Efficacy and safety of riociguat in combination therapy for patients with pulmonary arterial hypertension (PATENT studies)
Bibliographic record
Abstract
Many patients with pulmonary arterial hypertension do not achieve treatment goals with monotherapy, and therefore combination therapy is becoming the standard of care. The soluble guanylate cyclase stimulator riociguat is licensed for the treatment of pulmonary arterial hypertension; here we present findings from patients who were receiving combined riociguat plus endothelin receptor antagonists or non‐intravenous prostanoids in the randomized, placebo‐controlled PATENT‐1 study and its open‐label extension (PATENT‐2). Moreover, we include new data from patients receiving early sequential combination therapy (three to six months of endothelin receptor antagonist treatment) or long‐term background endothelin receptor antagonist therapy (>6 months). Patients were randomized to riociguat 2.5 mg–maximum (N = 131 pretreated patients) and placebo (N = 60 pretreated patients). Riociguat improved 6‐min walking distance (PATENT‐1 primary endpoint), functional capacity, and hemodynamics after 12 weeks in pretreated patients. The placebo‐corrected changes in 6‐min walking distance were +24 m in endothelin receptor antagonist‐pretreated patients and +106 m in the small group of prostanoid‐pretreated patients. In the early sequential combination and long‐term background endothelin receptor antagonist groups, the placebo‐corrected changes in 6‐min walking distance were +65 m (95% CI: 17 to 113 m) and +13 m (95% CI: –8 to 33 m), respectively. In conclusion, these data suggest that early sequential combination of an endothelin receptor antagonist plus riociguat is a feasible treatment option. Both early sequential therapy and long‐term background endothelin receptor antagonist plus riociguat were well tolerated in the PATENT studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".