New pharmacological agents and novel cardiovascular pharmacotherapy strategies in 2022
Bibliographic record
Abstract
Cardiovascular diseases (CVD) remain the leading cause of death worldwide and pharmacotherapy of most of them is suboptimal. Thus, there is a clear unmet clinical need to develop new pharmacological strategies with greater efficacy and better safety profiles. In this review, we summarize the most relevant advances in cardiovascular pharmacology in 2022 including the approval of first-in-class drugs that open new avenues for the treatment of obstructive hypertrophic cardiomyopathy (mavacamten), type 2 diabetes mellitus (tirzepatide), and heart failure (HF) independent of left ventricular ejection fraction (sodium-glucose cotransporter 2 inhibitors). We also dealt with fixed dose combination therapies repurposing different formulations of "old" drugs with well-known efficacy and safety for the treatment of patients with acute decompensated HF (acetazolamide plus loop diuretics), atherosclerotic cardiovascular disease (moderate-dose statin plus ezetimibe), Marfan syndrome (angiotensin receptor blockers plus β-blockers), and secondary cardiovascular prevention (i.e. low-dose aspirin, ramipril and atorvastatin), thereby filling existing gaps in knowledge, and opening new avenues for the treatment of CVD. Clinical trials confirming the role of dapagliflozin in patients with HF and mildly reduced or preserved ejection fraction, long-term evolocumab to reduce the risk of cardiovascular events, vitamin K antagonists for stroke prevention in patients with rheumatic heart disease-associated atrial fibrillation, antibiotic prophylaxis in patients at high risk for infective endocarditis before invasive dental procedures, and vutrisiran for the treatment of hereditary transthyretin-related amyloidosis with polyneuropathy were also reviewed. Finally, we briefly discuss recent clinical trials suggesting that FXIa inhibitors may have the potential to uncouple thrombosis from hemostasis and attenuate/prevent thromboembolic events with minimal disruption of hemostasis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".