Prevalence, Characteristics and Prognostic Impact of Aortic Valve Disease in Patients with Heart Failure and Reduced, Mildly Reduced, and Preserved Ejection Fraction: An Analysis of the ESC Heart Failure Long-Term Registry
Bibliographic record
Abstract
AIMS: To assess the prevalence, clinical characteristics, and outcomes of patients with heart failure (HF) with or without moderate to severe aortic valve disease (AVD) (aortic stenosis [AS], aortic regurgitation [AR], mixed AVD [MAVD]). METHODS AND RESULTS: Data from the prospective ESC HFA EORP HF Long-Term Registry including both chronic and acute HF were analysed. Of 15 216 patients with HF (62.5% with reduced ejection fraction, HFrEF; 14.0% with mildly reduced ejection fraction, HFmrEF; 23.5% with preserved ejection fraction, HFpEF), 706 patients (4.6%) had AR, 648 (4.3%) AS and 234 (1.5%) MAVD. The prevalence of AS, AR and MAVD was 6%, 8%, and 3% in HFpEF, 6%, 3%, and 2% in HFmrEF and 4%, 3%, and 1% in HFrEF. The strongest associations were observed for age and HFpEF with AS, and for left ventricular end-diastolic diameter with AR. AS (adjusted hazard ratio [HR] 1.43, 95% confidence interval [CI] 1.23-1.67), and MAVD (adjusted HR 1.37, 95% CI 1.07-1.74) but not AR (adjusted HR 1.13, 95% CI 0.96-1.33) were independently associated with the 12-month composite outcome of cardiovascular death and HF hospitalization. The associations between AS and the composite outcome were observed regardless of ejection fraction category. CONCLUSIONS: In the ESC HFA EORP HF Long-Term Registry, one in 10 patients with HF had AVD, with AS and MAVD being especially common in HFpEF and AR being similarly distributed across all ejection fraction categories. AS and MAVD, but not AR, were independently associated with increased risk of in-hospital mortality and 12-month composite outcome, regardless of ejection fraction category.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".