Thromboelastography and rotational thromboelastometry for the surgical intensivist: A narrative review
Bibliographic record
Abstract
BACKGROUND: Viscoelastic tests (VETs), specifically thromboelastography (TEG) and rotational thromboelastometry (ROTEM), are gaining popularity in the management of critically ill surgical patients with hemorrhage or thrombosis due to their comprehensive characterization of the coagulation process and point-of-care availability in comparison to conventional coagulation tests (CCTs). We review current evidence for VET use in patients in the surgical intensive care unit (SICU). METHODS: We searched PUBMED, EMBASE and the Cochrane Library through May 30, 2018 for articles that evaluated the use of VETs in patient populations and clinical scenarios germane to the surgical intensivist. Individual articles were critically evaluated for relevance and appropriate methodology using a structured technique. Information on patient characteristics, timing and methods of CCTs/VETs, and outcomes was collected and summarized in narrative form. RESULTS: Of 2,589 identified articles, 36 were included. Five (14%) were interventional studies and 31 (86%) were observational. Twenty-five (69%) evaluated TEG, 11 (31%) ROTEM and 18 (50%) CCTs. Investigated outcomes included quantitative blood loss (13 (36%)), blood product transfusion (9 (25%)), thromboembolic events (9 (25%)) and mortality (6 (17%)). We identified 12 clinical scenarios with sufficient available evidence, much of which was of limited quantity and poor methodological quality. Nonetheless, research supports the use of VETs for guiding early blood product administration in severe traumatic hemorrhage and for the prediction of abstract excess bleeding following routine cardiac surgery. In contrast, evidence suggests VET-based heparin dosing strategies for venous thromboembolism prophylaxis are not superior to standard dosing in SICU patients. CONCLUSION: While VETs have the potential to impact the care of critically ill surgical patients in many ways, current evidence for their use is limited, mainly because of poor methodological quality of most available studies. Further high-quality research, including several ongoing randomized controlled trials, is needed to elucidate the role of TEG/ROTEM in the SICU population. LEVEL OF EVIDENCE: Systematic review, level IV.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".