Diuretics and mortality in acute renal failure*
Bibliographic record
Abstract
OBJECTIVE: According to recent research, diuretics may increase mortality in acute renal failure patients. The administration of diuretics in such patients has been discouraged. Our objective was to determine the impact of diuretics on the mortality rate of critically ill patients with acute renal failure. DESIGN: Prospective, multiple-center, multinational epidemiologic study. SETTING: Intensive care units from 54 centers and 23 countries. PATIENTS: Patients were 1,743 consecutive patients who either were treated with renal replacement therapy or fulfilled predefined criteria for acute renal failure. INTERVENTIONS: Three distinct multivariate models were developed to assess the relationship between diuretic use and subsequent mortality: a) a propensity score adjusted multivariate model containing terms previously identified to be important predictors of outcome; b) a new propensity score adjusted multivariate model; and c) a multivariate model developed using standard methods, compensating for collinearity. MEASUREMENTS AND MAIN RESULTS: Approximately 70% of patients were treated with diuretics at study inclusion. Mean age was 68 and mean Simplified Acute Physiology Score II was 47. Severe sepsis/septic shock (43.8%), major surgery (39.1), low cardiac output (29.7), and hypovolemia (28.2%) were the most common conditions associated with the development of acute renal failure. Furosemide was the most common diuretic used (98.3%). Combination therapy was used in 98 patients only. In all three models, diuretic use was not associated with a significantly increased risk of mortality. CONCLUSIONS: Diuretics are commonly prescribed in critically ill patients with acute renal failure, and their use is not associated with higher mortality. There is full equipoise for a randomized controlled trial of diuretics in critically ill patients with renal dysfunction.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".