Application of a genomics‐guided warfarin dosing nomogram for hospitalized patients
Bibliographic record
Abstract
Adverse drug reactions (ADRs) due to warfarin are an important cause of emergency department visits, and also occur frequently among hospitalized patients. We now know common genetic variations in CYP2C9 ‐mediated metabolism of warfarin, as well as its target VKORC1 , account for much of the dosing variation. Accordingly, we hypothesized that application of a pharmacogenomics‐guided warfarin dosing algorithm that our group developed, known as WRAPID, may be clinically useful in guiding warfarin initiation and dosing for hospitalized patients. We initiated a personalized medicine‐based inpatient consult service with a goal of providing WRAPID‐based warfarin dosing recommendations. We now have data on a series of patients who were initiated on warfarin as inpatients who subsequently developed a supratherapeutic international normalized ratio (INR). When assessed by our service, most were shown to possess genotypes associated with marked sensitivity to warfarin, including one patient with the rare CYP2C9 *3/*3 genotype, as well as several with CYP2C9 *2/*2 and/or VKORC1 ‐1639 A/A genotype. Based on our WRAPID‐guided dosing, warfarin dose was accurately predicted to be as low as 0.5 mg. Accordingly, we believe genomics‐guided warfarin dosing is useful for identifying hospitalized patients at risk for ADRs, thereby reducing length of stay and preventing readmissions. Funding: AMOSO Drug Innovation Fund.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".