of life. This success may be attributed, at least in part, to the pleiotropic effects of exercise, which may favorably affect the full range of abnormalities-peripheral vascular, skeletal muscle, and cardiovascular-that contribute to exercise intolerance in HFpEF. Accordingly, this scientific statement critically examines the currently available literature on the effects of exercise-based therapies for chronic stable HFpEF, potential mechanisms for improvement of exercise capacity and symptoms, and how these data compare with exercise therapy for other cardiovascular conditions. Specifically, data reviewed herein demonstrate a comparable or larger magnitude of improvement in exercise capacity from supervised exercise training in patients with chronic HFpEF compared with those with heart failure with reduced ejection fraction, although Medicare reimbursement is available only for the latter group. Finally, critical gaps in implementation of exercise-based therapies for patients with HFpEF, including exercise setting, training modalities, combinations with other strategies such as diet and medications, long-term adherence, incorporation of innovative and more accessible delivery methods, and management of recently hospitalized patients are highlighted to provide guidance for future research.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.023 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 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".