Assessing Benefit in Heart Failure Patients with Reduced Ejection Fraction: Analysis of the VICTORIA Trial Using Novel Prognostic Risk Stratification
Bibliographic record
Abstract
Abstract Background Randomized controlled clinical trials remain the gold standard for determining efficacy of new heart failure (HF) therapies; however, failure to account for heterogeneity in risk of the primary endpoint(s) may dilute treatment efficacy. The novel 5-step stratified testing and amalgamation routine (5-STAR) methodology addresses these limitations using risk stratification based on treatment-independent associations between baseline covariates and clinical outcomes. We applied the 5-STAR methodology to the original VICTORIA database enriched by relevant ancillary information. Methods Within the 5-STAR analysis, elastic net Cox regression and a conditional inference tree tool blinded to treatment assignment were used to partition the trial population into risk strata for trial endpoints based on baseline covariates determined to be jointly strongly associated with the risk of the outcome. Core laboratory mechanistic biomarkers and baseline electrocardiographic variables were added to the VICTORIA dataset. After unblinding, treatments were compared for the primary composite endpoint of cardiovascular death or HF hospitalization within each risk stratum: stratum-level results were then averaged for overall inference. Results The 5-STAR analysis showed a greater vericiguat treatment effect on the primary composite endpoint than the original prespecified VICTORIA analysis (5-STAR-averaged HR, 95% CI: 0.85, 0.77–0.94 vs 0.90, 0.82–0.98), and on its components (5-STAR-averaged HR, 95% CI: cardiovascular death: 0.79, 0.67–0.93 vs 0.93, 0.81–1.06; HF hospitalization: 0.89, 0.79–1.00 vs 0.90, 0.81–1.00). Five biomarkers (GDF-15, NT-proBNP, albumin, blood urea nitrogen, urate) determined the risk strata across the 3 endpoints. Conclusions By developing treatment-independent risk stratification, the 5-STAR methodology attenuates dilution of treatment effects inherent in conventional prognostic risk heterogeneity. This retrospective analysis of VICTORIA revealed greater efficacy of vericiguat on the primary endpoint and its components. GDF-15 was consistently the strongest prognostic risk factor across the composite endpoint and its components of cardiovascular death and HF hospitalization. Clinical Trial Registration ClinicalTrials.gov ( NCT02861534 ).
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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 | 0.033 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".