Evaluation of a Novel Transfusion Algorithm Employing Point-of-care Coagulation Assays in Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Cardiac surgery requiring the use of cardiopulmonary bypass is frequently complicated by coagulopathic bleeding that, largely due to the shortcomings of conventional coagulation tests, is difficult to manage. This study evaluated a novel transfusion algorithm that uses point-of-care coagulation testing. METHODS: Consecutive patients who underwent cardiac surgery with bypass at one hospital before (January 1, 2012 to January 6, 2013) and after (January 7, 2013 to December 13, 2013) institution of an algorithm that used the results of point-of-care testing (ROTEM; Tem International GmBH, Munich, Germany; Plateletworks; Helena Laboratories, Beaumont, TX) during bypass to guide management of coagulopathy were included. Pre- and postalgorithm outcomes were compared using interrupted time-series analysis to control for secular time trends and other confounders. RESULTS: Pre- and postalgorithm groups included 1,311 and 1,170 patients, respectively. Transfusion rates for all blood products (except for cryoprecipitate, which did not change) were decreased after algorithm institution. After controlling for secular pre- and postalgorithm time trends and potential confounders, the posttransfusion odds ratios (95% CIs) for erythrocytes, platelets, and plasma were 0.50 (0.32 to 0.77), 0.22 (0.13 to 0.37), and 0.20 (0.12 to 0.34), respectively. There were no indications that the algorithm worsened any of the measured processes of care or outcomes. CONCLUSIONS: Institution of a transfusion algorithm based on point-of-care testing was associated with reduced transfusions. This suggests that the algorithm could improve the management of the many patients who develop coagulopathic bleeding after cardiac surgery. The generalizability of the findings needs to be confirmed.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".