Impact of riociguat on health-related quality of life (HRQoL) in patients with pulmonary arterial hypertension (PAH)
Bibliographic record
Abstract
Introduction In the Phase III PATENT-1 study, riociguat was well tolerated, significantly improved 6-min walking distance, and consistently improved clinically relevant secondary endpoints in PAH patients. Objectives Here the impact of riociguat on HRQoL during PATENT-1 was evaluated. Methods PATENT-1 was a 12-week, double-blind, randomized, placebo-controlled study. Disease-specific (Living with PH [LPH]) and generic EQ-5D questionnaires were administered at baseline and Week 12. Change from baseline to Week 12 for the total population, key subgroups, and sub-scores were examined. Relationships between HRQoL and other clinical endpoints were evaluated. Results 254 patients received riociguat (individual dose titration up to 2.5 mg tid) and 126 patients received placebo. There was a trend towards a higher EQ-5D score for patients treated with riociguat vs placebo (treatment difference +0.06 [95% CI: 0.01 to 0.11]; p=0.066). Improvements in EQ-5D scores were seen in the pretreated subgroup (+0.08 [95% CI: 0.004 to 0.15]) and to a lesser extent in the treatment-naïve subgroup (+0.05 [95% CI: –0.03 to 0.12]). On the LPH total score, riociguat patients significantly improved vs placebo (treatment difference –6.2 [95% CI: –9.8 to –2.5]; p=0.0019). Both treatment-naïve and pretreated subgroups demonstrated similar results, improving by –7.2 (95% CI: –12.3 to –2.2) and –5.1 (95% CI: –10.3 to 0.2), respectively. The LPH physical score improved for patients on riociguat vs placebo (treatment difference –3.3 [95% CI: –4.9 to –1.7]; p<0.0001). Conclusions PAH patients treated with riociguat reported improvement in both disease-specific and generic measures of HRQoL after 12 weeks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".