Riociguat in pulmonary arterial hypertension: Application of the 4-strata COMPERA 2.0 risk assessment tool in the PATENT studies
Bibliographic record
Abstract
Risk stratification is an essential part of evaluating disease severity in patients with pulmonary arterial hypertension (PAH). This study applied the 4-strata COMPERA 2.0 risk model to the Phase 3 PATENT-1/2 studies of riociguat. This was a post hoc analysis of PATENT-1 and PATENT-2. Log-rank tests of Kaplan–Meier curves were performed to compare the risk strata at PATENT-1 baseline and Week 12 regarding time to clinical worsening and survival at 2 years in the PATENT-2 population. Data on COMPERA 2.0 status at baseline were available for 214 patients with riociguat and 100 with placebo; overall, 120 patients were identified as intermediate-low risk and 96 as intermediate-high risk. At PATENT-1 Week 12, improvements in COMPERA 2.0 risk strata and median 6-minute walk distance were seen with riociguat vs placebo in patients assessed as intermediate-low risk and intermediate-high risk at baseline by COMPERA 2.0. More patients improved their COMPERA 2.0 risk status with riociguat vs placebo in the intermediate-low (38 % vs 22 %) and intermediate-high risk groups (42 % vs 31 %). COMPERA 2.0 assessed at PATENT-1 baseline and Week 12 discriminated between risk strata for survival and clinical worsening in PATENT-2 at 2 years ( p ≤ .001 for all analyses). In conclusion, this analysis supports the risk-reduction benefits of riociguat in patients with PAH at intermediate-low risk and intermediate-high risk, and externally validated the utility of COMPERA 2.0 in the long-term risk assessment of patients from a clinical trial population. • Risk stratification is an essential part of disease management in patients with PAH • The COMPERA 2.0 risk assessment model was applied to PATENT-1 and PATENT-2 • Riociguat improved COMPERA 2.0 risk strata and 6MWD in PATENT-1 • Improvements were seen in patients at intermediate-low and intermediate-high risk • COMPERA 2.0 was associated with survival and clinical worsening in PATENT-2
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".