Heart failure treatment up‐titration and outcome and age: an analysis of BIOSTAT‐CHF
Bibliographic record
Abstract
AIMS: Several studies have shown that older patients with heart failure with reduced ejection fraction (HFrEF) are undertreated. The aim of this study was to evaluate the association of up-titration of angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB) and beta-blockers on outcome across the age spectrum in HFrEF patients. METHODS AND RESULTS: We analysed HFrEF patients on sub-optimal doses of ACEI/ARB and/or beta-blockers from the BIOSTAT-CHF study stratified by age. Patients underwent a 3-month up-titration period. We used inverse probability weighting to adjust for the likelihood of successful up-titration to determine the association of achieved dose with mortality and/or heart failure hospitalisation, testing for an interaction with age. Over a median follow-up of 21 months in 1720 HFrEF patients (76.5% male, mean age 67 years), the primary outcome occurred in 558 patients. Increased percentage of target dose of ACEI/ARB and beta-blocker achieved at 3 months were both significantly associated with reduced incidence of the primary outcome, [ACEI-ARB: hazard ratio (HR) per 12.5% increase in dose: 0.92, 95% confidence interval (CI) 0.91-0.94, P < 0.001; beta-blocker: HR 0.98, 95% CI 0.95-1.00, P = 0.046], with a significant interaction with age seen for beta-blockers but not ACEI/ARB (P = 0.034 and P = 0.22, respectively). CONCLUSIONS: Achieving higher doses of ACEI/ARB was associated with improved outcome regardless of age. However, achieving higher doses of beta-blockers was only associated with improved outcome in younger, but not in older patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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) | 0.002 | 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".