Prediction of Bleeding Risk in Patients on Extended Oral Anticoagulation for Venous Thromboembolism
Bibliographic record
Abstract
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.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".