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Record W2511044399 · doi:10.1111/jth.13476

A comparison of red blood cell transfusion utilization between anti‐activated factor X and activated partial thromboplastin monitoring in patients receiving unfractionated heparin

2016· article· en· W2511044399 on OpenAlexfundno aff
Kathy Belk, Michael Laposata, Christopher Craver

Bibliographic record

VenueJournal of Thrombosis and Haemostasis · 2016
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsnot available
FundersMcMaster University
KeywordsPartial thromboplastin timeMedicineProthrombin timePopulationHeparinConfidence intervalRetrospective cohort studyInternal medicineAnticoagulantPlatelet

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.087
GPT teacher head0.348
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations16
Published2016
Admission routes1
Has abstractno

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