Influence of Sacubitril/Valsartan (LCZ696) on 30-Day Readmission After Heart Failure Hospitalization
Bibliographic record
Abstract
BACKGROUND: Patients with heart failure (HF) are at high risk for hospital readmission in the first 30 days following HF hospitalization. OBJECTIVES: This study sought to determine if treatment with sacubitril/valsartan (LCZ696) reduces rates of hospital readmission at 30-days following HF hospitalization compared with enalapril. METHODS: We assessed the risk of 30-day readmission for any cause following investigator-reported hospitalizations for HF in the PARADIGM-HF trial, which randomized 8,399 participants with HF and reduced ejection fraction to treatment with LCZ696 or enalapril. RESULTS: Accounting for multiple hospitalizations per patient, there were 2,383 investigator-reported HF hospitalizations, of which 1,076 (45.2%) occurred in subjects assigned to LCZ696 and 1,307 (54.8%) occurred in subjects assigned to enalapril. Rates of readmission for any cause at 30 days were 17.8% in LCZ696-assigned subjects and 21.0% in enalapril-assigned subjects (odds ratio: 0.74; 95% confidence interval: 0.56 to 0.97; p = 0.031). Rates of readmission for HF at 30-days were also lower in subjects assigned to LCZ696 (9.7% vs. 13.4%; odds ratio: 0.62; 95% confidence interval: 0.45 to 0.87; p = 0.006). The reduction in both all-cause and HF readmissions with LCZ696 was maintained when the time window from discharge was extended to 60 days and in sensitivity analyses restricted to adjudicated HF hospitalizations. CONCLUSIONS: Compared with enalapril, treatment with LCZ696 reduces 30-day readmissions for any cause following discharge from HF hospitalization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".