Aetiology, Timing and Clinical Predictors of Early vs. Late Readmission Following Index Hospitalization for Acute Heart Failure: Insights from ASCEND-HF
Bibliographic record
Abstract
AIMS: Patients hospitalized for heart failure (HF) are at high risk for 30-day readmission. This study sought to examine the timings and causes of readmission within 30 days of an HF hospitalization. METHODS AND RESULTS: Timing and cause of readmission in the ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide and Decompensated Heart Failure) trial were assessed. Early and late readmissions were defined as admissions occurring within 0-7 days and 8-30 days post-discharge, respectively. Patients who died in hospital or remained hospitalized at day 30 post-randomization were excluded. Patients were compared by timing and cause of readmission. Logistic and Cox proportional hazards regression analyses were used to identify independent risk factors for early vs. late readmission and associations with 180-day outcomes. Of the 6584 patients (92%) in the ASCEND-HF population included in this analysis, 751 patients (11%) were readmitted within 30 days for any cause. Overall, 54% of readmissions were for non-HF causes. The median time to rehospitalization was 11 days (interquartile range: 6-18 days) and 33% of rehospitalizations occurred by day 7. Rehospitalization within 30 days was independently associated with increased risk for 180-day all-cause death [hazard ratio (HR) 2.38, 95% confidence interval (CI) 1.93-2.94; P < 0.001]. Risk for 180-day all-cause death did not differ according to early vs. late readmission (HR 0.99, 95% CI 0.67-1.45; P = 0.94). CONCLUSIONS: In this hospitalized HF trial population, a significant majority of 30-day readmissions were for non-HF causes and one-third of readmissions occurred in the first 7 days. Early and late readmissions within the 30-day timeframe were associated with similarly increased risk for death. Continued efforts to optimize multidisciplinary transitional care are warranted to improve rates of early readmission.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".