Real-World Assessment of All-Cause Hospital Readmissions among Pulmonary Embolism Patients Treated With Rivaroxaban Versus Apixaban
Bibliographic record
Abstract
Background Although hospital readmission after pulmonary embolism (PE) is common, there is limited evidence on the comparative risk of readmission between rivaroxaban and apixaban. This study compared the real-world risk of all-cause hospital readmission among patients with PE treated with rivaroxaban or apixaban. Methods This retrospective study used data from Mass General Brigham's Research Patient Data Registry (01/2013-05/2023) to identify adult patients newly initiated on rivaroxaban or apixaban during a PE-related hospitalization (discharge = index). Patients with venous thromboembolism in the 3 months prior to the index PE hospitalization were excluded. All-cause hospital readmissions at 30, 60, and 90 days post-index were assessed using Kaplan-Meier analysis and were compared between cohorts using hazard ratios (HRs), 95% confidence intervals (CIs), and p-values from Cox proportional hazards regression models. Inverse probability of treatment weighting was used to adjust for baseline confounding. Results In total, 686 rivaroxaban (mean age: 59.5; female: 50.1; Quan-Charlson comorbidity index: 1.51) and 2207 apixaban (mean age: 60.6; female: 50.8; Quan-CCI: 1.58) initiators were included. Rivaroxaban was associated with a 26% lower risk of all-cause hospital readmission at 30 days post-index (12.3% vs 16.5%; HR [95% CI]: 0.74 [0.58, 0.94]; P = .012). Risk of hospital readmission was also significantly lower at 60 days (17.0% vs 22.3%; HR [95% CI]: 0.74 [0.61, 0.91]; P = .004) and 90 days post-index (21.6% vs 25.6%; HR [95% CI]: 0.81 [0.68, 0.98]; P = .029). Conclusions Rivaroxaban was associated with significantly lower risk of all-cause hospital readmission within 90 days post-discharge from PE-related hospitalization than apixaban.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".