Intermittent furosemide administration in patients with or at risk for acute kidney injury: Meta-analysis of randomized trials
Bibliographic record
Abstract
BACKGROUND: Furosemide is the most common loop diuretic used worldwide. The off-label administration of furosemide bolus(es) for the prevention or to reverse acute kidney injury (AKI) is widespread but not supported by available evidence. We conducted a meta-analysis of randomized trials (RCTs) to investigate whether bolus furosemide to prevent or treat AKI is detrimental on patients' survival. METHODS: Electronic databases were searched through October 2017 for RCTs comparing bolus furosemide administration versus any comparator in patients with or at risk for AKI. The primary endpoint was all-cause longest follow-up mortality. Secondary endpoints included new or worsening AKI, receipt of renal replacement therapy, length of hospital stay, and peak serum creatinine after randomization. RESULTS: A total of 28 studies randomizing 3,228 patients were included in the analysis. We found no difference in mortality between the two groups (143/892 [16%] in the furosemide group versus 141/881 [16%] in the control group; odds ratio [OR], 0.84; 95% confidence interval [CI], 0.63 to 1.13; p = 0.25). No significant differences in secondary outcomes were found. A significant improvement in survival was found in the subgroup of patients receiving furosemide bolus(es) as a preventive measure (43/613 [7.0%] versus 67/619 [10.8%], OR 0.62; 95% CI, 0.41 to 0.94; p = 0.03). CONCLUSIONS: Intermittent furosemide administration is not associated with an increased mortality in patients with or at risk for AKI, although it may reduce mortality when used as a preventive measure. Future high-quality RCTs are needed to define the role of loop diuretics in AKI prevention and management. TRIAL REGISTRATION: The study protocol was registered on PROSPERO database for systematic reviews (Registration no. CRD42017078607 - http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42017078607).
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.046 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".