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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".