Fluid and Volume Monitoring
Bibliographic record
Abstract
BACKGROUND: Fluid resuscitation is not only used to prevent acute kidney injury (AKI) but fluid management is also a cornerstone of treatment for patients with established AKI and renal failure. Ultrafiltration removes volume initially from the intravascular compartment inducing a relative degree of hypovolemia. Normal reflex mechanisms attempt to sustain blood pressure constant despite marked changes in blood volume and cardiac output. Thus, compensated shock with a normal blood pressure is a major cause of AKI or exacerbations of AKI during ultrafiltration. METHODS: We undertook a systematic review of the literature using MEDLINE, Google Scholar and PubMed searches. We determined a list of key questions and convened a 2-day consensus conference to develop summary statements via a series of alternating breakout and plenary sessions. In these sessions, we identified supporting evidence and generated clinical practice recommendations and/or directions for future research. RESULTS: We defined three aspects of fluid monitoring: i) normal and pathophysiological cardiovascular mechanisms; ii) measures of volume responsiveness and impending cardiovascular collapse during volume removal, and; iii) measured indices of each using non-invasive and minimally invasive continuous and intermittent monitoring techniques. The evidence documents that AKI can occur in the setting of normotensive hypovolemia and that under-resuscitation represents a major cause of both AKI and mortality ion critically ill patients. Traditional measures of intravascular volume and ventricular filling do not predict volume responsiveness whereas dynamic functional hemodynamic markers, such as pulse pressure or stroke volume variation during positive pressure breathing or mean flow changes with passive leg raising are highly predictive of volume responsiveness. Numerous commercially-available devices exist that can acquire these signals. CONCLUSIONS: Prospective clinical trials using functional hemodynamic markers in the diagnosis and management of AKI and volume status during ultrafiltration need to be performed. More traditional measure of preload be abandoned as marked of volume responsiveness though still useful to assess overall volume status.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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".