Time in Therapeutic Range Using a Nomogram for Dose Adjustment of Warfarin in Patients on Hemodialysis With Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Patients treated with hemodialysis and prescribed warfarin typically have lower time in therapeutic range (TTR) compared to the general population. This may result in less benefit or increased risk of over anticoagulation in these patients. OBJECTIVE: To assess effectiveness of use of an electronic nomogram for the management of warfarin therapy in patients treated with hemodialysis. DESIGN: Retrospective chart review. SETTING: Adult patients treated with hemodialysis. PATIENTS: Patients on hemodialysis receiving warfarin for the management of atrial fibrillation (AF) with therapy managed by nursing led electronic nomogram. MEASUREMENTS: Time in therapeutic range (as fraction and Rosendaal). METHODS: Retrospective chart review over 1 year of international normalized ratio (INR) results was completed, and TTR was calculated. Comparison of patients with TTR greater than 60% to those less than 60% was completed using chi-square analysis. RESULTS: Of 43 patients with warfarin therapy managed by the nomogram, the mean TTR was 55.2% (calculated by fraction method) or 61.2% (calculated by Rosendaal method). More than half of the patients (63.5%) had moderate to good control, defined as TTR greater than 60%. Female sex, liver disease, or history of substance use and more medication holds were associated with lower TTR. LIMITATIONS: Small sample size and retrospective nature of review. CONCLUSIONS: The results of this review supports the use of an electronic, nursing-led nomogram for the maintenance management of warfarin therapy in stable patients treated with hemodialysis, as use results in TTR greater than 60% for more than half of 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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".