A Systematic Review of Viscoelastic Testing in Patients with Subarachnoid Hemorrhage
Bibliographic record
Abstract
OBJECTIVE: Bleeding and thromboembolic complications frequently occur after subarachnoid hemorrhage (SAH) and substantially contribute to poor outcome. Viscoelastic testing could be used for detection of coagulopathies after SAH. This review summarizes literature on the usefulness of viscoelastic testing to detect coagulopathy in patients with SAH and explores whether viscoelastic parameters are associated with SAH-related complications and clinical outcome. METHODS: PubMed, Embase, and Google Scholar were systematically searched on August 18, 2022. Two authors independently selected studies that reported viscoelastic testing in patients with SAH and assessed the quality of studies using the Newcastle-Ottawa Scale or a previously reported framework for quality assessment. Data were meta-analyzed if methodologically possible. RESULTS: The search yielded 19 studies (1160 patients with SAH). Pooling of data including all relevant studies was not possible for any of the outcome measurements because of methodological differences. Thirteen of 19 studies evaluated the association of coagulation profiles and SAH, of which 11 studies showed a hypercoagulable profile. Rebleeding was associated with platelet dysfunction, deep venous thrombosis was associated with faster clot initiation, and both delayed cerebral ischemia and poor outcome were associated with increased clot strength. CONCLUSIONS: This explorative review shows that patients with SAH frequently have a hypercoagulable profile. Thromboelastography (TEG) and rotational thromboelastometry (ROTEM) parameters are associated with rebleeding, delayed cerebral ischemia, deep venous thrombosis, and poor clinical outcome after SAH; however, more research on the subject is needed. Future studies should focus on determining the optimal time frame and cutoff values for TEG or ROTEM to predict these complications.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".