Significance of Diuretic Responsiveness in Intensive Care Unit Patients during the De-Resuscitation Phase: A Retrospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Fluid management is a critical aspect of care in critically ill patients. While fluid overload has been linked to adverse outcomes, the balance between achieving a negative fluid balance and preserving kidney function presents a clinical challenge, and the significance of diuretic responsiveness in patients in the de-resuscitation phase remains unclear. OBJECTIVE: This study aimed to evaluate the association between forced diuresis, fluid balance, and clinical outcomes in ICU patients during the de- resuscitation phase. Additionally, we assessed whether changes in kidney function influence prognosis in this patient population. METHODS: A retrospective cohort study was conducted, including 527 critically ill patients treated with furosemide for at least three days during their ICU stay. Fluid balance, kidney function changes (assessed via KDIGO criteria), and clinical outcomes, including ICU mortality and modified SOFA score (excluding renal function), were analyzed. RESULTS: Patients who achieved both a negative fluid balance and improvement in kidney function had the lowest mortality rates and better outcomes. Conversely, those who remained in positive fluid balance despite forced diuresis and exhibited worsening kidney function had the highest mortality and organ dysfunction progression. The presence of vasopressor use and mechanical ventilation was associated with poorer outcomes. CONCLUSION: Among ICU patients undergoing forced diuresis during the de- indicator, non-responsiveness signals a high-risk population. These findings underscore the need for individualized fluid management strategies and highlight the importance of further prospective studies to clarify the role of forced diuresis in critically ill patients.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".