Prognostic Significance of Creatinine Increases During an Acute Heart Failure Admission in Patients With and Without Residual Congestion
Bibliographic record
Abstract
Background: The importance of a serum creatinine increase, traditionally considered worsening renal function (WRF), during admission for acute heart failure has been recently debated, with data suggesting an interaction between congestion and creatinine changes. Methods and Results: In post hoc analyses, we analyzed the association of WRF with length of hospital stay, 30-day death or cardiovascular/renal readmission and 90-day mortality in the PROTECT study (Placebo-Controlled Randomized Study of the Selective A1 Adenosine Receptor Antagonist Rolofylline for Patients Hospitalized With Acute Decompensated Heart Failure and Volume Overload to Assess Treatment Effect on Congestion and Renal Function). Daily creatinine changes from baseline were categorized as WRF (an increase of 0.3 mg/dL or more) or not. Daily congestion scores were computed by summing scores for orthopnea, edema, and jugular venous pressure. Of the 2033 total patients randomized, 1537 patients had both available at study day 14. Length of hospital stay was longer and 30-day cardiovascular/renal readmission or death more common in patients with WRF. However, these were driven by significant associations in patients with concomitant congestion at the time of assessment of renal function. The mean difference in length of hospital stay because of WRF was 3.51 (95% confidence interval, 1.29–5.73) more days ( P =0.0019), and the hazard ratio for WRF on 30-day death or heart failure hospitalization was 1.49 (95% confidence interval, 1.06–2.09) times higher ( P =0.0205), in significantly congested than nonsignificantly congested patients. A similar trend was observed with 90-day mortality although not statistically significant. Conclusions: In patients admitted for acute heart failure, WRF defined as a creatinine increase of ≥0.3 mg/dL was associated with longer length of hospital stay, and worse 30- and 90-day outcomes. However, effects were largely driven by patients who had residual congestion at the time of renal function assessment. Clinical Trial Registration : URL: https://www.clinicaltrials.gov . Unique identifiers: NCT00328692 and NCT00354458.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".