Comparison of Rivaroxaban and Low Molecular Weight Heparin in the Treatment of Cancer-Associated Venous Thromboembolism
Bibliographic record
Abstract
BACKGROUND: Cancer associated thrombosis (CAT) guidelines recommend direct oral anticoagulants as alternatives to low molecular weight heparin (LMWHs) in most patients. We sought to compare the effectiveness and safety of rivaroxaban versus LMWH in a broad CAT cohort. METHODS: This retrospective cohort analysis used US Optum De-Identified electronic health data from January 1, 2012 through December 31, 2020 to identify patients with any active cancer type, who were admitted to the hospital, emergency department or observation unit for venous thromboembolism (VTE) and treated with rivaroxaban or LMWH. We used propensity score overlap weighting to balance anticoagulant cohorts on baseline covariates. Hazard ratios (HRs) with 95% confidence intervals (CIs) for VTE, bleeding related hospitalization and all-cause mortality were calculated using Cox regression. RESULTS: We identified 4935 patients treated with rivaroxaban or LMWH (Table). Of these patients, 26.5% were ≥75 years of age, 55.9% were female, 19.0% had a body mass index ≥35 kg/m2 and 18.2% had a GFR <60 mL/minute at baseline. The CAT event was a pulmonary embolism ± deep vein thrombosis in 52.0% of patients, 46.3% had metastatic disease and 60% received active cancer treatment within 4 weeks of the CAT event. Cancer types included gastrointestinal (29.4%), genitourinary (26.2%), lung (24.0%), breast (19.7%) and hematologic (14.4%). At 3 months, rivaroxaban was associated with a significant 22% relative hazard reduction in recurrent VTE versus LMWH among all cancer patients. No significant difference in bleeding related hospitalization or all-cause mortality were observed at 3 months. Directionally similar results to those at 3 months were observed at 6 months for all outcomes. CONCLUSIONS: We observed less recurrent VTE and no increase in bleeding related hospitalizations associated with rivaroxaban compared to LMWH at 3 months in a broad cohort of patients with various cancer types. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".