Major transfusions remain frequent despite the generalized use of tranexamic acid: an audit of 3322 patients undergoing cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Aprotinin has been reapproved for use in Europe and in Canada. We sought to determine if its reintroduction was still pertinent given the widespread administration of tranexamic acid, another antifibrinolytic shown to reduce bleeding and transfusions. STUDY DESIGN AND METHODS: After institutional review board approval, we examined the cardiac surgery database (2012-2015; 3322 patients). Major transfusion was defined as 4 or more red blood cell units. A stratified multivariate logistic regression analysis identified predictors of major transfusion; 1064 patients were matched by propensity score to compare outcomes of patients with or without major transfusion. RESULTS: Cardiopulmonary bypass (CPB) was used in 2342 patients; 98.9% received tranexamic acid versus 15.2% (149/980) in off-pump coronary artery bypass graft patients. Major transfusion was required in 758 patients (23%). Age, low body mass index, low preoperative hemoglobin or platelet count, recent use of P2Y12 receptor blockers, chronic kidney disease, NYHA functional class, left ventricular ejection fraction of less than 30%, prior cardiac surgery, urgency, type of cardiac surgery, and duration of CPB were all independent predictors of major transfusions (all p < 0.05). Major transfusion was associated with a more than threefold increase in mortality (7.1% vs. 2.1%; p < 0.001) and increases in major adverse events (p < 0.001). CONCLUSIONS: Despite the use of tranexamic acid, 23% of cardiac surgery patients require a major transfusion. We identified predictors of major transfusion and showed that major transfusion is associated with important increases in mortality and morbidity. We conclude that there is still a need for an effective and safe blood-sparing drug in cardiac surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".