Blood Product Utilization in Thromboelastography-Aided Transfusion in Gastrointestinal Bleeding: A Single-Center Experience
Bibliographic record
Abstract
Background: Gastrointestinal bleeding (GIB) is a common cause for intensive care unit (ICU) admissions and is associated with high mortality rates. Effective resuscitation is essential prior to definitive procedural intervention. Thromboelastography (TEG) assesses patients' dynamic coagulation profiles and has been shown to reduce blood product usage and mortality in specific patient populations; however, its role in the management of GIB remains controversial. Methods: We performed a retrospective study of patients who had TEG performed during resuscitation of GIB in the ICU between January 1, 2017 and December 31, 2020 at a single center. Patients were identified through ICD-10 codes and blood bank's database. Results: A cohort of 244 patients was identified, of which 18 were excluded. The cohort was mainly represented by White (72%, n = 162) males (65%, n = 147) with a mean age of 61 (standard deviation (SD) 14) years. Alcoholic liver disease (31%, n = 69) and esophageal varices (30%, n = 65) were the most common comorbidities. Mean nadir systolic blood pressure was 75 (SD 18) mm Hg. Mean nadir hemoglobin concentration was 6.5 (SD 1.7) g/dL. Patients received a median of 5 packed red blood cells (pRBC) (interquartile range (IQR) 5.8), 1 fresh frozen plasma (FFP) (IQR 2), and 0 platelets and cryoprecipitate units (IQR 1 and 0, respectively). The median ICU length of stay was 3 (IQR 3) days. The observed mortality rate was 39% (n = 88). Conclusion: Although TEG may help reduce unnecessary blood product transfusions, its overall clinical benefit remains uncertain given the high mortality observed among patients with hemorrhagic shock secondary to GIB. Further studies are warranted to better evaluate the efficacy and clinical utility of TEG-guided transfusion strategies in this patient population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".