Association between isolated abnormal coagulation profile on transfusion following major surgery: A <scp>NSQIP</scp> analysis of individuals without bleeding disorders
Bibliographic record
Abstract
INTRODUCTION: Preoperative coagulation screening for patients without bleeding disorders remains controversial. The combinatorial risk of INR, aPTT, and platelet count (PLT) abnormalities leading to bleeding requiring transfusion is not known in these patients. We examined the association between abnormal coagulation profile and the risk of transfusion following common elective surgery in patients without bleeding disorders. STUDY DESIGN AND METHODS: We utilized the National Surgical Quality Improvement Program (NSQIP) database from 2004 to 2018 to identify patients without a history of bleeding disorders undergoing common 23 major elective procedures across 10 specialties. Multivariable logistic regression was used to assess the association between coagulation profile and bleeding requiring packed red blood cell transfusion intra-/post-operatively. RESULTS: Of the 672,075 patients meeting inclusion criteria, 53.7% presented with normal coagulation profile preoperatively. Overall, 12.2% (n = 82,368) received transfusion. In the setting of normal aPTT/PLT, both Equivocal INR of 1.1-1.5 (aOR 1.41, 95% CI 1.38-1.44) and Abnormal INR of >1.5 (aOR 1.81, 95% CI 1.71-1.93) were significantly associated with an increased risk of transfusion. Equivocal (60-70) and Abnormal (>70) aPTT with normal INR/PLT did not demonstrate a comparable risk of transfusion. We observed a synergistic effect of combinatorial lab abnormalities on the risk of transfusion when both Abnormal INR/aPTT and Low PLT of <100,000 were present (aOR 5.18, 95% CI 3.04-8.84), compared to the effect of Abnormal INR/aPTT and normal/elevated PLT (aOR 1.90, 95% CI 1.48-2.45). DISCUSSION: The preoperative presence of abnormal findings in INR or PLT was significantly associated with the risk of bleeding requiring transfusion during intraoperative and postoperative periods.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".