Abstract 13140: Effectiveness of Rivaroxaban among Patients Diagnosed With Pulmonary Embolism
Bibliographic record
Abstract
Introduction: Rivaroxaban is a fixed-dose anticoagulant that facilitates earlier hospital discharge, potentially reducing patient exposure to hospital-acquired complications (HAC). Due to the recent introduction of rivaroxaban and limited evidence on its impact in real world settings, we compared the effectiveness of rivaroxaban vs. standard of care (SOC) among pulmonary embolism (PE) patients in the Veterans Health Administration. Methods: Adult patients with continuous enrollment for ≥12 months before and 3 months after an inpatient diagnosis of PE between 10/1/11 and 6/30/15, and a prescription claim for an anticoagulant during the index hospitalization were included. SOC drugs were low molecular weight heparin, unfractionated heparin, and warfarin. Propensity score matching (PSM) compared PE-related outcomes (recurrent venous thromboembolism (VTE), major bleeding and death), HAC, healthcare utilization, and costs among patients receiving SOC and rivaroxaban. We defined net clinical benefit as 1 minus the combined rate of PE-related outcomes and HAC. Results: Among 6,746 PE patients, 208 received rivaroxaban and 4,641 received SOC during the index hospitalization. Most (95%) were male; 22% were African American. After 1:3 PSM, there were 203 rivaroxaban and 609 SOC patients. Mean length of stay (LOS) was 6.3 days for rivaroxaban and 10.4 days for SOC (p=0.0402). In the 90-day post-discharge period, rivaroxaban users (vs. SOC) had similar rates of PE-related outcomes (recurrent VTE: 3.0% vs. 5.3%, p=0.1793, major bleeding: 2.0% vs. 2.6%, p=0.6011, death: 2.5% vs. 4.1%, p=0.2828), fewer HAC (10.3% vs. 15.9%, p=0.0506), and fewer bacterial pneumonias (10.3% vs. 17.2%, p=0.0188). Rivaroxaban users had better net clinical benefit (82.8% vs. 71.1%, p=0.0010). Rivaroxaban users had fewer outpatient visits per patient (17.0 vs. 19.9, p=0.0005), similar rehospitalization rates (0.18 vs. 0.26, p=0.0836), lower inpatient costs ($3,501 vs. $6,189, p<0.0001), and lower total costs ($10,545 vs. $14,192, p=0.0002). When the sample was limited to low-risk PE patients, we found similar patterns. Conclusions: PE patients prescribed rivaroxaban had similar PE-related outcomes but shorter LOS, fewer HACs, and lower total costs than patients on SOC.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".