An order set and checklist improve physician transfusion ordering practices to mitigate the risk of transfusion‐associated circulatory overload
Bibliographic record
Abstract
OBJECTIVES AND BACKGROUND: There are few studies of quality interventions to mitigate the risk of transfusion-associated circulatory overload (TACO). Our aim was to reduce TACO risk in patients admitted to internal medicine at our hospital, by addressing gaps in transfusion practice. MATERIALS AND METHODS: A 3-month baseline audit of red blood cell (RBC) transfusion orders was conducted. An intervention consisting of a transfusion order set and physician checklist was developed and implemented based on identified gaps, followed by a 3-month post-intervention audit. Compliance with appropriateness criteria for RBC transfusion was ascertained, along with documentation of transfusion rate, diuretic usage and consent. RESULTS: A total of 97 transfusion orders from 68 inpatients and 95 orders from 62 inpatients were audited in the baseline and post-intervention groups, respectively. Compliance with appropriateness criteria was similar pre- and post-intervention (87 versus 85%, P = 0·81). Specification of transfusion rate improved (84 versus 98%, P < 0·01), and diuretics were appropriately ordered more frequently for patients with TACO risk factors (37 versus 64%, P < 0·01). Timing of diuretics shifted from between or post-transfusion to pre-transfusion (35 versus 86%, P < 0·01), without increases in hypokalemia or acute kidney injury. No case of TACO was observed during the study. Documentation of specific risks discussed during consent discussion improved (4 versus 23%, P < 0·01). CONCLUSION: A checklist and order set are tools that can improve the quality of transfusion orders by increasing the judicious use of pre-transfusion diuretics and augmenting the specification of transfusion rate. These interventions could be adapted to electronic order formats to improve transfusion safety.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".