Volume Assessment in Mechanically Ventilated Critical Care Patients Using Bioimpedance Vectorial Analysis, Brain Natriuretic Peptide, and Central Venous Pressure
Bibliographic record
Abstract
Purpose. Strategies for volume assessment of critically ill patients are limited, yet early goal-directed therapy improves outcomes. Central venous pressure (CVP), Bioimpedance Vectorial Analysis (BIVA), and brain natriuretic peptide (BNP) are potentially useful tools. We studied the utility of these measures, alone and in combination, to predict changing oxygenation. Methods. Thirty-four mechanically ventilated patients, 26 of whom had data beyond the first study day, were studied. Relationships were assessed between CVP, BIVA, BNP, and oxygenation index (O(2)I) in a cross-sectional (baseline) and longitudinal fashion using both univariate and multivariable modeling. Results. At baseline, CVP and O(2)I were positively correlated (R = 0.39; P = .021), while CVP and BIVA were weakly correlated (R = -0.38; P = .025). The association between slopes of variables over time was negligible, with the exception of BNP, whose slope was correlated with O(2)I (R = 0.40; P = .044). Comparing tertiles of CVP, BIVA, and BNP slopes with the slope of O(2)I revealed only modest agreement between BNP and O(2)I (kappa = 0.25; P = .067). In a regression model, only BNP was significantly associated with O(2)I; however, this was strengthened by including CVP in the model. Conclusions. BNP seems to be a valuable noninvasive measure of volume status in critical care and should be assessed in a prospective manner.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".