Optimizing Warfarin Therapy in a Rural Hospital Through the Use of a Diagnostic Management Team
Bibliographic record
Abstract
ABSTRACT BACKGROUND: Warfarin is indicated for the prevention and treatment of venous thrombosis and thromboembolic complications associated with atrial fibrillation. The delivery of high-quality healthcare in a rural hospital requires the same, if not higher, focus on managing patients’ international normalized ratio (INR) within the therapeutic range. Options for warfarin management include anticoagulation clinics, in-home self-testing, pharmacist-led management, and physician-led management. However, rural hospitals are usually unable to afford specialized anticoagulation clinics to monitor patients receiving warfarin therapy. The purpose of this study is to optimize and examine the efficacy of warfarin therapy management in a rural hospital by utilizing the resources available within the hospital through the use of a diagnostic management team (DMT). DESIGN: In order to evaluate the efficacy of DMT for warfarin management in a rural hospital (Hamilton General Hospital, Hamilton, TX), we conducted a retrospective chart review to analyze the time in therapeutic range (TTR) for the target and expanded therapeutic ranges (±0.2 and ±0.3 INR units), average percentage of INR values in the target and expanded therapeutic ranges, and percentage of INR <1.5 and INR >4.5. These outcomes were compared before and after DMT implementation. RESULTS: A total of 50 patients (48% male and 52% female) underwent 205 INR measurements before DMT implementation and 247 INR measurements after DMT. The most common indication for warfarin was atrial fibrillation, followed by DVT. TTR for the target range increased from 52% pre-DMT to 64% post-DMT. TTR for the expanded therapeutic range (±0.2 INR units) increased from 64% pre-DMT to 77% post-DMT. Similarly, TTR for the expanded therapeutic range (±0.3 INR units) increased from 69% pre-DMT to 81% post-DMT. The average percentage of therapeutic INRs was 62% pre-DMT and 74% post-DMT (P < .05) for the target range, 72% pre-DMT and 86% post-DMT (P < .05) for the expanded therapeutic range (±0.2 INR units), and 76% pre-DMT and 86% post-DMT (P < .05) for the expanded therapeutic range (±0.3 INR units). The percentage of INR <1.5 decreased by 6.9% and INR >4.5 decreased by 1.6% post-DMT. CONCLUSION: Incorporating a comprehensive approach for optimizing warfarin therapy through the use of DMT and utilizing the resources available in a rural hospital improved TTR and the percentage of INRs in the therapeutic range for both target and expanded therapeutic ranges and decreased bleeding and clotting episodes as well as warfarin-related documentation. DMT may be an economically attractive alternative platform to prevent bleeding and clotting and improve treatment monitoring for patients on warfarin therapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".