Fluid Management for Critically Ill Patients with Acute Kidney Injury Receiving Kidney Replacement Therapy
Bibliographic record
Abstract
BACKGROUND: In critically ill patients receiving KRT, high ultrafiltration rates and persistent fluid accumulation are associated with adverse outcomes. The purpose of this international survey was to evaluate current practices and evidence gaps related to fluid removal with KRT in critically ill patients. METHODS: This was a multinational, web-based survey distributed by seven networks comprising nephrologists and intensivists. Physicians involved in the care of critically ill patients were invited to complete a 39-question survey about fluid management practices on KRT. The survey was distributed from September 2021 to December 2021. RESULTS: There were 757 respondents from 96 countries (response rate of 65%). Most respondents practiced adult medicine (89%) and worked in an academic center (69%). The majority (91%) reported aiming for a 0.5- to 2-L negative fluid balance per day when fluid removal is indicated, although there was important variability in what respondents considered a safe maximal target. Intensivists were more likely than nephrologists to use adjunct volume status assessment methods ( i.e. , ultrasound, hemodynamic markers, and intra-abdominal pressure), while nephrologists were more likely to deploy cointerventions aimed at improving tolerance to fluid removal ( i.e. , osmotic agents and low-temperature dialysate). There was a broad consensus that rapid decongestion should be prioritized when fluid accumulation is present, but the prevention of hypotension was also reported as a competing priority. A majority (77%) agreed that performing trials that compare fluid management strategies would be ethical and clinically relevant. CONCLUSIONS: We have identified multiple areas of variability in current practice of fluid management for patients receiving KRT. Most nephrologists and intensivists agreed that several knowledge gaps related to fluid removal strategies should be investigated in future randomized controlled trials.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".