Evolution of and Evidence-Practice Gaps in Antithrombotic Management of Atrial Fibrillation Patients After Percutaneous Coronary Intervention
Bibliographic record
Abstract
BackgroundThe management of atrial fibrillation and flutter (AF) patients undergoing percutaneous coronary intervention (PCI) has evolved rapidly in the past decade. We determine whether the publication of the 2016 Canadian Cardiovascular Society AF guidelines were associated with a shift in practice patterns.MethodsUsing Quebec provincial administrative database information for the period from 2010-2017, a retrospective cohort of patients with inpatient or outpatient coding for AF, who subsequently underwent PCI with placement of a coronary stent, was created and analyzed for the antithrombotic regimen received in the following year. Prescribing behavior was compared among 3 time periods (2010-2011, 2012-2015, 2016-2017), and use of antithrombotics was compared to guideline-predicted therapy using the χ2 test. Predictors of oral anticoagulation (OAC) prescription were identified using adjusted logistic regression.ResultsA total of 3740 AF patients undergoing PCI were included. The proportion of OAC prescription increased over time (2010-2011 = 51.4%; 2012-2015 = 54.3%; 2016-2017 = 56.6%; P = 0.13), with a significant increase in direct OAC prescription (P < 0.01). A substantial treatment gap in OAC prescription persisted after publication of the 2016 guidelines (56.6% observed vs 89.7% predicted; P < 0.01). Previous stroke, CHADS2 score, Charlson Comorbidity Index ≥ 4, and prior use of direct OAC or warfarin were predictors of being exposed to OAC claims; previous major bleeding, and low-dose acetylsalicylic acid or P2Y12 inhibitor use were predictors of not being exposed to OACs.ConclusionExpert guidance contributed to an increase in OAC prescription following PCI, but up to 2017, substantial further changes in practice patterns would have been required to achieve the recommended rates of OAC prescription.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".