Risk of venous thromboembolism or hemorrhage among individuals with chronic kidney disease on prophylactic anticoagulant after hip or knee arthroplasty
Bibliographic record
Abstract
Chronic kidney disease (CKD) confers a high risk of thrombosis and bleeding. However, little evidence exists regarding the optimal choice of postoperative thromboprophylaxis in these patients. We conducted a population-based, retrospective cohort study among adults ≥66 years old with CKD undergoing hip or knee arthroplasty who had filled an outpatient prophylactic anticoagulant prescription between 2010 and 2020 in Ontario, Canada. The primary outcomes of venous thrombosis (VTE) and hemorrhage were identified by validated algorithms using relevant diagnoses and billing codes. Overlap-weighted cause-specific Cox proportional hazard models were used to examine the association of direct oral anticoagulants (DOAC) on the 90-day risk of VTE and hemorrhage compared with low-molecular-weight heparin (LMWH). A total of 27 645 patients were prescribed DOAC (N = 22 943) or LMWH (N = 4702) after arthroplasty. Rivaroxaban was the predominant DOAC (94.5%), while LMWH mainly included enoxaparin (67%) and dalteparin (31.5%). DOAC users had higher eGFRs, fewer co-morbidities, and surgery in more recent years compared to LMWH users. After weighing, DOAC (compared with LMWH) was associated with a lower risk of VTE (DOAC: 1.5% vs. LMWH: 2.1%, weighted hazard ratio [HR] 0.75, 95% confidence interval [CI] 0.59-0.94) and a higher risk of hemorrhage (DOAC: 1.3% vs. LMWH: 1.0%, weighted HR 1.44, 95% CI 1.04-1.99). Additional analyses including a more stringent VTE defining algorithm, different eGFR cut-offs, and limiting to rivaroxaban and enoxaparin showed consistent findings. Among elderly adults with CKD, DOAC was associated with a lower VTE risk and a higher hemorrhage risk compared to LMWH following hip or knee arthroplasty.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".