Contemporary Management of Heart Failure with Preserved Ejection Fraction: What is Current and What Lies Ahead?
Bibliographic record
Abstract
In Canada, the incidence of heart failure (HF) among adults ≥40 years has increased from 521 per 100,000 to 601 per 100,000 from 2013 to 2023, and is expected to rise further in the coming decades. HF is the second leading cause of death in Canada, with an age standardized all-cause mortality rate of 5,761 per 100,000 compared to people without HF at 913 per 100,000. HF with preserved ejection fraction (HFpEF), defined as the clinical syndrome of HF with left-ventricular ejection fraction (LVEF) ≥50%, comprises approximately half of all HF diagnoses. Contemporary data published this year suggests one- and five-year mortality rates for HFpEF are similar to those seen in heart failure with reduced ejection fraction (HFrEF). The Canadian Cardiovascular Society (CCS) endorses the universal definition of HF, which classifies HFpEF as having an LVEF cutoff of 50% and emphasizes markers of increased left ventricular (LV) filling pressures as a reflection of the underlying pathophysiology. HFpEF is associated with both functional and structural cardiac abnormalities, including diastolic dysfunction, ventricular and atrial remodelling, LV hypertrophy, and fibrosis.5 In addition, systemic inflammation, endothelial dysfunction, altered myocardial energetics, and abnormalities in skeletal muscle are increasingly recognized as important contributors to HFpEF pathophysiology and serve as therapeutic targets. Comorbid conditions including type 2 diabetes mellitus (T2DM), obesity, atrial fibrillation, chronic kidney disease, pulmonary hypertension, obstructive sleep apnea, and iron deficiency have been associated with the development and progression of HFpEF. Furthermore, there is growing interest in identifying distinct HFpEF phenotypes to better characterize patient populations beyond their comorbid conditions, with the aim of personalizing prognosis and treatment options. In a recent study, three distinct HFpEF phenotypes were identified, including a younger group with primarily New York Heart Association (NYHA) II symptoms, a higher prevalence of smoking, and a lower prevalence of diabetes and chronic kidney disease; another consisting of older age individuals (mean age 77 years), predominantly women with atrial fibrillation and chronic kidney disease; and a third group of intermediate age (mean age 66 years) with a very high prevalence of obesity and diabetes, greater functional impairment, and elevated inflammatory markers. Notably, the patients in this latter phenotype, with a very high prevalence of obesity and diabetes, were most likely to be hospitalized for HF along with having an overall mortality risk comparable to those patients classified in the older, atrial fibrillation, chronic kidney disease phenotype, despite their younger age.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".