How much furosemide should be administered to prevent transfusion‐associated circulatory overload? Results of a dose‐finding study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Transfusion-associated circulatory overload (TACO) is a common and life-threatening transfusion complication. Because of uncertainty regarding dosing, pre-transfusion furosemide prophylaxis is not widely endorsed. The aim of this study was to generate a furosemide dose-response curve in TACO-susceptible patients using the multiple comparisons procedure and modelling (MCP-Mod) methodology. MATERIALS AND METHODS: and diuretic therapy administered within 24 h or albumin administered within 8 h. The primary outcome measure was 6-h urine output post furosemide administration. After incorporation of age, sex, chronic diuretic use, mean arterial pressure, GFR and serum albumin as covariates of diuretic response, MCP-Mod was applied after every 50th enrolment until a weight-adjusted dose-response curve was identified with 100 mL precision. RESULTS: One-hundred forty-nine patients were enrolled. Urine output varied widely at each furosemide dose. Because of the presence of outliers and a paucity of patients receiving higher doses, only those receiving doses up to 0.6 mg/kg (n = 132) were included. After incorporating covariates, linear-log was identified as the best fitting model. Application of this formula revealed that, depending upon patient characteristics, 10-40 mg of furosemide IV would be required to achieve a diuresis volume of 400 mL, which is sufficient to offset 1 red blood cell unit. CONCLUSION: We report a novel furosemide dose-response model for TACO-susceptible patients. Once validated, this model will guide furosemide dosing for a planned controlled trial evaluating furosemide for TACO prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".