Transfusion‐associated circulatory overload prevention: a retrospective observational study of diuretic use
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Approaches to preventing transfusion-associated circulatory overload (TACO) include the use of diuretics. The purpose of this study was to determine how commonly diuretics are prescribed in patients receiving a red-blood-cell (RBC) transfusion. MATERIALS AND METHODS: This was a retrospective study of 200 adult inpatient RBC transfusion orders, 50 consecutive at each of four academic institutions. Only the first transfusion order for each patient was included. Only 1 or 2 unit orders were included. The primary outcome was the percentage of patients receiving furosemide peri-transfusion. Secondary objectives included the dose, route, and timing of furosemide and the association of clinical factors with ordering furosemide. RESULTS: The median age was 62·5 years (IQR 53, 73), and 52% were female. Peri-transfusion furosemide was ordered in 16% (95% CI 11-21%). The most common dose was 20 mg (55%), the route intravenous (90%) and timing post-transfusion (74%). At least one risk factor for TACO was present in 55% of patients: renal dysfunction (33%), older than 70 years (28%), history of congestive heart failure (18%), ejection fraction <60% (16%) and diastolic dysfunction (5%). Low haemoglobin as an indication for transfusion (OR 4·2; 95% CI 1·4-12·8) and diuretics on admission (OR 3·5; 95% CI 1·5-8·0) were associated with ordering furosemide peri-transfusion. CONCLUSIONS: Furosemide is not routinely ordered for RBC transfusion, even in patients with risk factors for TACO. Studies assessing the safety, efficacy, optimal dose, and timing of furosemide in preventing TACO are justified.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".