The Prevalence and Importance of Frailty in Heart Failure with Reduced Ejection Fraction – An Analysis of PARADIGM-HF and ATMOSPHERE
Bibliographic record
Abstract
AIMS: Frailty, characterized by loss of homeostatic reserves and increased vulnerability to physiological decompensation, results from an aggregation of insults across multiple organ systems. Frailty can be quantified by counting the number of 'health deficits' across a range of domains. We assessed the frequency of, and outcomes related to, frailty in patients with heart failure and reduced ejection fraction (HFrEF). METHODS AND RESULTS: Using a cumulative deficits approach, we constructed a 42-item frailty index (FI) and applied it to identify frail patients enrolled in two HFrEF trials (PARADIGM-HF and ATMOSPHERE). In keeping with previous studies, patients with FI ≤0.210 were classified as non-frail and those with higher scores were divided into two categories using score increments of 0.100. Clinical outcomes were examined, adjusting for prognostic variables. Among 13 625 participants, mean (± standard deviation) FI was 0.250 (0.10) and 8383 patients (63%) were frail (FI >0.210). The frailest patients were older and had more symptoms and signs of heart failure. Women were frailer than men. All outcomes were worse in the frailest, with high rates of all-cause death or all-cause hospitalization: 40.7 (39.1-42.4) vs. 22.1 (21.2-23.0) per 100 person-years in the non-frail; adjusted hazard ratio 1.63 (1.53-1.75) (P < 0.001). The rate of all-cause hospitalizations, taking account of recurrences, was 61.5 (59.8-63.1) vs. 31.2 (30.3-32.2) per 100 person-years (incidence rate ratio 1.76; 1.62-1.90; P < 0.001). CONCLUSION: Frailty is highly prevalent in HFrEF and associated with greater deterioration in quality of life and higher risk of hospitalization and death. Strategies to prevent and treat frailty are needed in HFrEF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".