Blood Pressure and Intensive Treatment Up-Titration After Acute Heart Failure Hospitalization: Insights from the STRONG-HF Trial
Bibliographic record
Abstract
Abstract Aims A high-intensity care (HIC) strategy with rapid guideline-directed medical therapy (GDMT) up-titration and close follow-up visits improved outcomes, compared to usual care (UC), in patients recently hospitalized for acute heart failure (AHF). Hypotension is a major limitation to GDMT implementation. We aimed to assess the impact of baseline systolic blood pressure (SBP) on the effects of HIC versus UC and the role of early SBP changes in STRONG-HF. Methods and results A total of 1075 patients hospitalized for AHF with SBP ≥100 mmHg were included in STRONG-HF. For the purpose of this post-hoc analysis, patients were stratified by tertiles of baseline SBP (<118, 118–128, and ≥129 mmHg) and, in the HIC arm, by tertiles of changes in SBP from the values measured before discharge to those measured at 1 week after discharge (≥2 mmHg increase, ≤7 mmHg decrease to <2 mmHg increase, and ≥8 mmHg decrease). The primary endpoint was 180-day heart failure rehospitalization or death. The effect of HIC versus UC on the primary endpoint was independent of baseline SBP evaluated as tertiles (pinteraction = 0.77) or as a continuous variable (pinteraction = 0.91). In the HIC arm, patients with increased, stable and decreased SBP at 1 week reached 83.5%, 76.2% and 75.3% of target doses of GDMT at day 90. The risk of the primary endpoint was not significantly different between patients with different SBP changes at 1 week (adjusted p = 0.46). Conclusions In STRONG-HF, the benefits of HIC versus UC were independent of baseline SBP. Rapid GDMT up-titration was performed also in patients with an early SBP drop, resulting in similar 180-day outcome as compared to patients with stable or increased SBP.
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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) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".