Platelet Dysfunction as Measured by a Point-of-Care Monitor is an Independent Predictor of High Blood Loss in Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Excessive bleeding carries a heavy burden of illness in cardiac surgery. Although platelet dysfunction is considered to be an important cause, it is not routinely measured. Our objective was to explore the relationship between platelet dysfunction and blood loss in cardiac surgery. METHODS: In 100 consenting patients undergoing cardiac surgery requiring cardiopulmonary bypass, platelet function was measured before, during, and after bypass with a point-of-care device that compares platelet counts before and after exposure to an agonist. Clinicians were blinded to the results of testing. Patients whose calculated blood loss was part of the highest quartile for the cohort were classified as having had high blood loss. The independent relationship between platelet function and high blood loss was measured with the aid of multivariable Poisson regression modeling (with a robust error variance) that controlled for patients' overall risk of high blood loss. RESULTS: Calculated blood loss was negatively skewed with a median of 798 mL (25th and 75th percentiles of 380 and 1775 mL). Patients whose blood loss exceeded 1770 mL were classified as having had high blood loss, and 25 patients met this criterion. There was 1 death in the high blood loss group unrelated to hemorrhage. After adjusting for bleeding risk, each 10 × 10/L increase in collagen-activated functional platelet count during rewarming and postprotamine, respectively, was associated with a relative risk of 0.89 (95% confidence interval, 0.82-0.97; P = 0.006) and 0.87 (95% confidence interval, 0.78-0.98; P = 0.02) for high blood loss. CONCLUSIONS: Platelet dysfunction, as measured by a point-of-care method during rewarming and postprotamine, is independently associated with high blood loss in cardiac surgery. Additional studies are needed to determine whether the incorporation of this assay into blood management algorithms might help rationalize blood transfusion therapy, potentially reducing blood loss and improving clinical outcomes.
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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".