Early Changes in Renal Function During Rapid Up-Titration of Guideline-Directed Medical Therapy Following an Admission for Acute Heart Failure
Bibliographic record
Abstract
AIM: In this subgroup analysis of STRONG-HF, we explored the association between changes in renal function and efficacy of rapid up-titration of guideline-directed medical therapy (GDMT) according to a high-intensity care (HIC) strategy. METHODS AND RESULTS: In patients randomized to the HIC arm (n = 542), renal function was assessed at baseline and during follow-up visits. We studied the association with clinical characteristics and outcomes of a decrease in estimated glomerular filtration rate (eGFR) at week 1, defined as ≥15% decrease from baseline. Patients in the usual care group (n = 536) were seen at day 90. The treatment effect of HIC versus usual care was independent of baseline eGFR (p-interaction = 0.4809). A decrease in eGFR within 1 week occurred in 77 (15.5%) patients and was associated with more rales on examination (p = 0.004), and a higher New York Heart Association class at the corresponding visit. Following the decrease in eGFR at 1 week, lower average optimal doses of GDMT were prescribed during follow-up (p = 0.0210) and smaller reductions in N-terminal pro-B-type natriuretic peptide occurred (geometrical mean 0.81 in no eGFR decrease vs 1.12 in GFR decrease, p = 0.0003). The rate of heart failure (HF) readmission or death at 180 days was 12.3% in no eGFR decrease versus 18.5% in eGFR decrease (p = 0.2274) and HF readmissions were 7.8% versus 16.6% (p = 0.0496). CONCLUSIONS: In the STRONG-HF study, HIC reduced 180-day HF readmission or death regardless of baseline eGFR. An early decrease in eGFR during rapid up-titration of GDMT was associated with more evidence of congestion, yet lower doses of GDMT during follow-up.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".