Bevacizumab in combination with FOLFIRI in metastatic colorectal cancer: A retrospective study of adverse events and their correlation with clinical outcome.
Bibliographic record
Abstract
426 Background: Bevacizumab is a monoclonal antibody against the VEGF ligand that disrupts vascularization of solid tumors and is part of first-line therapy for metastatic colorectal cancer (mCRC) in Ontario. The current study is intended to assess the rate of adverse events in general practice and to explore the relationship of adverse events to clinical outcomes in patients (pts) receiving bevacizumab. Methods: Patients with mCRC from one Ontario cancer centre who received FOLFIRI with bevacizumab (BEV-FOLFIRI) as first-line treatment were retrospectively reviewed. Data collected included demographics, adverse events, radiographic response, and survival times. Results: 57 patients were included in the study with a median age of 61 years, all with ECOG ≤1, and received a median of 5.5 cycles of BEV-FOLFIRI. Median follow-up was 5.8 months. Progression-free survival (PFS) was 9.8 months (95% CI 8.2-11.0) and overall survival (OS) was 13.1 months (95% CI 12.0-15.0). The most common bevacizumab-related adverse event was proteinuria with 11 (19.3%) pts developing any grade. Other adverse events included venous thromboembolism (VTE) in 9 (15.8%) pts, hypertension (NCI-CTCAE v3) in 7 (12.3%) pts, and GI perforation in 2 (3.5%) pts. 15 (26.3%) pts had a favorable radiographic response with 40% of pts who developed significant hypertension considered responders, compared with only 25% of those without hypertension. When using NCI-CTCAE v4.03, 19 (33.3%) pts developed grade ≥2 hypertension and had significantly longer PFS (13.5 vs 8.9 months, p=0.005) and OS (16.3 vs 11.0 months, p=0.015). Finally, developing a VTE was associated with increased PFS (14.8 vs 9.6 months, p=0.012), whereas proteinuria was associated with a significantly reduced OS (6.6 vs 14.0 months, p=0.001). Conclusions: Use of NCI-CTCAE v4.03 criteria for hypertension identifies more patients with significant hypertension. Patients with hypertension according to these criteria appear to have a better treatment response, suggesting hypertension may be a useful biomarker. Conversely, new or worse proteinuria related to therapy was associated with a poor treatment response.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".