Abstract 5795: Impact of the Modified Henry Ford Warfarin Maintenance Dosing Algorithm on Quality of Anticoagulation at a Specialist Anticoagulation Clinic
Bibliographic record
Abstract
Purpose: The efficacy of warfarin depends on the time that the international normalized ratio (INR) is in the target therapeutic range. The time-in-therapeutic range (TTR) is a measure of anticoagulation quality, and should be maximized. The objective of our before-after study was to determine whether routine use of a simple manual warfarin dosing algorithm compared with ‘expertise-based’ dosing can improve the TTR for warfarin. Methods: The study was performed at a single anticoagulation clinic in Hamilton, Canada. In the ‘before’ phase (August, 2006 until September, 2007) we retrospectively calculated in patients on warfarin: agreement between warfarin dosing and the modified Henry Ford dosing algorithm and TTR, using Rosendaal’s linear interpolation method. In this period, warfarin was managed by experienced anticoagulation clinic physicians. In the ‘after’ phase (July until December, 2008) we prospectively calculated the same parameters. Differences between ‘before’ and ‘after’ were tested with the independent t-test for continuous and chi-square for dichotomous variables. Results: We included 873 patients in the ‘before’ phase and 1,088 patients in the ‘after’ phase. Before introduction of the algorithm, 71% of warfarin dose adjustments were consistent with the algorithm in patients targeting an INR of 2–3 and 56% for those targeting INR 2.5–3.5, whereas after algorithm introduction agreement increased to 90% and 81%, respectively. Introduction of the dosing algorithm significantly increased the TTR in patients targeting an INR of 2–3 from 67.2% to 73.2% (p<0.001) and in those targeting an INR of 2.5–3.5 from 49.8% to 63.8% (p<0.001). This improvement of TTR was due to a decreased proportion of subtherapeutic INRs, from 18 to 13% (p<0.01) with target INR 2–3 and from 41 to 25% (p<0.01) with target INR 2.5–3.5, while the proportion of supratherapeutic INRs did not significantly change. Conclusion: Introduction of the modified Henry Ford manual warfarin maintenance dosing algorithm in place of expertise-based dosing significantly improved the mean TTR in our tertiary care anticoagulation clinic. This widely applicable algorithm could be an inexpensive evidence-based method to improve warfarin control and thereby patient outcomes.
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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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".