Implementing a protocol to optimize blood use in a cardiac surgery service: results of a pre‐post analysis and the impact of high‐volume blood users
Bibliographic record
Abstract
BACKGROUND: Blood transfusions are a common and costly intervention for cardiac surgery patients. Evidence suggests that a more restrictive transfusion strategy may reduce costs and transfusion-related complications without increasing perioperative morbidity and mortality. STUDY DESIGN AND METHODS: A transfusion-limiting protocol was developed and implemented in a cardiovascular surgery unit. Over a 5-year period, data were collected on patient characteristics, procedures, utilization of blood products, morbidity, and mortality, and these were compared before and after the protocol was implemented. RESULTS: After the protocol was put in place, fewer patients required transfusions (38.2% vs. 45.5%, p = 0.004), with the greatest reduction observed in postoperative blood use (29.1% vs. 37.2%, p = 0.001). In-hospital morbidity and mortality did not increase. When patients who received transfusions were stratified by procedure, the protocol was most effective in reducing transfusions for patients undergoing isolated coronary artery bypass grafting (CABG; 4.09 units vs. 2.51 units, p = 0.009) and CABG plus valve surgery (10.32 units vs. 4.77 units, p = 0.014). A small group of patients were disproportionate recipients of transfusions, with approximately 6% of all patients receiving approximately half of the blood products. CONCLUSION: A protocol to limit transfusions decreased the proportion of cardiothoracic surgery patients who received blood products. A very small group of patients received a large number of transfusions, and within that group the observed mortality was significantly higher than in the general patient population. Current protocols cannot possibly account for these patients, and this should be considered when analyzing the performance of protocols designed to reduce unnecessary transfusions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".