Combination therapy in pulmonary arterial hypertension: recent accomplishments and future challenges
Bibliographic record
Abstract
Pulmonary arterial hypertension (PAH) is a life-threatening disease characterized by a progressive increase in pulmonary vascular resistance, ultimately leading to right heart failure and death. Throughout the past 20 years, numerous specific pharmacologic agents, including phosphodiesterase-5 inhibitors, endothelin receptor antagonists, prostaglandins, and more recently, soluble guanylate cyclase stimulators and selective IP prostacyclin receptor agonist, have emerged for the treatment of PAH. Early clinical trials were typically of short-term duration, comparing the effects of PAH-targeted therapies versus placebo and using exercise tolerance as the primary endpoint in most trials. A meta-analysis of these trials documented a reduction in short-term mortality of ∼40% with monotherapy. More recently, we have witnessed a progressive shift in PAH study designs using longer event-driven trials comparing the effects of upfront and sequential combination therapy on clinical worsening that is perceived as a more clinically relevant outcome measure. Recent meta-analyses also documented that combination therapy significantly reduced the risk of clinical worsening by ∼35% compared with monotherapy alone. In this review article, we will discuss the evolution of treatments and clinical trial design in the field of PAH over the past decades with a special focus on combination therapy and its current role in the management of PAH. We will also detail unresolved questions regarding the future of PAH patients' care and the challenges of future clinical trials.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".