A comparison of red blood cell transfusion utilization between anti‐activated factor X and activated partial thromboplastin monitoring in patients receiving unfractionated heparin
Bibliographic record
Abstract
Essentials Anti-activated factor X (Anti-Xa) monitoring is more precise than activated partial thromboplastin (aPTT). 20 804 hospitalized cardiovascular patients monitored with Anti-Xa or aPTT were analyzed. Adjusted transfusion rates were significantly lower for patients monitored with Anti-Xa. Adoption of Anti-Xa protocols could reduce transfusions among cardiovascular patients in the US. SUMMARY: Background Anticoagulant activated factor X protein (Anti-Xa) has been shown to be a more precise monitoring tool than activated partial thromboplastin time (aPTT) for patients receiving unfractionated heparin (UFH) anticoagulation therapy. Objectives To compare red blood cell (RBC) transfusions between patients receiving UFH who are monitored with Anti-Xa and those monitored with aPTT. Patients/Methods A retrospective cohort study was conducted on patients diagnosed with acute coronary syndrome (ACS) (N = 14 822), diagnosed with ischemic stroke (STK) (N = 1568) or with a principal diagnosis of venous thromboembolism (VTE) (N = 4414) in the MedAssets data from January 2009 to December 2013. Anti-Xa and aPTT groups were identified from hospital billing details, with both brand and generic name as search criteria. Propensity score techniques were used to match Anti-Xa cases to aPTT controls. RBC transfusions were identified from hospital billing data. Multivariable logistic regression was used to identify significant drivers of transfusions. Results Anti-Xa patients had fewer RBC transfusions than aPTT patients in the ACS population (difference 17.5%; 95% confidence interval [CI] 16.4-18.7%), the STK population (difference 8.2%; 95% CI 4.4-11.9%), and the VTE population (difference 4.7%; 95% CI 3.3-6.1%). After controlling for patient age and gender, diagnostic risks (e.g. anemia, renal insufficiency, and trauma), and invasive procedures (e.g. cardiac catheterization, hemodialysis, and coronary artery bypass graft), Anti-Xa patients were less likely to have a transfusion while hospitalized for ACS (odds ratio [OR] 0.16, 95% CI 0.14-0.18), STK (OR 0.41, 95% CI 0.29-0.57), and VTE (OR 0.35, 95% CI 0.26-0.48). Conclusion Anti-Xa monitoring was associated with a significant reduction in RBC transfusions as compared with aPTT monitoring alone.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".