Risk of arterial (ATE) and venous thromboembolism (VTE) in a population-based cohort of bevacizumab-treated metastatic colorectal cancer (mCRC) patients.
Bibliographic record
Abstract
9624 Background: Bevacizumab potentiates the risk of ATE and VTE in cancer patients who are in a prothrombotic state. Whether there are specific factors that add to this risk and how bevacizumab-related thromboembolisms are best managed remain unclear. Our objectives were to 1) characterize the incidence of ATE and VTE in a population-based cohort of mCRC patients, 2) describe patient and treatment factors associated with thromboembolisms, and 3) examine how ATE and VTE are managed in routine practice. Methods: All patients diagnosed with mCRC from 2006 to 2008, evaluated at 1 of 5 regional cancer centers in British Columbia, and offered bevacizumab were included. Multivariate regression models were constructed to explore the associations between clinical factors and thromboembolisms. Results: A total of 541 mCRC patients were identified: 27 never started bevacizumab and 14 were lost to follow-up. Of the 500 remaining patients: median age was 61 years (IQR 53-67), 297 (59%) were men, 309 (62%) had ECOG 0/1, and 39 (8%) reported prior ATE or VTE. Median number of bevacizumab cycles was 11 (IQR 7-15). After receiving bevacizumab, 91 (18%) patients developed 12 ATE and 88 VTE, with 8 patients experiencing >1 event. Baseline characteristics, such as median age (61 vs 61 years), gender distribution (61 vs 58% men), and ECOG 0/1 (66 vs 58%) were similar between patients with and without thromboembolisms, respectively (all p>0.05). In regression models, individuals who experienced ATE or VTE were more likely to have a prior history (14 vs 6%, p=0.02), reported greater pre-existing cardiac comorbidities (42 vs 32%, p=0.05), and received a higher median number of bevacizumab cycles (13 vs 9, p<0.01), suggesting a potential dose-related effect. Following the development of ATE or VTE, management varied: bevacizumab was discontinued in 46%, held temporarily in 14%, and continued in 40% of patients. Conclusions: In this population-based cohort, the thromboembolism risk is high, especially in patients with pre-existing risk factors and those heavily treated with bevacizumab. Management of bevacizumab-related ATE and VTE appears variable, underscoring the need for guidelines.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".