Anticoagulation Therapy for Atrial Fibrillation in Patients With Alzheimer’s Disease
Bibliographic record
Abstract
Background and Purpose- Direct oral anticoagulants (DOACs) are safer, at least equally efficacious, and cost-effective compared to warfarin for stroke prevention in atrial fibrillation (AF) but they remain underused, particularly in demented patients. We estimated the cost-effectiveness of DOACs compared with warfarin in patients with AF and Alzheimer's disease (AD). Methods- We constructed a microsimulation model to estimate the lifetime costs, quality-adjusted life-years (QALYs), and cost-effectiveness of anticoagulation therapy (adjusted-dose warfarin and various DOACs) in 70-year-old patients with AF and AD from a US societal perspective. We stratified patient cohorts based on stage of AD and care setting. Model parameters were estimated from secondary sources. Health benefits were measured in the number of acute health events, life-years, and QALYs gained. We classified alternatives as cost-effective using a willingness-to-pay threshold of $100 000 per QALY gained. Results- For patients with AF and AD, compared with warfarin, DOACs increase costs but also increase QALYs by reducing the risk of stroke. For mild-AD patients living in the community, edoxaban increased lifetime costs by $6603 and increased QALYs by 0.076 compared to warfarin, yielding an incremental cost-effectiveness ratio of $86 882/QALY gained. Even though DOACs increased QALYs compared with warfarin for all patient groups (ranging from 0.019 to 0.085 additional QALYs), no DOAC treatment alternative had an incremental cost-effectiveness ratio <$150 000/QALY gained for patients with moderate to severe AD. For patients living in a long-term care facility with mild AD, the DOAC with the lowest incremental cost-effectiveness ratio (rivaroxaban) costs $150 169 per QALY gained; for patients with more severe AD, the incremental cost-effectiveness ratios were higher. Conclusions- For patients with AF and mild AD living in the community, edoxaban is cost-effective compared with warfarin. Even though patients with moderate and severe AD living in the community and patients with any stage of AD living in a long-term care setting may obtain positive clinical benefits from anticoagulation treatment, DOACs are not cost-effective compared with warfarin for these populations. Compared to aspirin, no oral anticoagulation (warfarin or any DOAC) is cost effective in patients with AF and AD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".