What Influences the Cost Effectiveness of Dabigatran versus Warfarin for Stroke Prevention in Atrial Fibrillation: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: The introduction of new oral anticoagulants for the prevention of stroke in atrial fibrillation (AF) has changed the clinical management of AF. To inform decision making around dabigatran by identifying factors influencing cost-effectiveness results, we undertook a systematic review of economic evaluations of dabigatran versus warfarin for the prevention of stroke in AF patients. METHODS: A systematic literature search of Ovid Medline and Embase, Wiley's Cochrane Library, HEED, PubMed databases and grey literature was carried out for primary economic evaluations comparing dabigatran versus warfarin in patients with AF. Data on study characteristics, model inputs and results, and sensitivity analyses were abstracted and synthesized qualitatively. RESULTS: Twenty-three economic evaluations were identified and RE-LY was cited in 52% of studies as the source of the efficacy data. Twenty evaluations used Markov modelling, 2 performed discrete event simulation, and 1 was a trial-based evaluation. Eighty-two percent reported base case incremental cost-effectiveness ratios (ICERs) of less than $50,000 USD/QALY. Key variables, including international normalized ratio (INR) control, the cost of monitoring, risk of stroke and bleeding, and age were found to alter the conclusions in only a few studies. Less commonly explored factors included time horizon and cost of long-term care follow-up. CONCLUSIONS: Several factors should be considered when interpreting the results of economic analyses which are based on randomized clinical trial evidence. Real-world data are needed to further assess the clinical and economic consequences of dabigatran relative to warfarin for the prevention of stroke in AF.
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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 | 0.015 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".