Abstract 150: Antithrombotic Use in Nonvalvular Atrial Fibrillation (NVAF): Alignment between Guidelines and Emerging Evidence with Clinician Prescribing Preferences
Bibliographic record
Abstract
Background: 2014 AHA/ACC/HRS guidelines recommend anticoagulation for NVAF patients with a CHA2DS2-VASc≥2, but do not endorse a specific therapy. Several published indirect treatment comparisons demonstrate similar stroke risk reduction but distinct differences for bleeding risk among novel oral anticoagulants (NOACs) in NVAF patients. Objectives: Survey physicians to determine how their preferences over antithrombotic therapies compare with current treatment guidelines and indirect treatment comparisons. Methods: An online survey was completed by 200 physicians who regularly treat patients with NVAF. Respondents answered 12 questions comparing two hypothetical antithrombotic treatments that varied across five attributes: stroke risk, major bleeding risk, inconvenience (i.e., regular INR blood-testing/dietary restrictions), dosing frequency and patient out-of-pocket (OOP) cost. Physician willingness to trade higher OOP cost for improvements in other attributes was estimated using a logistic regression. Based on these results, we calculated the share of prescriptions that would be written for apixaban, aspirin, dabigatran, rivaroxaban and warfarin using real-world US patient OOP costs. Results: Physicians were willing to trade an increase in monthly OOP cost of $38.21 (95% CI: $22.07-$54.34) for a 1 percentage point (absolute) decrease in annual stroke risk. Physicians also placed a positive value on less inconvenience ($34.46, 95% CI: $8.50-$60.41), and a 1 percentage point reduction in the risk of a major bleed ($14.44, 95% CI: $8.01-$20.88). Physicians did not have a significant willingness to pay to reduce dosing frequency from twice to once per day ($17.16; 95% CI: -0.08-$34.40). Cardiologists and cardiac electrophysiologists had higher willingness to pay for stroke risk reduction than general practitioners ($54.32 vs. $24.74, p<0.001). Based on these preferences, physicians would recommend NOACs to 77% of patients, with apixaban (32%) being the preferred NOAC. Conclusions: Similar to findings from indirect treatment comparison studies, physicians largely prefer NOACs_particularly apixaban_compared to warfarin or aspirin for stroke risk reduction in NVAF patients. Additional research is needed to determine why NOACs are underused in practice.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".