Biomarker Changes as Surrogate Endpoints in Early-Phase Trials in Heart Failure with Reduced Ejection Fraction
Bibliographic record
Abstract
AIMS: No biomarker has achieved widespread acceptance as a surrogate endpoint for early-phase heart failure (HF) trials. We assessed whether changes over time in a panel of plasma biomarkers were associated with subsequent morbidity/mortality in HF with reduced ejection fraction (HFrEF). METHODS AND RESULTS: In 1040 patients with HFrEF from the BIOSTAT-CHF cohort, we investigated the associations between changes in the plasma concentrations of 30 biomarkers, before (baseline) and after (9 months) attempted optimization of guideline-recommended therapy, on top of the BIOSTAT risk score and the subsequent risk of HF hospitalization/all-cause mortality using Cox regression models. C-statistics were calculated to assess discriminatory power of biomarker changes/month-nine assessment. Changes in N-terminal pro-B-type natriuretic peptide (NT-proBNP) and WAP four-disulphide core domain protein HE4 (WAP-4C) were the only independent predictors of the outcome after adjusting for their baseline plasma concentration, 28 other biomarkers (both baseline and changes), and BIOSTAT risk score at baseline. When adjusting for month-nine rather than baseline biomarkers concentrations, only changes in NT-proBNP were independently associated with the outcome. The C-statistic of the model including the BIOSTAT risk score and NT-proBNP increased by 4% when changes were considered on top of baseline concentrations and by 1% when changes in NT-proBNP were considered on top of its month-nine concentrations and the BIOSTAT risk score. CONCLUSIONS: Among 30 relevant biomarkers, a change over time was significantly and independently associated with HF hospitalization/all-cause death only for NT-proBNP. Changes over time were modestly more prognostic than baseline or end-values alone. Changes in biomarkers should be further explored as potential surrogate endpoints in early phase HF 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 | 0.083 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".