Adherence and Optimization of Angiotensin Converting Enzyme Inhibitor/Angiotensin II Receptors Blockers and Beta-Blockers in Patients Hospitalized for Acute Heart Failure
Bibliographic record
Abstract
AIMS: Treatment with angiotensin converting enzyme inhibitor (ACEi)/angiotensin II receptors blockers (ARBs) and beta-blockers is frequently suboptimal at discharge in patients hospitalized for acute heart failure (AHF). We investigated the prognostic significance of medical treatment at discharge and its changes during hospitalization. METHODS AND RESULTS: In a retrospective analysis, we included 623 patients hospitalized for AHF with reduced left ventricular ejection fraction (<40%). The primary endpoint was all-cause mortality and heart failure rehospitalization to Day 180 since hospital discharge. A total of 249 (42.4%) of patients received no ACEi/ARBs/BB or <50% target dose (TD) of these drugs, 249 (42.4%) had either ACEi/ARBs or BB ≥ 50% of TD, and 89 (15.2%) ACEi/ARBs and BB ≥ 50% of TD at discharge. The primary endpoint was significantly lower in patients receiving at least one drug ≥50% of TD compared with no or low-dose treatment (ACEi/ARBs or BB ≥ 50% TD: adjusted hazard ratio (HR) 0.69, 95% confidence interval (CI) [0.49-0.98], P = 0.04; ACEi/ARBs and BB ≥ 50% TD: adjusted HR 0.54, 95% CI [0.30-0.96], P = 0.03). With regard to treatment changes from admission to discharge, therapy was decreased in 258 (44.6%) patients, stable in 194 (33.6%), and increased in 126 (21.8%). Compared with patients with stable therapy, treatment intensification was associated with a lower rate of the primary endpoint (adjusted HR 0.49, 95% CI [0.29-0.83]; P = 0.01). CONCLUSIONS: In patients with AHF, prescription of ACEi/ARBs/BB ≥ 50% TD at the time of discharge, whether achieved or not through treatment intensification during the hospitalization, is associated with better post-discharge 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".