Impact of COVID-19 on the Prescribing Pattern of Oral Anticoagulants for Atrial Fibrillation After Cardiac Surgery
Bibliographic record
Abstract
Background:Because of logistic challenges associated with the COVID-19 pandemic, direct oral anticoagulants (DOAC) were favored over warfarin in patients presenting postoperative atrial fibrillation (AF) after cardiac surgery in our institution. Considering the limited evidence supporting the use of DOAC in this context, we sought to evaluate the safety and efficacy of this practice change.Methods:A retrospective study was performed with patients from the Quebec City metropolitan area who were hospitalized at the Institut universitaire de cardiologie et de pneumologie de Québec-Université Laval following cardiac surgery and who required oral anticoagulant (OAC) for postoperative AF. The primary objective was to compare the pre- and peri-COVID-19 period for OAC prescribing patterns and the incidence of thrombotic and bleeding events at 3 months post-surgery. The secondary objective was to compare DOAC to warfarin in terms of thrombotic events and bleeding events.Results:A total of 233 patients were included, 142 from the pre-COVID-19 and 91 from the peri-COVID-19 period, respectively. Both groups had equivalent proportions of preoperative AF (48%) and new-onset postoperative AF (52%). The proportion of patients treated with a DOAC increased from 13% pre-COVID-19 to 82% peri-COVID-19. This change in practice was not associated with a significant difference in the incidence of thrombotic or bleeding events 3 months postoperatively. However, compared to DOAC, warfarin was associated with a higher incidence of major bleeding. Only 1 thrombotic event was reported with warfarin, and none were reported with DOAC.Conclusion:This study suggests that DOAC are an effective and safe alternative to warfarin to treat postoperative AF after cardiac surgery and that this practice can be safely maintained.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".