Echocardiography as a guide for fluid management
Bibliographic record
Abstract
BACKGROUND: In critically ill patients at risk for organ failure, the administration of intravenous fluids has equal chances of resulting in benefit or harm. While the intent of intravenous fluid is to increase cardiac output and oxygen delivery, unwelcome results in those patients who do not increase their cardiac output are tissue edema, hypoxemia, and excess mortality. Here we briefly review bedside methods to assess fluid responsiveness, focusing upon the strengths and pitfalls of echocardiography in spontaneously breathing mechanically ventilated patients as a means to guide fluid management. We also provide new data to help clinicians anticipate bedside echocardiography findings in vasopressor-dependent, volume-resuscitated patients. OBJECTIVE: To review bedside ultrasound as a method to judge whether additional intravenous fluid will increase cardiac output. Special emphasis is placed on the respiratory effort of the patient. CONCLUSIONS: Point-of-care echocardiography has the unique ability to screen for unexpected structural findings while providing a quantifiable probability of a patient's cardiovascular response to fluids. Measuring changes in stroke volume in response to either passive leg raising or changes in thoracic pressure during controlled mechanical ventilation offer good performance characteristics but may be limited by operator skill, arrhythmia, and open lung ventilation strategies. Measuring changes in vena caval diameter induced by controlled mechanical ventilation demands less training of the operator and performs well during arrythmia. In modern delivery of critical care, however, most patients are nursed awake, even during mechanical ventilation. In patients making respiratory efforts we suggest that ventilator settings must be standardized before assessing this promising technology as a guide for fluid management.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.013 |
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".