A prospective study of an aggressive warfarin dosing algorithm to reach and maintain INR 2 to 3 after heart valve surgery
Bibliographic record
Abstract
Good anticoagulation control in patients during the first months after heart valve surgery is important to prevent thrombotic complications. This is difficult to achieve, partly because the sensitivity to warfarin decreases progressively during approximately three months after valve surgery. A recently developed, simple but aggressive algorithm might improve anticoagulation control in this patient group. It was the objective of this study to evaluate the level of anticoagulation control when a specialised anticoagulation clinic changed from empirical dosing to the use of this new algorithm. In a before-and-after design, a cohort of consecutive patients managed with a new, aggressive dosing algorithm ('Algorithm cohort') was compared to a 'Retrospective cohort' of similar patients dosed empirically. Primary endpoint was individual time in therapeutic range (ITTR) during the first three months of warfarin therapy. Secondary endpoints included proportion of extreme International Normalised Ratio (INR) results, thrombotic and bleeding complications. Ninety-eight patients were included in the Algorithm cohort, 94 of whom were warfarin-naïve. Two hundred patients were included in the Retrospective cohort. Mean ITTR was 60.1% in the Algorithm cohort versus 48.7% in the Retrospective cohort (p <0.001). Patients in the Algorithm cohort spent 0.5% of time at an INR >5, versus 0.2 % in the Retrospective cohort. There was no major bleeding in either cohort; one patient in each cohort had a thrombotic complication. We demonstrate an improvement of the level of anticoagulation control with the use of a condition-specific, aggressive algorithm, as compared to standard dosing, in patients after heart valve surgery.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".