Thromboprophylaxis with Rivaroxaban in Acutely Ill Medical Patients with Renal Impairment: Insights from the MAGELLAN and MARINER Trials
Bibliographic record
Abstract
Patients with renal impairment are at higher risk of thrombosis and bleeding than those with normal renal function. The optimal rivaroxaban dose for thromboprophylaxis in acutely ill medical patients with renal impairment is unknown. MARINER and MAGELLAN were multicenter, randomized clinical trials of rivaroxaban in acutely ill medical patients. Efficacy and safety outcomes in patients with renal impairment in MARINER (7.5 mg once daily) were compared with those in patients with normal renal function in MARINER (10 mg once daily) and in a subpopulation of MAGELLAN that excluded patients at high risk for bleeding at baseline (10 mg once daily). Compared with enoxaparin/placebo in the MAGELLAN subpopulation, the relative risk (RR) of symptomatic venous thromboembolism (VTE) and VTE-related death with rivaroxaban 10 mg in patients with renal impairment (RR = 0.62; 95% confidence interval [CI] 0.27-1.44) was similar to that in those with normal renal function (RR = 0.78; 95% CI 0.44-1.40), while in MARINER, the 7.5 mg dose did not reduce the risk in patients with renal impairment (hazard ratio = 1.00; 95% CI 0.52-1.92). Major bleeding with rivaroxaban 10 mg once daily was higher in patients with renal impairment than in those with normal renal function in MAGELLAN (1.54% vs. 0.98%) and in the MAGELLAN subpopulation (0.94% vs. 0.61%). At a dose of 10 mg once daily, rivaroxaban is effective for thromboprophylaxis in acutely ill medical patients with impaired or normal renal function. The safety of this regimen is enhanced without loss of efficacy by excluding patients at high risk for bleeding, but not by using a reduced-dose strategy. TRIAL REGISTRATION: ClinicalTrials.gov identifiers: NCT00571649 for the MAGELLAN trial, NCT02111564 for the MARINER trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".