Association between up‐titration of medical therapy and total hospitalizations and mortality in patients with recent worsening heart failure across the ejection fraction spectrum
Bibliographic record
Abstract
BACKGROUND: The role of neurohormonal inhibition in chronic heart failure (HF) is well established. There are limited data on the effect of up-titration of renin-angiotensin inhibitors (RASi) and beta-blockers (BBs) on clinical outcomes of patients with worsening HF across the left ventricular ejection fraction (LVEF) spectrum. METHODS AND RESULTS: We analysed data from 2345 patients from BIOSTAT-CHF (80.9% LVEF <40%), who completed a 3-month up-titration period after recent worsening of HF. Patients were classified by achieved dose (% of recommended): ≥100%, 50-99%, 1-49%, and none. Recurrent event analysis using joint and shared frailty models was used to examine the association between RASi/BB dose and all-cause and HF hospitalizations. In the 21 months following up-titration, 512 patients died and 879 (37.5%) had ≥1 hospitalization. RASi up-titration was associated, incrementally, with reduced risk of all-cause hospitalization at all achieved dose levels compared to no treatment [hazard ratio (95% confidence interval): ≥100%: 0.60 (0.49-0.74), P < 0.001; 50-99%: 0.56 (0.46-0.68), P < 0.001; 1-49%: 0.71 (0.59-0.86), P < 0.001]. This association was consistent up to an LVEF of 49% (P < 0.001), and when considering only HF hospitalizations. Up-titration of BBs was associated with fewer all-cause hospitalizations only when LVEF was <40% (overall P < 0.001), but with more HF hospitalizations when LVEF was ≥50%. Up-titration of both RASi/BBs was associated with lower mortality in LVEF up to 49%. CONCLUSION: After recent worsening of HF, up-titration of RASi and BBs was associated with a better prognosis in patients with LVEF ≤49%. Up-titration of BBs was associated with a greater risk of HF hospitalization when LVEF was ≥50%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".