Worsening Heart Failure, a Critical Event During Hospital Admission for Acute Heart Failure: Results from the VERITAS Study
Bibliographic record
Abstract
AIMS: Worsening heart failure (WHF) in the first 7 days after an admission for acute HF (AHF) has been proposed as a therapeutic target in several recent AHF studies and was a co-primary endpoint of the VERITAS studies. METHODS AND RESULTS: Patients were randomized within 24 h of admission for AHF. WHF was defined as worsening or persistent signs and symptoms of HF requiring additional intravenous or mechanical therapy for HF or death within 7 days of randomization. Multivariable models were developed to predict the time to WHF through day 7. Unadjusted and multivariable-adjusted associations of WHF with the length of stay (LOS) of the index hospitalization, and 30- and 90-day outcomes were estimated. WHF occurred by day 7 in 27% of the 1347 patients enrolled. Age, co-morbidities, and markers of HF severity were moderately predictive of WHF; the C-index for a multivariable model for WHF was 0.66. After multivariable adjustment for baseline characteristics, WHF was associated with an increase in LOS of 4.33 days [95% confidence interval (CI) 3.54-5.13 days], a hazard ratio (HR) for 30-day HF readmission or death of 2.43 (95% CI 1.75-3.40), and a HR for 90-day mortality of 2.57 (95% CI 1.81-3.65), all with P < 0.0001.The associations of WHF with these outcomes remained largely unchanged after adjustment for both baseline characteristics and changes in markers of renal and hepatic dysfunction during the first day of admission. CONCLUSIONS: In patients admitted for AHF, WHF is a significant clinical event that is associated with delays in discharge and higher rates for readmission and death.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".