Riociguat in pulmonary arterial hypertension: Application of the 4-strata COMPERA 2.0 risk assessment tool in the PATENT studies
Bibliographic record
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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-min 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). CONCLUSIONS: 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.
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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 | 0.059 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".