Quality of life assessed 6 months after hospitalisation for acute heart failure: an analysis from <scp>REPORT‐HF</scp> (international REgistry to assess <scp>medical</scp> Practice with <scp>lOngitudinal obseRvation</scp> for Treatment of Heart Failure)
Bibliographic record
Abstract
AIMS: Recovery of well-being after hospitalisation for acute heart failure (AHF) is a measure of the success of interventions and the quality of care but has rarely been quantified. Accordingly, we measured health status after discharge in an international registry (REPORT-HF) of AHF. METHODS AND RESULTS: The analysis included 4606 patients with AHF who survived to hospital discharge, had known vital status at 6 months, and were enrolled in the United States of America, Russian Federation, or Western Europe, where the Kansas City Cardiomyopathy Questionnaire (KCCQ) was administered. Median age was 69 years (quartiles 59-78), 40% were women, and 34% had a left ventricular ejection fraction (LVEF) <40%, and 12% patients died by 6 months. Of 2475 patients with a follow-up KCCQ, 28% were 'alive and well' (KCCQ >75), while 43% had poor health status (KCCQ ≤50). Being 'alive and well' was associated with new-onset AHF, LVEF <40%, younger age, higher baseline KCCQ, country, and race. Associations were similar for increasing health status, with the exception of country and addition of comorbidities. CONCLUSION: In this international global registry, health status recovery after AHF hospitalisation was highly variable. Those with the best health status at 6 months were younger, had new-onset heart failure, and higher baseline KCCQ; nearly one-third of survivors were 'alive and well'. Investigating reasons for changes in KCCQ after hospitalisation might identify new therapeutic targets to improve patient-centred outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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".