Global Registries and Surveys Programme–Heart Failure (GRASP-HF): Rationale, Study Design and Research Implications
Bibliographic record
Abstract
Heart failure (HF) is characterized by increasing prevalence, high morbidity and mortality, poor quality of life, and substantial healthcare costs. Despite advancements in pharmacologic and device-based therapies, translating evidence from randomized controlled trials into clinical practice remains suboptimal. The Global Registries and Surveys Programme-Heart Failure (GRASP-HF) is a pan-European, snapshot, observational study, aiming at assessing the real-world implementation of evidence-based HF management. GRASP-HF captures both acute and chronic HF presentations to assess the adherence to the 2021 and 2023 European Society of Cardiology (ESC) HF Guidelines. It also serves as a platform for the accreditation of HF centres for the Improving Care through Accreditation and Recognition in Heart Failure (ICARe-HF) programme. This manuscript outlines the rationale, methodology, and design of GRASP-HF. Unlike previous registries, GRASP-HF ensures that all patients are consecutively enrolled over a pre-defined 2-month period, minimizing selection bias. GRASP-HF offers a real-time perspective on diagnostic strategies, use of guideline-recommended medical therapy and implementation of quality-of-care indicators. In addition, GRASP-HF addresses less explored domains by other registries, such as frailty, rare aetiologies (e.g. amyloidosis, genetic cardiomyopathies, Takotsubo syndrome), as well as non-fatal events during hospitalization and follow-up. GRASP-HF is also designed to inform ESC educational strategies and to benchmark progresses in HF care across European and non-European centres. In conjunction with ICARe-HF, annual repetition of GRASP-HF aims to facilitate continuous feedback between evidence, practice, and quality improvement. GRASP-HF will assist National Cardiac Societies in shaping national and institutional policies and will contribute with data-driven insights to future guideline development.
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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 | 0.222 | 0.250 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".