Current practice patterns and patient persistence with anticoagulant treatments for cancer‐associated thrombosis
Bibliographic record
Abstract
BACKGROUND: Recommended therapeutic options for the management of venous thromboembolism (VTE) in patients with cancer are burdensome, and compliance with guidelines is unknown. OBJECTIVES: To describe current treatment patterns and to evaluate patient persistence on various anticoagulants. PATIENTS/METHODS: Medical and pharmacy claims from the Humana Database were analyzed (01/2007-12/2014). Newly diagnosed cancer patients treated with anticoagulants were categorized into one of the following cohorts: low-molecular-weight heparin (LMWH), warfarin, and rivaroxaban. Discontinuation, switching, and persistence with the index therapy were analyzed. RESULTS: A total of 2941 newly diagnosed patients with cancer who developed VTE and received anticoagulation in outpatient settings were identified. Of these, 97% initiated anticoagulation with LMWH (n=735; 25%), warfarin (n=1403; 47.7%), or rivaroxaban (n=709; 24.1%). Median treatment durations for the LMWH, warfarin, and rivaroxaban cohorts were 3.3, 7.9, and 7.9 months, respectively; Kaplan-Meier rates of persistence to the initial therapy were 37%, 61%, and 61% at 6 months. Warfarin and rivaroxaban users were significantly more likely to remain on initial therapy compared to LMWH (adjusted hazard ratios [HRs; 95% CI]: warfarin, 0.33 [0.28-0.38]; rivaroxaban, 0.38 [0.32-0.46]). The proportion of patients that switched from their initial treatment to another anticoagulation treatment was 22.9%, 7.9%, and 4.7% in the LMWH, warfarin, and rivaroxaban cohorts, respectively. CONCLUSIONS: This real-world analysis showed that, despite guideline recommendations, warfarin and rivaroxaban are at least as equally utilized as LMWH for the treatment of cancer-associated thrombosis. LMWH was associated with significantly lower persistence, shorter duration of treatment, and more switching than warfarin and rivaroxaban.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".