Safety, Tolerability and Efficacy of Rapid Optimization, Helped by NT-proBNP and GDF-15, of Heart Failure Therapies (STRONG-HF): Rationale and Design for a Multicentre, Randomized, Parallel-Group Study
Bibliographic record
Abstract
AIMS: Patients admitted for acute heart failure (HF) are at high risk of readmission and death, especially in the 90 days following discharge. We aimed to assess the safety and efficacy of early optimization of oral HF therapy with beta-blockers (BB), angiotensin-converting enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARB) or angiotensin receptor-neprilysin inhibitors (ARNi), and mineralocorticoid receptor antagonists (MRA) on 90-day clinical outcomes in patients admitted for acute HF. METHODS: In a multicentre, randomized, open-label, parallel-group study, a total of 900 patients will be randomized in a 1:1 ratio to either 'usual care' or 'high-intensity care'. Patients enrolled in the usual care arm will be discharged and managed according to usual clinical practice at the site. In the high-intensity care arm, doses of oral HF medications - including a BB, ACEi or ARB, and MRA - will be up-titrated to 50% of recommended doses before discharge and to 100% of recommended doses within 2 weeks of discharge. Up-titration will be delayed if the patients develop worsening symptoms and signs of congestion, hyperkalaemia, hypotension, bradycardia, worsening of renal function or significant increase in N-terminal pro-B-type natriuretic peptide between visits. The primary endpoint is 90-day all-cause mortality or HF readmission. CONCLUSIONS: STRONG-HF is the first study to assess whether rapid up-titration of evidence-based guideline-recommended therapies with close follow-up in a large cohort of patients discharged from an acute HF admission is safe and can affect adverse outcomes during the first 90 days after discharge. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT03412201.
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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 | 0.033 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".