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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".