REVEAL risk scores applied to riociguat-treated patients in PATENT-2: Impact of changes in risk score on survival
Bibliographic record
Abstract
BACKGROUND: The Registry to Evaluate Early and Long-term PAH Disease Management (REVEAL) risk score (RRS) calculator was developed using data derived from the REVEAL registry, and predicts survival in patients with pulmonary arterial hypertension (PAH) based on multiple patient characteristics. Herein we applied the RRS to a pivotal PAH trial database, the 12-week PATENT-1 and open-label PATENT-2 extension studies of riociguat. We examined the effect of riociguat vs placebo on RRS in PATENT-1, and investigated the prognostic implications of change in RRS during PATENT-1 on long-term outcomes in PATENT-2. METHODS: RRS was calculated post hoc for baseline and Week 12 of PATENT-1, and Week 12 of PATENT-2. Patients were grouped into risk strata by RRS. Kaplan-Meier estimates were made for survival and clinical worsening-free survival in PATENT-2 to evaluate the relationship between RRS in PATENT-1 and long-term outcomes in PATENT-2. RESULTS: A total of 396 patients completed PATENT-1 and participated in PATENT-2. In PATENT-1, riociguat significantly improved RRS (p = 0.031) and risk stratum (p = 0.018) between baseline and Week 12 compared with placebo. RRS at baseline, and at PATENT-1 Week 12, and change in RRS during PATENT-1 were significantly associated with survival (hazard ratios for a 1-point reduction in RRS: 0.675, 0.705 and 0.804, respectively) and clinical worsening-free survival (hazard ratios of 0.736, 0.716 and 0.753, respectively) over 2 years in PATENT-2. CONCLUSIONS: RRS at baseline and Week 12, and change in RRS, were significant predictors of both survival and clinical worsening-free survival. These data support the long-term predictive value of the RRS in a controlled study 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".