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Record W2596445459 · doi:10.1182/blood.v128.22.139.139

Prediction of Bleeding Risk in Patients on Extended Oral Anticoagulation for Venous Thromboembolism

2016· article· en· W2596445459 on OpenAlexaffabout
Philip S. Wells, Michael J. Kovacs, David R. Anderson, Susan R. Kahn, Clive Kearon, Sam Schulman, David Keeling, Scott Kaatz, Susan Solymoss, Daniel J. Corsi, Marc Rodger

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcGill UniversityUniversity of OttawaJewish General HospitalMcMaster UniversityLondon Health Sciences CentreOttawa HospitalDalhousie University
Fundersnot available
KeywordsMedicineMajor bleedingRisk assessmentProspective cohort studyVenous thromboembolismCohortSurgeryInternal medicineIntensive care medicineThrombosisAtrial fibrillation

Abstract

fetched live from OpenAlex

Abstract Background: While tools exist to predict the risk of major bleeding in patients on oral anticoagulation therapy (OAT) for venous thromboembolism (VTE), these have focused on the higher risk period during the first 3 months of OAT therapy (risk of major bleeding 2.4% in first 3 months vs. 2% per year thereafter), and have not been developed or evaluated for bleeding risk after the first 3 months of therapy (extended OAT). It is widely considered that a bleeding risk tool that is able to identify patients with an annual rate of major bleeding over 3% is needed, as this is the cut-point at which the risk of continued anticoagulant therapy exceeds the benefit. Aims: We sought to evaluate if the rates of major bleeding were over 3% when four existing, previously published tools (RIETE,outpatient bleeding risk index (OBRI), ACCP and HAS-BLED) classified VTE patients during extended OAT as being high risk for major bleeding, and to assess if the differences in major bleeding risk between high risk and not high risk patients were statistically significantly different. Methods: The Bleeding Risk study was a multicentre, multinational prospective cohort study of patients on extended OAT for unprovoked VTE, or provoked VTE with prior VTE, designed to generate a new prediction tool for major bleeding. Patients were enrolled after at least 3 months of OAT. All major bleeding events during long term OAT were captured and adjudicated. We applied each of the 4 tools above to determine the risk for major bleeding according to patient scores. Not all variables in these tools were collected, including drug and alcohol history, and INR control. Results: 2514 patients enrolled at 12 sites have contributed over 7000 years of observation. The mean patient age was 60.2±14.7 years, 64% were male, 92% Caucasian, average BMI was 31.3, and 9% of patients were on antiplatelet agents. Patients were followed for a mean of 2.8 years (range, 0.1 to 6.8 years. 121 patients (4.8%) experienced at least one episode of major bleeding. The annual rate of bleeding was 1.7 per 100 patient years of observation. The proportion of patients classified as high risk of major bleeding by the RIETE (score of 3 or 4), OBRI (score of 3), ACCP (score ≥ 2) and HAS-BLED (score ≥ 3) scores were 12.3%, 13.3%, 28.0% and 23.1%, with major bleeding rates of 3.9%, 3.2%, 3.4% and 3.4% per year, respectively. The major bleeding rates of patients who were classified as not high risk of bleeding were 1.4%%, 1.5%, 1.1% and 1.2% per year, respectively. All differences were statistically significant with p values < 0.0001. Conclusion: Despite the potential to underestimate risk due to missing variables, all currently available prediction tools are able to identify patients with a 3% or higher risk of major bleeding per year. , The HAS-BLED and ACCP scores were able to identify the highest proportions of patients as high risk for major bleeding. Disclosures Wells: BMS/Pfizer: Research Funding; Bayer Healthcare: Other: Speaker Fees and Advisory Board; Janssen Pharmaceuticals: Consultancy; Itreas: Other: Served on a Writing Committee. Kovacs:Bayer: Honoraria, Research Funding; LEO Pharma: Honoraria; Daiichi Sankyo Pharma: Research Funding; Pfizer: Honoraria, Research Funding. Anderson:Bayer Healthcare: Research Funding. Rodger:Boehringer Ingelheim: Research Funding; Canadian Agency for Drugs and Technologies in Health: Consultancy.

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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.024
GPT teacher head0.260
Teacher spread0.236 · 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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Citations4
Published2016
Admission routes2
Has abstractyes

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