Early Initiation of Sacubitril/Valsartan in Patients With Acute Heart Failure and Renal Dysfunction: An Analysis of the TRANSITION Study
Bibliographic record
Abstract
Background Treatment of patients with heart failure with reduced ejection fraction (HFrEF) and renal dysfunction (RD) is challenging owing to the risk of further deterioration in renal function, especially after acute decompensated HF (ADHF). Methods and Results We assessed the effect of RD (estimated glomerular filtration rate of ≥30 to <60 mL/min/1.73 m 2 ) on initiation, up-titration, and tolerability of sacubitril/valsartan in hemodynamically stabilized patients with HFrEF admitted for ADHF (RD, n = 476; non-RD, n = 483). At week 10, the target dose of sacubitril/valsartan (97/103 mg twice daily) was achieved by 42% patients in RD subgroup vs 54% in non-RD patients ( P < .001). Sacubitril/valsartan was associated with greater estimated glomerular filtration rate improvements in RD subgroup than non-RD (change from baseline least squares mean 4.1 mL/min/1.73 m 2 , 95% confidence interval 2.2–6.1, P < .001). Cardiac biomarkers improved significantly in both subgroups; however, compared with the RD subgroup, the improvement was greater in those without RD (N-terminal pro-brain natriuretic peptide, −28.6% vs −44.8%, high-sensitivity troponin T −20.3% vs −33.9%) ( P < .001). Patients in the RD subgroup compared with those without RD experienced higher rates of hyperkalemia (16.3% vs 6.5%, P < .001), investigator-reported cardiac failure (9.7% vs 5.6%, P = .029), and renal impairment (6.4% vs 2.1%, P = .002). Conclusions Most patients with HFrEF and concomitant RD hospitalized for ADHF tolerated early initiation of sacubitril/valsartan and showed significant improvements in estimated glomerular filtration rate and cardiac biomarkers. Clinical Trial Registration NCT02661217.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".