Direct Acting Oral Anticoagulants Following Gastrointestinal Tract Surgery
Bibliographic record
Abstract
ABSTRACT: Direct-acting oral anticoagulants (DOACs) vary in bioavailability and sites of absorption in the gastrointestinal tract (GIT). Data on DOAC use after major GIT surgery are limited. The aim of this case series was to report the impact of surgical resection or bypass of the GIT on rivaroxaban and apixaban peak plasma concentrations. This was a case series of patients who received rivaroxaban or apixaban after GIT surgery, during the period of July 1, 2019, to December 31, 2020. Peak plasma concentrations of rivaroxaban and apixaban were assessed for the expected concentrations. Of the 27 assessed patients, 18 (66.7%) received rivaroxaban, and 9 (33.3%) received apixaban. After rivaroxaban therapy, 4 of 5 patients (80%) who underwent gastrectomy, and 3 of 3 patients (100%) who underwent duodenum and proximal jejunum exclusion had peak plasma concentrations of rivaroxaban lower than the effective range, whereas 11 of 11 patients (100%) who underwent distal bowel or ileostomy had peak rivaroxaban plasma within the effective range. After apixaban therapy, 5 of 6 patients (83.3%) who underwent total or partial gastrectomy achieved effective peak concentrations. All the patients who underwent proximal and distal bowel resection or bypass had peak concentrations of apixaban within the effective range. In conclusion, surgical resection or bypass of the upper GIT could affect DOAC absorption and subsequently peak plasma concentrations. This effect was more observed among rivaroxaban recipients. An injectable anticoagulant or vitamin K antagonist may be preferred if DOAC concentrations cannot be measured after GIT 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".