Safety and efficacy of riociguat in patients with pulmonary arterial hypertension and cardiometabolic comorbidities: Data from interventional clinical trials
Bibliographic record
Abstract
BACKGROUND: There is limited evidence to support treatment recommendations in patients with pulmonary arterial hypertension (PAH) and comorbidities. To investigate the impact of riociguat treatment in this patient population, we analyzed pooled data from randomized controlled trials of riociguat. METHODS: This post hoc analysis included data from the PATENT-1, PATENT-2, PATENT PLUS, and REPLACE studies. Safety, efficacy (6-minute walk distance [6MWD], World Health Organization functional class [WHO-FC], and N-terminal probrain natriuretic peptide [NT-proBNP]), and COMPERA 2.0 risk status were assessed in patients with 0, 1 to 2, or 3 to 4 cardiometabolic comorbidities (obesity, systemic hypertension, diabetes mellitus, coronary artery disease) in the main phase of the studies. Safety was also assessed in the long-term extensions. RESULTS: The analysis included 686 patients (riociguat, n = 440; placebo, n = 132; phosphodiesterase type 5 inhibitors [PDE5i], n = 114), of whom 55%, 39%, and 6% had 0, 1 to 2, and 3 to 4 comorbidities, respectively. In the main phase, rates and severity of adverse events (AEs) were similar in riociguat-treated patients across comorbidity subgroups. After 2 years, discontinuations of riociguat due to AEs were also similar across subgroups. Compared with placebo and PDE5i, riociguat improved 6MWD and NT-proBNP across comorbidity groups and improved WHO-FC and COMPERA 2.0 risk status in patients with 0 or 1 to 2 comorbidities. CONCLUSIONS: Riociguat had an acceptable safety profile in PAH patients with cardiometabolic comorbidities. Efficacy and risk assessment results suggest that riociguat can be beneficial for patients with PAH, irrespective of the presence of comorbidities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".