Safety Indicators in Patients Receiving High-intensity Care After Hospital Admission for Acute Heart Failure: The STRONG-HF Trial
Bibliographic record
Abstract
Background STRONG-HF demonstrated the safety and efficacy of rapid up-titration of guideline-directed medical therapy (GDMT) with high intensity care (HIC) compared to usual care in patients hospitalized for acute heart failure (HF). In the HIC group, the following safety indicators were used to guide up-titration: estimated glomerular filtration rate [eGFR] <30ml/min/1.73m 2 , serum potassium >5.0 mmol/L, systolic blood pressure (SBP) <95mmHg, heart rate <55bpm, NT-proBNP concentration >10% higher than pre-discharge values. Methods We examined the impact of protocol-specified safety indicators on achieved dose of GDMT and clinical outcomes. Results Three-hundred-thirteen of the 542 patients in the HIC arm (57.7%) met at least one safety indicator at any follow-up visit 1 to 6 weeks after discharge. As compared to those without, patients meeting at least one safety indicator had more severe HF symptoms, lower SBP and higher heart rate at baseline and achieved a lower average percentage of GDMT optimal doses (mean difference vs the HIC arm patients not reaching any safety indicator, -11.0% [95% CI -13.6 to -8.4%], P<0.001). The primary endpoint of 180-day all-cause death or HF re-admission occurred in 15.0% of patients with any safety indicator versus 14.2% of those without (adjusted hazard ratio [HR] 0.84, 95% CI 0.48 to 1.46, P=0.540). None of each safety indicator, considered alone, was significantly associated with the primary endpoint, but SBP < 95mmHg was associated with a trend towards increased 180 days all-cause mortality (adjusted HR = 2.68 [0.94 to 7.64]; P = 0.065) and eGFR drop to < 30ml/min/1.73m 2 with more HF readmissions (adjusted HR 3.60 [1.22 to 10.60]; p = 0.0203). The occurrence of a safety indicator was associated with a smaller 90-day improvement in EQ-5D VAS (adjusted mean difference -3.32 points, 95% CI -5.97 to -0.66, P=0.015). Conclusions Among patients with acute HF enrolled in STRONG-HF in the HIC arm, the occurrence of any safety indicator was associated with the administration of slightly lower GDMT doses and less improvement in quality of life but with no significant increase in the primary outcome of 180-day HF readmission or death when appropriately addressed according to the study protocol.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".