Highlights in Heart Failure
Bibliographic record
Abstract
Heart failure (HF) remains a major cause of mortality, morbidity, and poor quality of life. It is an area of active research. This article is aimed to give an update on recent advances in all aspects of this syndrome. Major changes occurred in drug treatment of HF with reduced ejection fraction (HFrEF). Sacubitril/valsartan is indicated as a substitute to ACEi/ARBs after PARADIGM-HF (hazard ratio [HR], 0.80; 95% confidence interval [CI], 0.73 to 0.87 for sacubitril/valsartan vs. enalapril for the primary endpoint and Wei, Lin and Weissfeld HR 0.79, 95% CI 0.71-0.89 for recurrent events). Its initiation was then shown as safe and potentially useful in recent studies in patients hospitalized for acute HF. More recently, dapagliflozin and prevention of adverse-outcomes in DAPA-HF trial showed the beneficial effects of the sodium-glucose transporter type 2 inhibitor dapaglifozin vs. placebo, added to optimal standard therapy [HR, 0.74; 95% CI, 0.65 to 0.85;0.74; 95% CI, 0.65 to 0.85 for the primary endpoint]. Trials with other SGLT 2 inhibitors and in other patients, such as those with HF with preserved ejection fraction (HFpEF) or with recent decompensation, are ongoing. Multiple studies showed the unfavourable prognostic significance of abnormalities in serum potassium levels. Potassium lowering agents may allow initiation and titration of mineralocorticoid antagonists in a larger proportion of patients. Meta-analyses suggest better outcomes with ferric carboxymaltose in patients with iron deficiency. Drugs effective in HFrEF may be useful also in HF with mid-range ejection fraction. Better diagnosis and phenotype characterization seem warranted in HF with preserved ejection fraction. These and other burning aspects of HF research are summarized 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.075 | 0.029 |
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".