Abstract WP171: Antithrombotic Therapy for Secondary Stroke Prevention in Patients with Severe Chronic Kidney Disease and Atrial Fibrillation
Bibliographic record
Abstract
Background: The prevalence of renal disease is increasing in the U.S. Renal dysfunction increases risk of atrial fibrillation, ischemic stroke and systemic bleeding. Lack of randomized trial data in this population has led to conflicting recommendations on management. Our objective was to review a decade of practice in utilization of antithrombotics including oral anticoagulants (OACs) for secondary stroke prevention in patients with severe renal dysfunction. Methods: We analyzed all ischemic stroke patients with atrial fibrillation and impaired renal function (creatinine clearance < 30) who were discharged on antithrombotics in the Get with the Guidelines- Stroke registry from Jan 2013 – Dec 2023. Subjects with other indication for anticoagulation such as venous thromboembolism, pulmonary embolism, and prosthetic valve were excluded. For the analysis, subjects were categorized as advanced chronic kidney disease (CKD, CrCl 15-30) and end stage renal disease (ESRD, CrCl<15). Results: Of 67,209 patients meeting inclusion criteria, 55,501 (82.6%) were classified as CKD and 11,708 (17.4%) as ESRD. Apixaban was the most utilized antithrombotic in both groups (41% in CKD and 40% in ESRD) followed by warfarin in the ESRD group (24%) and aspirin monotherapy (20%) in the CKD group. Among those on apixaban 5mg dose, 24.5% met criteria for the lower 2.5mg dose, yet were treated with the standard dose. Many subjects were discharged on the same oral anticoagulant they were taking at admission: warfarin 26%, apixaban 85%, rivaroxaban 33%, and dabigatran 58%. From 2013 to 2023, Apixaban utilization increased the most in both groups from 1.1% to 63.5% in the CKD group and 0.6% to 66.9% in the ESRD group. Aspirin monotherapy decreased from 27% to 14% in the CKD group and 25% to 14% in the ESRD group. Conclusions: Trends in OAC utilization show increasing uptake in use of OACs, particularly apixaban, over antiplatelets in the renal dysfunction population. More data is needed to determine the risk/benefit of DOAC vs Vitamin K antagonists, the role of antiplatelets, the inappropriate dosing of DOACs and the management of OAC “failure” in renal 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".