A propensity score case‐control comparison of aprotinin and tranexamic acid in high‐transfusion‐risk cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Cardiac surgery with cardiopulmonary bypass may result in excessive fibrinolysis and platelet (PLT) dysfunction, resulting in impaired hemostasis and excessive blood loss. Prophylactic use of the antifibrinolytic drugs aprotinin and tranexamic acid is thought to prevent these hemostatic defects. Their relative clinical utility and safety in high-transfusion-risk cardiac surgery, however, is not known. STUDY DESIGN AND METHODS: Using propensity scores, 449 patients who received aprotinin for high-transfusion-risk cardiac surgery were matched to 449 patients who received tranexamic acid from a pool of 10,870 consecutive patients who underwent cardiac surgery at a single center, 586 of whom received aprotinin and the remainder of whom received tranexamic acid. RESULTS: The two matched groups were well balanced in terms of measured perioperative variables. Blood product transfusion rates were similar in the aprotinin and tranexamic acid groups: red blood cells, 79 percent versus 76 percent (p = 0.3); PLTs, 56 percent versus 50 percent (p = 0.06); and plasma, 66 percent versus 61 percent (p = 0.1). Adverse events rates were comparable in the two groups, except for renal dysfunction (defined as a greater than 50% increase in creatinine concentration during the first postoperative week to >100 micromol/L in women and >110 micromol/L in men or a new requirement for dialysis support), which occurred in 24 percent (107/449) of aprotinin patients and 17 percent (75/449) of tranexamic acid patients (p = 0.01). CONCLUSIONS: Aprotinin and tranexamic acid have similar hemostatic effectiveness in high-transfusion-risk cardiac surgery. Within the confines of propensity score matching, our results suggest that aprotinin may be associated with renal dysfunction.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".