A Cost-Effectiveness Analysis of Biomarkers for Risk Prediction in Atrial Fibrillation
Bibliographic record
Abstract
RATIONALE: Atrial fibrillation (AF) is associated with an increased risk of thromboembolism. This risk is currently assessed with scoring systems based on clinical characteristics. However, these tools have limited prognostic performance. Circulating biomarkers are proposed for improved prediction of major clinical events and individualization of treatments in patients with AF. OBJECTIVE: The aim was to assess the cost-effectiveness of precision medicine (PM), i.e., the use of combined biomarkers and clinical variables, in comparison to standard of care (SOC) for risk stratification in a hypothetical cohort of AF patients at risk of stroke. METHODS: A Markov cohort model was developed to evaluate the costs and quality-adjusted life-years (QALYs) of PM compared to SOC, over 20 years using a Canadian healthcare system perspective. RESULTS: PM decreased the mean per-patient overall costs by 7% ($94,932 vs $102,057 [Canadian dollars], respectively) and increased the QALYs by 12% (8.77 vs 7.68 QALYs, respectively). The calculated incremental cost-effectiveness ratio was negative, indicating that PM is an economically dominant strategy. These results were robust to one-way and probabilistic sensitivity analyses. CONCLUSION: PM compared to SOC is economically dominant and is projected to generate cost savings.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".