Warfarin anticoagulation in hemodialysis patients with atrial fibrillation: comparison of nephrologist-led and anticoagulation clinic-led management
Bibliographic record
Abstract
BACKGROUND: There is conflicting evidence of benefit versus harm for warfarin anticoagulation in hemodialysis patients with atrial fibrillation. This equipoise may be explained by suboptimal Time in Therapeutic Range (TTR), which correlates well with thromboembolic and bleeding complications. This study aimed to compare nephrologist-led management of warfarin therapy versus that led by specialized anticoagulation clinic. METHODS: In a retrospective cohort of chronic hemodialysis patients from two institutions (Institution A: Nephrologist-led warfarin management, Institution B: Anticoagulation clinic-led warfarin management), we identified patients with atrial fibrillation who were receiving warfarin for thromboembolic prophylaxis. Mean TTRs, proportion of patients achieving TTR ≥ 60%, and frequency of INR testing were compared using a logistic regression model. RESULTS: In Institution A, 16.7% of hemodialysis patients had atrial fibrillation, of whom 36.8% were on warfarin. In Institution B, 18% of hemodialysis patients had atrial fibrillation, and 55.5% were on warfarin. The mean TTR was 61.8% (SD 14.5) in Institution A, and 60.5% (SD 15.8) in Institution B (p-value 0.95). However, the proportion of patients achieving TTR ≥ 60% was 65% versus 43.3% (Adjusted OR 2.22, CI 0.65-7.63) and mean frequency of INR testing was every 6 days versus every 13.9 days in Institutions A and B respectively. CONCLUSIONS: There was no statistical difference in mean TTR between nephrologist-led management of warfarin and that of clinic-led management. However, the former achieved a trend toward a higher proportion of patients with optimal TTR. This improved therapeutic results was associated with more frequent INR monitoring.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".