Frailty According to the 2019 HFA-ESC Definition in Patients at Risk for Advanced Heart Failure: Insights from the HELP-HF Registry
Bibliographic record
Abstract
AIMS: Frailty is highly prevalent in patients with heart failure (HF), but a concordant definition of this condition is lacking. The Heart Failure Association of the European Society of Cardiology (HFA-ESC) proposed in 2019 a new multi-domain definition of frailty, but it has never been validated. METHODS AND RESULTS: Patients from the HELP-HF registry were stratified according to the number of HFA-ESC frailty domains fulfilled and to the cumulative deficits frailty index (FI) quintiles. Prevalence of frailty and of each domain was reported, as well as the rate of the composite of all-cause death and HF hospitalization, its single components, and cardiovascular death in each group and quintile. Among 854 included patients, 37 (4.3%), 206 (24.1%), 365 (42.8%), 217 (25.4%), and 29 (3.4%) patients fulfilled zero, one, two, three, or four domains, respectively, while 179 patients had a FI < 0.21 and were considered not frail. The 1-year risk of adverse events increased proportionally to the number of domains fulfilled (for each criterion increase, all-cause death or HF hospitalization: hazard ratio [HR] 1.43, 95% confidence interval [CI] 1.27-1.62; all-cause death: HR 1.72, 95% CI 1.46-2.02, HF hospitalizations: subHR 1.21, 95% CI 1.04-1.31; cardiovascular death: HR 1.77, 95% CI 1.45-2.15). Consistent results were found stratifying the cohort for FI quintiles. The FI as a continuous variable demonstrated higher discriminative ability than the number of domains fulfilled (area under the curve = 0.68 vs. 0.64, p = 0.004). CONCLUSION: Frailty in patients at risk for advanced HF, assessed via a multi-domain approach and the FI, is highly prevalent and identifies those at increased risk of adverse events. The FI was found to be slightly more effective in identifying patients at increased risk of mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".