Development of a specific algorithm to guide haemostatic therapy in children undergoing cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Although rotational thromboelastometry (ROTEM) is increasingly used to guide haemostatic therapy in a bleeding patient, there is a paucity of data guiding its use in the paediatric population. OBJECTIVE: The objective of this study is to develop an algorithm on the basis of ROTEM values obtained in our paediatric cardiac population to guide the management of the bleeding child. DESIGN: A retrospective analysis. SETTING: Department of Anaesthesiology, Queen Fabiola Children's University Hospital. Data were collected between September 2010 and January 2012. PATIENTS: All children who underwent elective cardiac surgery requiring cardiopulmonary bypass (CPB) were reviewed. INTERVENTION: None. MAIN OUTCOME MEASURES: Significant postoperative bleeding was defined as blood loss more than 10% of the child's estimated blood volume within the first six postoperative hours, dividing our population according to high blood loss (HBL) or low blood loss (LBL). Factors independently associated with postoperative bleeding determined the bleeding probability. Receiving operating characteristics (ROC) curves were constructed with the aim of determining relevant ROTEM parameters (including clot amplitude 10 min after administration of protamine [A10]) to be used in our algorithm. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were determined for the developed algorithm. RESULTS: One-hundred and fifty children were included in our study. Univariate and multivariate logistic regression analysis revealed that preoperative weight (kg), presence of a cyanotic disease (yes/no) and wound closure duration (min) were independent predictors of postoperative bleeding. Analysis of our ROTEM parameters revealed that clotting time (CT) ≥ 111 s, A10 ≤ 38 mm measured on the EXTEM and A10 ≤ 3 mm obtained on the FIBTEM tests were the three relevant parameters to guide haemostatic therapy. If the ROTEM-based algorithm was applied according to the bleeding risk (n = 65), 27 out of 29 of the HBL and 24 out of 36 of the LBL group would have been treated. CONCLUSION: This study describes an algorithm starting with the detection of abnormal bleeding in which ROTEM could be used to guide haemostatic therapy in bleeding children after CPB. Further studies are needed to test the efficacy of this specific algorithm-based approach.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".