Diuretic strategies in patients with resistance to loop-diuretics in the intensive care unit: A retrospective study from the MIMIC-III database
Bibliographic record
Abstract
PURPOSE: To investigate various diuretic strategies to alleviate loop-diuretics resistance in critically ill patients. MATERIALS AND METHOD: ICU adults requiring more than 1 mg/kg/day of furosemide, from the MIMIC-III database. Four diuretic strategies were investigated: incremental dose of loop diuretics, continuous infusion, combinations with a second class of diuretics and administration of intravenous albumin. A generalized estimating equation was used to investigate the associations between these strategies and endpoints. The primary outcome was the 24-h urine output and secondary endpoints included fluid balance, weight change, electrolyte and acid-base abnormalities, kidney replacement therapy initiation, and mortality. RESULTS: A total of 7645 ICU stays from 6358 patients were included. After adjustment, the use of continuous loop-diuretic infusion was associated with a higher 24-h urine output (β: 732, 95% CI:669-795, p < 0.001), lower 24-h fluid balance (p < 0.001) and greater weight loss at 48-h (p < 0.001). Thiazide- and carbonic anhydrase inhibitor combinations were both associated with higher urine output (p < 0.001) and weight loss at 48-h (p < 0.01), while intravenous albumin was associated with fluid gain (p < 0.001). Risks of electrolyte and metabolic disturbances varied across diuretic strategies. CONCLUSIONS: Continuous loop-diuretic infusion and thiazide- or acetazolamide-loop diuretic combinations increased urine output significantly, leading to a negative fluid balance and weight loss.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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".