A Year in Heart Failure: An Update of Recent Findings
Why this work is in the frame
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Bibliographic record
Abstract
Major changes have occurred in these last years in heart failure (HF) management. Landmark trials and the 2021 European Society of Cardiology guidelines for the diagnosis and treatment of HF have established four classes of drugs for treatment of HF with reduced ejection fraction: angiotensin-converting enzyme inhibitors or an angiotensin receptor-neprilysin inhibitor, beta-blockers, mineralocorticoid receptor antagonists, and sodium-glucose co-transporter 2 inhibitors, namely, dapagliflozin or empagliflozin. These drugs consistently showed benefits on mortality, HF hospitalizations, and quality of life. Correction of iron deficiency is indicated to improve symptoms and reduce HF hospitalizations. AFFIRM-AHF showed 26% reduction in total HF hospitalizations with ferric carboxymaltose vs. placebo in patients hospitalized for acute HF (P = 0.013). The guanylate cyclase activator vericiguat and the myosin activator omecamtiv mecarbil improved outcomes in randomized placebo-controlled trials, and vericiguat is now approved for clinical practice. Treatment of HF with preserved ejection fraction (HFpEF) was a major unmet clinical need until this year when the results of EMPEROR-Preserved (EMPagliflozin outcomE tRial in Patients With chrOnic HFpEF) were issued. Compared with placebo, empagliflozin reduced by 21% (hazard ratio, 0.79; 95% confidence interval, 0.69 to 0.90; P < 0.001), the primary outcome of cardiovascular death or HF hospitalization. Advances in the treatment of specific phenotypes of HF, including atrial fibrillation, valvular heart disease, cardiomyopathies, cardiac amyloidosis, and cancer-related HF, also occurred. Coronavirus disease 2019 (COVID-19) pandemic still plays a major role in HF epidemiology and management. All these aspects are highlighted in this review.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it