The Risk-Benefit Profile of Aprotinin Versus Tranexamic Acid in Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Aprotinin is superior to other antifibrinolytic drugs for preventing major blood loss after cardiac surgery but may also increase perioperative mortality. It remains unclear whether its risk-benefit profile differs among low-, moderate-, and high-risk cardiac surgical patients. METHODS: In this retrospective single-center cohort study, we included 15,365 patients who underwent cardiac surgery with cardiopulmonary bypass from 2000 to 2008. Of these, 1017 received aprotinin (6 x 10(6) U) and 14,358 received tranexamic acid (50-100 mg/kg). Propensity score methods were used to create a matched-pairs cohort (n = 1544) that adjusted for important between-group differences. The influence of patients' risk status on aprotinin's association with in-hospital mortality, morbidity, and blood loss was measured. RESULTS: In the matched set, aprotinin was only associated with increased acute kidney injury (> 50% decrease in estimated glomerular filtration or dialysis; odds ratio 1.5; 95% confidence interval [CI] 1.1-2.1). Patients' risk status significantly influenced the associations of aprotinin with mortality, acute kidney injury, and massive blood loss (transfusion of > or = 10 U of red blood cells or need for surgical reexploration). Among high-risk patients, the respective odds ratios were 0.6 (CI 0.3-1.0), 1.1 (CI 0.7-1.7), and 0.7 (CI 0.4-1.04), and among low- to moderate-risk patients, they were 1.5 (CI 0.9-2.7), 2.2 (CI 1.4-3.5), and 1.2 (CI 0.9-1.07) (Breslow-Day test for homogeneity of odds ratios between high-risk versus low- to moderate-risk patients: P < 0.05 for all 3 outcomes). CONCLUSIONS: Aprotinin tends to have a better risk-benefit profile than tranexamic acid in high-risk, but not low- to moderate-risk, patients. Its use in high-risk cases may therefore be warranted.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".