The Influence of Perioperative Coagulation Status on Postoperative Blood Loss in Complex Cardiac Surgery
Bibliographic record
Abstract
INTRODUCTION: Coagulopathy leading to excessive blood loss is a serious complication of cardiac surgery. In this prospective cohort study, we measured patients' coagulation status before and after cardiopulmonary bypass (CPB) and examined their relationships with postoperative blood loss. METHODS: Patients undergoing complex cardiac surgery with CPB who did not have preexisting coagulopathy were eligible. Detailed clinical and coagulation data were prospectively collected on all patients. Coagulation testing was performed before and after CPB, and included measures of thrombin generation, clotting factor consumption and dilution, clot stabilization, and fibrinolysis. The associations of variables with post-CPB blood loss (estimated loss from CPB to intensive care unit admission and 24-hour chest tube drainage) were assessed with the Spearman rank correlation test and multivariable linear regression. RESULTS: The median blood loss among the 101 study patients was 952 mL (interquartile range, 601-1553 mL). Variables independently associated with increasing blood loss were as follows: previous sternotomies (P = 0.01), lower pre-CPB prothrombin fragment F1 + 2 levels (measure of thrombin generation; P = 0.001), lower post-CPB platelet counts (P = 0.01), larger percent decrease in fibrinogen levels (P = 0.05), and higher post-CPB soluble fibrin monomer levels (measure of thrombin activity and clot stabilization; P < 0.0001) (model R(2) = 0.43). CONCLUSIONS: In complex cardiac surgery, blood loss is directly influenced by reduced pre-CPB thrombin generation rate, increased post-CPB consumption and dilution of clotting factors, as well as inadequate post-CPB clot stabilization. This information can aid in identifying patients at high risk for excessive blood loss and testing new interventions aimed at reducing the burden of this complication. The validity and generalizability of these findings need to be assessed by other studies.
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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 | 0.001 |
| 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".