Discriminating Risk of Anticoagulant-Related Bleeding in Ambulatory Cancer Patients on Thromboprophylaxis
Bibliographic record
Abstract
Introduction: Among patients taking anticoagulants, those with cancer have double the risk of major bleeding (MB). Because they also have a risk of VTE, two large randomized-control trials, AVERT and CASSINI, demonstrated effectiveness of low-dose direct oral anticoagulants (DOACs) for primary prevention of VTE in patients with cancer at high-risk for VTE based on a validated prediction model. Despite demonstrated efficacy, there is limited uptake of primary thromboprophylaxis in clinical practice. This apprehension may, in part, be due to concerns about the risk of bleeding. We aimed to determine the predictive performance of available risk prediction scores for anticoagulant-related bleeding in cancer patients randomized to thromboprophylaxis during participation in the AVERT randomized trial. Methods: Using patients in the modified intent-to-treat analysis in the AVERT randomized controlled trial, we conducted a post-hoc analysis to assess the performance of three available risk prediction scores for anticoagulant-related bleeding in 288 patients randomized to apixaban. We selected scores based on availability of candidate variables: RIETE, VTE Bleed, and Kuijer et al. scores. The primary outcome of interest was development of a MB or clinically relevant non-major bleed (CRNMB). A second analysis was conducted limiting the bleeding event to either a MB or a CRNMB that required a medical intervention. Each bleeding risk score was applied to the cohort with clinical point assignments as in the original papers (Table) . Using a Fine and Gray competing risk model, we tested the association between each score and development of first bleed following anticoagulant prescription. Patients were censored after a MB. The performance of each model was evaluated using time-dependent ROC (model discrimination) and Brier score (model calibration/discrimination). All analyses were conducted using R (4.2.3) and SAS (9.4) statistical software. Results: Between 2014 and 2018, 574 patients were randomized with 563 receiving at least one dose of study medication. A total of 288 patients received apixaban 2.5mg twice daily and 275 received placebo. The mean age was 61 years and mostly women (58.2%). Frequent cancers included: gynecologic (25.8%), lymphoma (25.3%), and pancreatic cancer (13.6%). The median duration of follow-up for the cohort was 183 days with adherence rates of 83.6% and 84.1% with apixaban and placebo respectively. There were 10 MBs and 18 CRNMBs when censoring patients at the time of first bleed. Of the 18 CRNMB events, 6 required a medical intervention. There was no significant association between each 1-point increase in the Kuijer et al. score and risk of MB+CRNMB (subdistribution hazard ratio (sHR) 0.97, p 0.85). However, for each 1-point increase in score in RIETE and VTE BLEED, there was a 72% (sHR 1.72, p <0.0001) and 26% (sHR 1.26, p 0.05) increase in risk of MB+CRNMB respectively. Discrimination of each score at 180 days was 0.69 for RIETE, 0.50 for Kuijer et al., and 0.61 for VTE BLEED. Results of calibration for each score was similar at 180 days was 0.056 for RIETE, 0.059 for Kuijer et al., and 0.058 for VTE BLEED. When limiting analyses to MB+CRNMB that required medical intervention, results remained consistent for the RIETE and Kuijer et al. scores, with a loss of association for the VTE BLEED score. Conclusions: Of the scores analyzed, the RIETE score performed best with moderate discrimination. There is a need to improve the performance of these scores. The ability to quantify risk of anticoagulant-related bleeding in patients who are candidates for primary thromboprophylaxis can allow providers and patients to make informed decisions about primary thromboprophylaxis based on the risk of VTE versus risk of anticoagulant-related bleeding.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".