Ultrafiltration in Acute Heart Failure: Implications of Ejection Fraction and Early Response to Treatment From CARRESS‐HF
Bibliographic record
Abstract
Background Ultrafiltration is not commonly used because of higher incidence of worsening renal function without improved decongestion. We examined differential outcomes of high versus low fluid removal and preserved versus reduced ejection fraction (EF) in CARRESS‐HF (Cardiorenal Rescue Study in Acute Decompensated Heart Failure). Methods and Results Baseline characteristics in the ultrafiltration arm were compared according to 24‐hour ultrafiltration‐based fluid removal above versus below the median. Patients were stratified by EF (≤40% or >40%). We compared clinical parameters of clinical decongestion during the hospitalization based on initial (≤24 hours) response to ultrafiltration. Cox‐proportional hazards models were used to identify associations between fluid removal <24 hours and composite of death, hospitalization, or unscheduled outpatient/emergency department visit during study follow‐up. The intention‐to‐treat analysis included 93 patients. Within 24 hours, median fluid removal was 1.89 L (Q1, Q3: 1.22, 3.16). The high fluid removal group had a greater urine output (9.08 versus 6.23 L, P =0.027) after 96 hours. Creatinine change from baseline to 96 hours was similar in both groups (0.10 mg/dL increase, P =0.610). The EF >40% group demonstrated larger increases of change in creatinine ( P =0.023) and aldosterone ( P =0.038) from baseline to 96 hours. Among patients with EF >40%, those with above median fluid removal (n=17) when compared with below median (n=17) had an increased rate of the combined end point (87.5% versus 47.1%, P =0.014). Conclusions In patients with acute heart failure, higher initial fluid removal with ultrafiltration had no association with worsening renal function. In patients with EF >40%, ultrafiltration was associated with worsening renal function irrespective of fluid removal rate and higher initial fluid removal was associated with higher rates of adverse clinical outcomes, highlighting variable responses to decongestive therapy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".