Heart Failure in the Last Year: Progress and Perspective
Bibliographic record
Abstract
Research about heart failure (HF) has made major progress in the last years. We give here an update on the most recent findings. Landmark trials have established new treatments for HF with reduced ejection fraction. Sacubitril/valsartan was superior to enalapril in PARADIGM-HF trial, and its initiation during hospitalization for acute HF or early after discharge can now be considered. More recently, new therapeutic pathways have been developed. In the DAPA-HF and EMPEROR-Reduced trials, dapagliflozin and empagliflozin reduced the risk of the primary composite endpoint, compared with placebo [hazard ratio (HR) 0.74; 95% confidence interval (CI) 0.65-0.85; P < 0.001 and HR 0.75; 95% CI 0.65-0.86; P < 0.001, respectively]. Second, vericiguat, an oral soluble guanylate cyclase stimulator, reduced the composite endpoint of cardiovascular death or HF hospitalization vs. placebo (HR 0.90; 95% CI 0.82-0.98; P = 0.02). On the other hand, both the diagnosis and treatment of HF with preserved ejection fraction, as well as management of advanced HF and acute HF, remain challenging. A better phenotyping of patients with HF would be helpful for prognostic stratification and treatment selection. Further aspects, such as the use of devices, treatment of arrhythmias, and percutaneous treatment of valvular heart disease in patients with HF, are also discussed and reviewed in this article.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".