Patients on vitamin K treatment: is switching to direct-acting oral anticoagulation cost-effective? A target trial on a prospective cohort
Bibliographic record
Abstract
AIMS: Direct-acting oral anticoagulants (DOACs) have, to a substantial degree, replaced vitamin K antagonists (VKA) as treatments for stroke prevention in atrial fibrillation (AF) patients. However, evidence on the real-world causal effects of switching patients from VKA to DOAC is lacking. We aimed to assess the empirical incremental cost-effectiveness of switching patients to DOAC compared with maintaining VKA treatment. METHODS: The target trial approach was applied to the prospective observational Swiss-AF cohort, which enrolled 2415 AF patients from 2014 to 2017. Clinical data, healthcare resource utilisation and EQ-5D-based utilities representing quality of life were collected in yearly follow-ups. Health insurance claims were available for 1024 patients (42.4%). Overall survival, quality-of-life, costs from the Swiss statutory health insurance perspective and cost-effectiveness were estimated by emulating a target trial in which patients were randomly assigned to switch to DOAC or maintain VKA treatment. RESULTS: 228 patients switching from VKA to DOAC compared with 563 patients maintaining VKA treatment had no overall survival advantage over a 5-year observation period (HR 0.99, 95% CI 0.45, 1.55). The estimated gain in quality-adjusted life years (QALYs) was 0.003 over the 5-year period at an incremental costs of CHF 23 033 (€ 20 940). The estimated incremental cost-effectiveness ratio was CHF 425 852 (€ 387 138) per QALY gained. CONCLUSIONS: Applying a causal inference method to real-world data, we could not demonstrate switching to DOACs to be cost-effective for AF patients with at least 1 year of VKA treatment. Our estimates align with results from a previous randomised trial.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".