The Incremental Value of Thrombelastography for Prediction of Excessive Blood Loss After Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Accurate risk stratification may help reduce the burden of excessive blood loss after cardiac surgery. We measured the incremental value of thrombelastography to an existing risk prediction model for excessive blood loss in cardiac surgery. METHODS: This observational study included 434 patients who underwent cardiac surgery with cardiopulmonary bypass (CPB) and had thrombelastographic measures before and during CPB, their risk of excessive blood loss could be calculated with an existing risk prediction model and they had not received clopidogrel or warfarin within 5 days of surgery. Excessive blood loss was defined as transfusion of > or = 5 U of red blood cells from termination of CPB to 1 day after surgery. Logistic regression models including an existing risk prediction model without and with thrombelastographic measures were constructed. Improvement in risk prediction was measured by the area under the curve and net reclassification improvement. RESULTS: Excessive blood loss occurred in 59 of 434 patients (13.6%). The only thrombelastographic measure that improved risk stratification was maximum amplitude during CPB, which reflects maximum clot strength. Although the addition of this variable to the existing prediction model did not have a material effect on the area under the curve (increased from 0.780 to 0.784; P = 0.8), it did improve the net reclassification improvement by 12% (P = 0.05), primarily by improving the detection of high-risk cases. CONCLUSIONS: Risk stratification for excessive blood loss after cardiac surgery is improved when on-CPB thrombelastography is added to an existing risk prediction model that incorporates readily available patient- and surgery-related variables, but large, multicenter trials are needed to verify this finding and create a new risk prediction model.
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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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".