Abstract 11603: Comparing Analytical Methods for Composite Endpoints in Heart Failure With Reduced Ejection Fraction: Insights From the VICTORIA Trial
Bibliographic record
Abstract
Background: In the VICTORIA trial in patients with heart failure with reduced ejection fraction, vericiguat (V) versus placebo (P) reduced the primary composite outcome (heart failure hospitalization [HFH] or cardiovascular death [CVD]) from 38.5 to 35.5% (1869 events: V=897, P=972) ( Fig A ). In this prespecified analysis, we assessed vericiguat’s treatment effect using weighted composite endpoint (WCEP) and win-ratio (WR) methods. Methods: Clinical events were centrally adjudicated. A survey of 37 international trial leaders conducted before database lock led to the derivation of relative weights of subtypes of HFH (mild [change in therapy] 0.39; moderate [requiring IV diuresis] 0.50; severe [urgent deterioration requiring inotropic support] 0.67) and CVD (1.0). A WCEP model estimated the effect of vericiguat on mean survival after severity-weighting recurrent HFH events and CVD. The unmatched WR approach, which accounted for recurrent HFH events, was applied with the hierarchy of HFH Results: All 3412 primary clinical events in 5050 patients were analyzed in the WCEP model (855 CVD [V=414, P=441]; 875 severe HFH [V=416, P=459]; 1614 moderate HFH [V=767, P=847]; 68 mild HFH [V=38, P=30]). WCEP yielded a higher hospitalization-adjusted survival in the vericiguat arm (mean 78.2 vs 75.6%; difference [95% CI]: 2.4% (1.7-3.2%]; p<.0001) ( Fig B ). 2608 events were used in the WR model (V=1232, P=1376) which also found an improvement in clinical outcomes in the vericiguat arm (WR [95% CI]: 1.13 [1.03-1.24]; p=0.01) ( Fig C ). Conclusions: These 2 alternative methods complement the primary VICTORIA analysis with more refined characterization of vericiguat’s treatment effect. While both WCEP and WR included recurrent events, the prespecified WCEP approach allowed for inclusion of all recurrent events and further insight into the severity of HFH. These findings may help inform health providers, consumers, and those planning future HFrEF trials.
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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 | 0.150 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".