Natriuretic Peptide–driven Fluid Management during Ventilator Weaning: A Randomized Controlled Trial
Bibliographic record
Abstract
RATIONALE: Difficult weaning from mechanical ventilation is often associated with fluid overload. B-type natriuretic peptide (BNP) has been proposed as a tool for predicting and detecting weaning failure of cardiovascular origin. OBJECTIVES: To investigate whether fluid management guided by daily BNP plasma concentrations improves weaning outcomes compared with empirical therapy dictated by clinical acumen. METHODS: In a randomized controlled multicenter study, we allocated 304 patients to either a BNP-driven or physician-driven strategy of fluid management during ventilator weaning. To standardize the weaning process, patients in both groups were ventilated with an automatic computer-driven weaning system. The primary end point was time to successful extubation. MEASUREMENTS AND MAIN RESULTS: In the BNP-driven group, furosemide and acetazolamide were given more often and in higher doses than in the control group, resulting in a more negative median (interquartile range) fluid balance during weaning (-2,320 [-4,735, 738] vs. -180 [-2,556, 2,832] ml; P < 0.0001). Time to successful extubation was significantly shorter with the BNP-driven strategy (58.6 [23.3, 139.8] vs. 42.4 [20.8, 107.5] h; P = 0.034). The BNP-driven strategy increased the number of ventilator-free days but did not change length of stay or mortality. The effect on weaning time was strongest in patients with left ventricular systolic dysfunction. The two strategies did not differ significantly regarding electrolyte imbalance, renal failure, or shock. CONCLUSIONS: Our results suggest that a BNP-driven fluid management strategy decreases the duration of weaning without increasing adverse events, especially in patients with left ventricular systolic dysfunction. Clinical trial registered with www.clinicaltrials.gov (NCT00473148).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".