Cost-effectiveness of antithrombotic agents for atrial fibrillation in older adults at risk for falls: a mathematical modelling study
Bibliographic record
Abstract
BACKGROUND: Antithrombotic drugs decrease stroke risk in patients with atrial fibrillation, but they increase bleeding risk, particularly in older adults at high risk for falls. We aimed to determine the most cost-effective antithrombotic therapy in older adults with atrial fibrillation who are at high risk for falls. METHODS: We conducted a mathematical modelling study from July 2019 to March 2020 based on the Ontario, Canada, health care system. We derived the base-case age, sex and fall risk distribution from a published cohort of older adults at risk for falls, and the bleeding and stroke risk parameters from an atrial fibrillation trial population. Using a probabilistic microsimulation Markov decision model, we calculated quality-adjusted life years (QALYs), total cost and incremental cost-effectiveness ratios (ICERs) for each of acetylsalicylic acid (ASA), warfarin, apixaban, dabigatran, rivaroxaban and edoxaban. Cost data were adjusted for inflation to 2018 values. The analysis used the Ontario public payer perspective with a lifetime horizon. RESULTS: In our model, the most cost-effective antithrombotic therapy for atrial fibrillation in older patients at risk for falls was apixaban, with an ICER of $8517 per QALY gained (5.86 QALYs at $92 056) over ASA. It was a dominant strategy over warfarin and the other antithrombotic agents. There was moderate uncertainty in cost-effectiveness ranking, with apixaban as the preferred choice in 66% of model iterations (given willingness to pay of $50 000 per QALY gained); edoxaban, 30 mg, was preferred in 31% of iterations. Sensitivity analysis across ranges of age, bleeding risk and fall risk still favoured apixaban over the other medications. INTERPRETATION: From a public payer perspective, apixaban is the most cost-effective antithrombotic agent in older adults at high risk for falls. Health care funders should implement strategies to encourage use of the most cost-effective medication in this population.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".