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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".