Abstract 284: Association of Worsening Renal Function With Mortality and Heart Failure Readmissions in Patients Hospitalized With Acute Heart Failure
Bibliographic record
Abstract
Background: The impact of worsening renal function (WRF) on heart failure (HF)-related readmissions (HFR) and HF-related mortality among hospitalized acute HF patients was examined. Methods: A patient’s first acute HF hospitalization event (index) was identified in Cerner Health Facts® database (Jan 2008–March 2011). Patients were categorized as WRF (serum creatinine ≥0.3 mg/dL and ≥25% increase from baseline) persisting at discharge (WRFp), not persisting at discharge (WRFt), or non WRF. Outcomes were compared for the index hospitalization and cumulatively at 30, 180, and 365 days post discharge. Generalized linear model (HFR count) and logistic regression models (mortality) were constructed. Results: The acute HF patients (77% [42,507 of 55,436] non WRF, 10% [5,563 of 55,436] WRFp, and 13% [7,366 of 55,436] WRFt) were 53% [29,442 of 55,436] female with a mean age of 72.4 (±14.3) years. WRFp had higher index mortality rates (23.6% [1,312 of 5,563] vs 5.7% [418 of 7,366] vs 3.9% [1,673 of 42,507], P<0.0001) than WRFt and non WRF patients, respectively. For mortality, 70% [3,403 of 4,883] of deaths occurred at the index hospitalization. WRFp and WRFt patients combined had higher 30-day HFR counts than non WRF patients (0.12 vs 0.09, P<0.0001), but there was no difference between WRFp and WRFt. These observations were consistent across all cumulative time points and confirmed by multivariable analyses. Conclusion: Acute HF patients with WRF were more likely to die or be readmitted than non WRF patients. WRFp patients experienced higher HF-related mortality rates than WRFt patients but there were no differences in HFR between WRFt and WRFp.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".