Abstract 18061: Supplementation to Treat Antithrombin Deficiency Improves Sensitivity to Heparin, Anticoagulation and Decreased Thrombogenecity in Neonates and Infants Undergoing Cardiac Surgery With Cardiopulmonary Bypass
Bibliographic record
Abstract
Introduction: Preoperative antithrombin (AT) deficiency is common in infants undergoing cardiac surgery with cardiopulmonary bypass (CPB), and is associated with heparin resistance, difficulties achieving optimal intraoperative anticoagulation and post-operative thrombosis. Methods: We performed a pilot randomized trial of pooled human AT supplementation for children <1 year with preoperative AT <0.85U/ml. Subjects received a split (patient and CPB prime) dose of AT before heparinization. AT was dosed to reach a predicted AT activity of 1.2U/ml during CPB calculated from preoperative AT level and patient weight. Results: We randomized 18 subjects; 17 completed the study (9 controls, 8 AT). Mean preoperative AT level was similar between groups (Control: 0.65±0.10 vs. AT: 0.68±0.15 U/mL, p=0.66). AT group subjects had higher AT than controls immediately after start of CPB (Control: 0.44±0.10 vs. AT: 1.15±0.20 U/mL, p<0.001) and at the end of CPB (Control: 0.67±0.14 vs. AT: 1.19±0.16 U/mL, p<0.001). AT supplementation was associated with increased activated clotting time (ACT) post-heparinization (Control: 500±72 vs. AT: 639±172 seconds, p=0.02), increased heparin sensitivity (Control: 89±23 vs. AT: 114±22 ACT seconds per 100U/kg heparin, p=0.02), and decreased heparin dose given during CPB (Control: 1126±344 vs. AT: 845±165 U/kg, p=0.03). After primary heparinization, 56% of control subjects did not achieve ACT target vs. 25% of AT subjects. At the end of CPB, AT group subjects had lower prothrombin levels (Control: 746±372 vs. AT: 460±102 ng/mL, p=0.02) and lower levels of inflammatory cytokines (IL-1α, IL-1β, IL-6, IL-8 and TNFα). Supplementation with AT was not associated with increased chest tube volume loss or blood product requirements. One subject in each group had severe bleeding, and one in each group had post-operative infection. Thrombosis was noted for 3 controls and 1 AT subjects. Conclusions: AT supplementation to treat preoperative AT deficiency in young children is associated with decreased heparin resistance, improved anticoagulation and decreased thrombogenecity. Safety profile regarding bleeding and infections appears favorable. Equipoise exists for a larger definitive outcome trial.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".