European Society of Cardiology Heart Failure Long-Term Registry (ESC-HF-LT): 1-Year Follow-Up Outcomes and Differences Across Regions
Bibliographic record
Abstract
AIMS: The European Society of Cardiology Heart Failure Long-Term Registry (ESC-HF-LT-R) was set up with the aim of describing the clinical epidemiology and the 1-year outcomes of patients with heart failure (HF) with the added intention of comparing differences between participating countries. METHODS AND RESULTS: The ESC-HF-LT-R is a prospective, observational registry contributed to by 211 cardiology centres in 21 European and/or Mediterranean countries, all being member countries of the ESC. Between May 2011 and April 2013 it collected data on 12 440 patients, 40.5% of them hospitalized with acute HF (AHF) and 59.5% outpatients with chronic HF (CHF). The all-cause 1-year mortality rate was 23.6% for AHF and 6.4% for CHF. The combined endpoint of mortality or HF hospitalization within 1 year had a rate of 36% for AHF and 14.5% for CHF. All-cause mortality rates in the different regions ranged from 21.6% to 36.5% in patients with AHF, and from 6.9% to 15.6% in those with CHF. These differences in mortality between regions are thought reflect differences in the characteristics and/or management of these patients. CONCLUSION: The ESC-HF-LT-R shows that 1-year all-cause mortality of patients with AHF is still high while the mortality of CHF is lower. This registry provides the opportunity to evaluate the management and outcomes of patients with HF and identify areas for improvement.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".