Predictors of Direct Oral Anticoagulants Utilization for Thromboembolism Prevention in Atrial Fibrillation
Bibliographic record
Abstract
PURPOSE: Several factors have been associated with the prescription of direct oral anticoagulants (DOAC) over warfarin such as younger age, fewer concomitant medications, and lower CHADS2 or bleeding scores. The primary objective of this study was to identify predictors of DOAC choice compared with warfarin for patients who are starting a new oral anticoagulant (OAC) for atrial fibrillation (AF). The secondary objective was to describe the proportion of DOAC prescriptions in new users of OAC for AF. METHODS: A retrospective cross-sectional study was conducted in a teaching hospital in Canada. Medical records of adult patients hospitalized in any medical units between October 1st, 2011 and October 1st, 2014, who were newly prescribed an OAC for non valvular AF were systematically reviewed. Baseline characteristics of warfarin and DOAC users were compared and a multivariate logistic regression analysis was completed to identify predictors of DOAC use. Variables included in the multiple regression analysis were: age, hypertension, diabetes, history of stroke or transient ischemic attack, coronary artery disease, peripheral arterial disease, CHADS2 score of 2 or more, creatinine clearance 30mL/min or more, polypharmacy, concomitant use of ASA or clopidogrel, and prescription by a neurologist. RESULTS: Among OAC users (144 patients on DOAC and 295 patients on warfarin), older age (odds ratio [OR] 0.97; 95%CI 0.95-0.98), peripheral arterial disease (OR: O.41;95%CI: 0.21-0.82), polypharmacy (OR: 0.30;95%CI:0.10-0.89), and concomitant use of clopidogrel (OR: 0.19;95%CI:0.07-0.56) decreased the probability of DOAC use. Prescription by a neurologist (OR: 2.77;95%CI:1.34-5.76) and an estimated creatinine clearance of at least 30mL/min (OR: 3.53;95%CI:1.18-10.57) increased the likelihood of DOAC prescription. CONCLUSION: To the best of our knowledge, this is the first observational study finding that concomitant use of clopidogrel reduced the likelihood of DOAC utilization while prescription by a neurologist increased the probability of receiving a DOAC over warfarin in patients with AF.This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.003 |
| 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".