Abstract 14319: Riociguat in Pulmonary Arterial Hypertension: Application of the 4-strata COMPERA 2.0 Risk Assessment Tool in the PATENT Studies
Bibliographic record
Abstract
Background: The 2022 ESC/ERS treatment guidelines for pulmonary arterial hypertension (PAH) recommend using a four-strata risk assessment strategy at follow-up. COMPERA 2.0 is a refined four-strata risk assessment tool that subdivides patients at intermediate risk of mortality at 1 year into intermediate-low and intermediate-high risk groups for a more granular approach to risk prediction. Aim: This post-hoc analysis applied COMPERA 2.0 to the PATENT studies of riociguat in patients with PAH. Methods: COMPERA 2.0 was applied to patients who were pretreated with PAH therapy at PATENT-1 entry. Patients with missing data for COMPERA 2.0 variables were excluded. In PATENT-1, risk strata were assessed at baseline and Week 12; and 6-minute walking distance (6MWD) at Week 12 was analyzed by risk strata at baseline. All pretreated patients entering the PATENT-2 open-label extension were included in Kaplan-Meier analyses to assess association of risk strata at PATENT-1 baseline and Week 12 with clinical worsening and survival. Results: At PATENT-1 baseline (riociguat 2.5 mg n=102, placebo n=49), more patients were at intermediate-low risk than intermediate-high risk (Figure 1). At PATENT-1 Week 12, a higher proportion of patients were at low risk with riociguat vs placebo (Figure 1). At PATENT-1 Week 12, the following mean [SD] changes in 6MWD were seen in patients at intermediate-low risk (riociguat 24 m [57 m], placebo 10 m [60 m]) and intermediate-high risk (riociguat 34 m [56 m], placebo –2 m [70 m]) by COMPERA 2.0 at baseline. COMPERA 2.0 assessed at PATENT-1 baseline and Week 12 was able to discriminate between risk strata for clinical worsening and survival in PATENT-2 (n=167, log-rank tests: p≤0.001 for all analyses). Conclusion: This analysis confirmed the risk-reduction benefits of riociguat in patients with PAH and 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 | 0.046 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".