Clinical Application of the Fluid Challenge Approach in Goal-Directed Fluid Therapy: What Can We Learn From Human Studies?
Bibliographic record
Abstract
Resuscitative fluid therapy aims to increase stroke volume (SV) and cardiac output (CO) and restore/improve tissue oxygen delivery in patients with circulatory failure. In individualized goal-directed fluid therapy (GDFT), fluids are titrated based on the assessment of responsiveness status (i.e., the ability of an individual to increase SV and CO in response to volume expansion). Fluid administration may increase venous return, SV and CO, but these effects may not be predictable in the clinical setting. The fluid challenge (FC) approach, which consists on the intravenous administration of small aliquots of fluids, over a relatively short period of time, to test if a patient has a preload reserve (i.e., the relative position on the Frank-Starling curve), has been used to guide fluid administration in critically ill humans. In responders to volume expansion (defined as individuals where SV or CO increases ≥10-15% from pre FC values), FC administration is repeated until the individual no longer presents a preload reserve (i.e., until increases in SV or CO are <10-15% from values preceding each FC) or until other signs of shock are resolved (e.g., hypotension). Even with the most recent technological developments, reliable and practical measurement of the response variable (SV or CO changes induced by a FC) has posed a challenge in GDFT. Among the methods used to evaluate fluid responsiveness in the human medical field, measurement of aortic flow velocity time integral by point-of-care echocardiography has been implemented as a surrogate of SV changes induced by a FC and seems a promising non-invasive tool to guide FC administration in animals with signs of circulatory failure. This narrative review discusses the development of GDFT based on the FC approach and the response variables used to assess fluid responsiveness status in humans and animals, aiming to open new perspectives on the application of this concept to the veterinary field.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".