Budget Impact of Rivaroxaban for the Prevention of Thromboembolic Events after Hip or Knee Replacement in Spain
Bibliographic record
Abstract
Background: Anti-thrombotic prophylaxis is routinely used in patients undergoing elective total hip or knee replacement (THR or TKR) to reduce the risk of venous thromboembolism (VTE). In Spain, pharmacological prophylaxis is performed with low-molecular-weight heparin, enoxaparin being the most commonly used. Rivaroxaban is an oral antithrombotic drug that has shown superior efficacy and similar safety profile compared to enoxaparin regimens in randomized clinical trials. The aim of the study was to estimate the budget impact of increasing the use of rivaroxaban with respect to enoxaparin in the prophylaxis of VTE in patients undergoing elective THR or TKR. Methods: A budget impact analysis was conducted in order to estimate the economic cost from an increase of rivaroxaban use versus enoxaparin by 10%, 20%, and 30% over the 3 years of the time horizon (2015, 2016, and 2017) for the THR and TKR populations. Data related to rate of thromboembolic events, major bleeding events and use of resources (local or general anesthesia and nurse care after surgery) were obtained from the Xarelto® for VTE Prophylaxis After Hip or Knee Arthroplasty (XAMOS) study, an international, non-interventional, observational, open-label study in unselected patients undergoing THR or TKR surgery in routine practice. The study included a total of 17 701 patients from 252 centers in 37 countries, including Spain, Italy, France and United Kingdom, among others. Two cohorts where considered (patients undergoing THR or TKR) with two arms (patients treated with rivaroxaban or enoxaparin). The Spanish patients enrolled in the XAMOS study were 262 with THR and 538 with TKR. Thromboembolic events, major bleeding rates and health care resources were considered from both the international and the Spanish population. Health care resources including pharmacologic prophylaxis, anesthesia and nurse care costs (Euros 2014) were estimated from the Spanish National Healthcare System (NHS) perspective. The annual cost associated with each cohort was estimated based on the mean cost per patient and the estimated distribution of use of rivaroxaban or enoxaparin in the base case scenario and alternative scenario (increase of rivaroxaban use) over the 3 years. A one-way sensitivity analysis was conducted to evaluate the effect that the uncertainty of the input parameters may have on the results of the impact budget. Results: The difference in cost per patient undergoing THR or TKR with rivaroxaban versus enoxaparin was -€140.69 including event rates and resource use from the Spanish XAMOS population, and -€110.54 when considering event rates and resource use from the multinational XAMOS population (including but not limited to European [Spain, France, Italy, United Kingdom, Portugal, etc.], American [Canada, Mexico, Colombia, Venezuela, etc.], Asian [China, etc.] and Australian countries). In the analysis per cohort (THR or TKR), the impact of increasing the use of rivaroxaban in the THR cohort, was -€1106, -€2875, and -€5607 for 2015, 2016, and 2017, considering the data from the Spanish XAMOS population, and -€869, -€2259, and -€4405 considering the data from the multinational population. Considering the TKR cohort, the impact was -€2271, -€5904, and -€11 513, and -€1784, €4639, and -€9046, respectively. Conclusions: The present analysis shows that, according to effectiveness data from the XAMOS study (Spanish and multinational cohorts), an increase in the usage of rivaroxaban in VTE prophylaxis would lead to significant direct cost reduction in elective THR and TKR patients.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".