Increased bleeding risk associated with concurrent vascular endothelial growth factor receptor tyrosine kinase inhibitors and low‐molecular‐weight heparin
Bibliographic record
Abstract
BACKGROUND: Some cancer patients who are diagnosed with thromboembolism may require dual treatment with vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitors (TKIs) and factor Xa inhibitors (low-molecular-weight heparin [LMWH] or direct oral anticoagulants [DOACs]). However, to the authors' knowledge, the safety of such combinations has not been well characterized. METHODS: Patients with advanced cancer who were treated with concurrent VEGFR TKIs and factor Xa inhibitors between 2010 and 2018 at The Ohio State University Comprehensive Cancer Center were included. Charts were reviewed retrospectively for clinically significant bleeding events occurring during concurrent treatment compared with those occurring during factor Xa inhibitor therapy alone, using each patient as their own control. The Fisher exact test was used to compare distribution of bleeding severities. The Cox proportional hazards model was used to compare bleeding risk between groups. RESULTS: Among 86 patients, there were 29 clinically significant bleeding events (including 8 major bleeding events) reported during concurrent treatment and 17 events (including 4 major bleeding events) reported during factor Xa inhibitor therapy alone over a median follow-up of 63 days. Concurrent treatment was associated with significantly higher risks of overall bleeding (hazard ratio, 2.45; 95% confidence interval, 1.28-4.69 [P = .007]) and first-onset bleeding (hazard ratio, 2.23; 95% confidence interval, 1.13-4.42 [P = .02]). Analysis of 6-month bleeding risk and the subgroups of patients treated with concurrent TKIs and LMWH versus LMWH alone demonstrated a similar trend. The sample size was inadequate for comparisons between treatment with concurrent TKIs and DOACs versus DOACs alone. CONCLUSIONS: Concurrent treatment with VEGFR TKIs and LMWH was found to be associated with a significantly increased risk of bleeding events when compared with LMWH therapy alone.
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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 | 0.004 |
| 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 | 0.000 |
| 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".