Systematic Review to Identify Validated Manual Warfarin Maintenance Dosing Tools.
Bibliographic record
Abstract
Abstract Abstract 3800 Rationale: Vitamin K antagonists, of which warfarin is the most widely used in North America, are some of the most difficult medications to control and require careful monitoring to keep the international normalized ratio (INR) in the therapeutic range. Anticoagulation clinics and computer-assisted decision support systems have been associated with an improved time patients spend in the therapeutic INR range, but these systems can be expensive. Also, patient self-management delivers the best INR control, but is only useful for patients who are capable to perform self testing and management. Simple, inexpensive and easy to use dosing algorithms have the potential to overcome some of these logistical barriers and offer a tool that could be used without the infrastructure of a formal anticoagulation clinic. We performed a systematic review to identify validated manual warfarin maintenance dosing algorithms that do not require computer support. Methods: MEDLINE was searched, without language restriction, by two independent reviewers for observational and experimental reports of warfarin dosing algorithms, nomograms or formulas. Studies that reported efficacy of anticoagulation clinics, patient self management and computer assisted warfarin dosing were reviewed for references for an underlying dosing process. Inclusion criteria for studies for this review were: 1) the tool needed to provide advice on maintenance dose adjustment and next INR testing, 2) the manual dosing tool needed to be compared to a control group, 3) time in therapeutic range or patient outcomes were reported, 4) the effect of the dosing method could be separated from other interventional aspects of warfarin management. Inter-rater agreement for inclusion of candidate studies was measured with the kappa statistic and disagreement was resolved by consensus. Results: Twenty-five studies were identified and 23 either did not report a manually useful tool (computer-based algorithm or complex formula), did not have a control comparator, did not report the time in therapeutic range or patient outcomes, or the effect of the tool could not be separated from other interventional aspects of anticoagulation clinics, computer systems or self-management. Only 2 studies fulfilled all of the inclusion criteria and there was 100% agreement between the two independent reviewers for their selection. Both studies were single center studies and used practice performance before implementation of the dosing tool as the comparator. One study (n=72) showed an improvement in the proportion of INRs in the therapeutic range from 32% to 46% (p < 0.05). The other study (n=1961) showed an improvement in the time in therapeutic range in patients with a target INR range of 2–3 from 67% to 73% (p < 0.001) and in patients with a range of 2.5–3.5, form 50% to 64% (p < 0.001). Data of the two studies were not pooled due to differences in the dosing tool, quality level of care and calculation of the primary outcome, and the negligible effect of the smaller study. Conclusion: We identified only 2 manually useful VKA maintenance dosing tools that have been compared with a control group. Both studies showed an improvement in the quality of INR control with a simple dosing algorithm, but the studies were limited by their pre/post interventional design. Our results stress the need for a randomized trial to validate the usefulness of a manual dosing algorithm which could yield a simple and inexpensive evidence-based method for many physicians managing patients taking a vitamin K antagonist. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.020 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".