Diuretics decrease fluid balance in patients on invasive mechanical ventilation: the randomized-controlled single blind, IRIHS study
Bibliographic record
Abstract
Abstract Background Fluid overload has been associated with increased morbidity and mortality in critically ill patients. The goal of this study was to assess the efficacy and safety of a diuretic strategy to overcome positive fluid balance in patients on invasive mechanical ventilation. Methods Design: Multicenter, single-blind, randomized-controlled study. Patients were randomized into a diuretic (furosemide) or a control group. Patients were eligible in case of fluid overload defined as in-ICU weight increase ≥ 3%, invasive mechanical ventilation (FiO 2 ≤ 60% and PEEP ≤ 10 cm H 2 O on inclusion) and hemodynamic stabilization. The primary outcome was fluid balance, defined as weight variation from reference weight to successful extubation. The main secondary outcome was the safety of diuretic. Results 171 patients were randomized. After 5 exclusions, 166 patients were included in the analysis: 77 in the diuretic and 89 in the control group . Fluid balance was 1.4 [− 2.5 to 4.5] kg in the diuretic and 6.4 [0.5–11.2] kg in the control group ( p < 0.001). In the multiple imputation analysis, fluid balance was significantly decreased in the diuretic group (mean difference = − 4.8 95% CI [− 7.3 to − 2.5], p < 0.001). Eleven (14%) patients died in the diuretic group and 16 (18%) patients in the control group ( p = 0.5). There was a worsening of Acute Kidney Injury in 67 (75.3%) patients of the control group versus 46 (59.7%) patients in the diuretic group ( p = 0.03). Conclusions In this multicenter randomized-controlled study, protocolized diuretic therapy reduced fluid accumulation in patients receiving mechanical ventilation and was well tolerated with a favorable safety profile. Trial registration NCT 02345681, Registered January 26 2015, Prospectively registered, https://clinicaltrials.gov/ct2/show/NCT02345681?term=02345681&draw=2&rank=1 .
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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 | 0.010 |
| 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 | 0.000 |
| 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".