Effect of riociguat on right ventricular function in patients with pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension
Bibliographic record
Abstract
BACKGROUND: In the Phase III PATENT-1 (NCT00810693) and CHEST-1 (NCT00855465) studies, riociguat demonstrated efficacy vs placebo in patients with pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH). Clinical effects were maintained at 2 years in the long-term extension studies PATENT-2 (NCT00863681) and CHEST-2 (NCT00910429). METHODS: This post hoc analysis of hemodynamic data from PATENT-1 and CHEST-1 assessed whether riociguat improved right ventricular (RV) function parameters including stroke volume index (SVI), stroke volume, RV work index, and cardiac efficiency. REVEAL Risk Score (RRS) was calculated for patients stratified by SVI and right atrial pressure (RAP) at baseline and follow-up. The association between RV function parameters and SVI and RAP stratification with long-term outcomes was assessed. RESULTS: In PATENT-1 (n = 341) and CHEST-1 (n = 238), riociguat improved RV function parameters vs placebo (p < 0.05). At follow-up, there were significant differences in RRS between patients with favorable and unfavorable SVI and RAP, irrespective of treatment arm (p < 0.0001). Multiple RV function parameters at baseline and follow-up were associated with survival and clinical worsening-free survival (CWFS) in PATENT-2 (n = 396; p < 0.05) and CHEST-2 (n = 237). In PATENT-2, favorable SVI and RAP at follow-up only was associated with survival and CWFS (p < 0.05), while in CHEST-2, favorable SVI and RAP at baseline and follow-up were associated with survival and CWFS (p < 0.05). CONCLUSION: This post hoc analysis of PATENT and CHEST suggests that riociguat improves RV function in patients with PAH and CTEPH.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 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".