Biomarkers in heart failure clinical trials. A review from the Biomarkers Working Group of the Heart Failure Association of the European Society of Cardiology
Bibliographic record
Abstract
The approval of new heart failure (HF) therapies has slowed over the past two decades in part due to the high costs of conducting large randomized clinical trials that are needed to adequately power major clinical endpoint studies. Several biomarkers have been identified reflecting different elements of HF pathophysiology, with possible applications in diagnosis, risk stratification, treatment monitoring, and even in the design of clinical trials. Biomarkers could potentially be used to refine study inclusion criteria to enable enrolment of patients who are more likely to respond to a therapeutic intervention, despite being at sufficient risk to meet pre-determined study endpoint rates. When there is a close relationship between biomarker levels and clinical endpoints, changes in biomarker levels after a given treatment can act as a surrogate endpoint, potentially reducing the duration and cost of a clinical trial. Natriuretic peptides have been widely used in clinical trials with a variable amount of added value, which such variation being probably due to the absence of a close pathophysiological connection to the study drug. Notable exceptions to this include sacubitril/valsartan and vericiguat. Future studies should seek to adopt unbiased approaches for discovery of true companion diagnostics; with -omics-based tools, biomarkers might be more precisely selected for use in clinical trials to identify responses that closely reflect the biological effects of the drug under investigation. Finally, biomarkers associated with cardiac damage and remodelling, such as cardiac troponin, could be employed as safety endpoints provided that standardization between different assays is achieved.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".