Insights into the European Heart Failure Epidemiology
Bibliographic record
Abstract
AIMS: The Heart Failure Association (HFA) of the European Society of Cardiology (ESC), together with the National Heart Failure Societies (NHFS), designed the European Heart Failure (HF) Survey with an aim of assessing contemporary HF epidemiology, management resources, availability and reimbursement of guideline-directed medications and devices, and structure of professional and patient organizations. This document presents data on HF epidemiology. METHODS AND RESULTS: The European HF Survey was conducted in 43 ESC member countries. Epidemiology data were exclusively collected from national health statistics from 2019, and standardized according to the European Standard Population, with variable response rates and data completeness among the countries. Median annual HF incidence was 3.9 patients per 1000 person-years (interquartile range [IQR] 3.1-6.5), and median HF prevalence was 1937 patients (IQR 1463-3416) per 100 000 population. Median in-hospital mortality of patients admitted for HF was 8.0% (IQR 4.9-9.6%), and median 1-year all-cause mortality of patients with HF was 14.5% (IQR 8.2-21.6%). Median number of HF-related hospitalizations was 333 (IQR 230-469) per 100 000 population, and median length of stay for HF-related hospitalizations was 8.5 (IQR 7.2-9.2) days. A heterogeneity in HF epidemiology statistics was observed across different countries. CONCLUSIONS: The European HF Survey provides a contemporary insight into HF epidemiology and outcomes across the ESC member countries. These data are valuable to inform strategies to improve prevention, diagnosis, and management of HF. The persisting gaps and considerable heterogeneity in epidemiology statistics highlight the need to further unify data collection and reporting practices across European countries.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".