Cost-effectiveness of self-managed versus physician-managed oral anticoagulation therapy
Bibliographic record
Abstract
BACKGROUND: Patient self-management of long-term oral anticoagulation therapy is an effective strategy in a number of clinical situations, but it is currently not a funded option in the Canadian health care system. We sought to compare the incremental cost and health benefits of self-management with those of physician management from the perspective of the Canadian health care payer over a 5-year period. METHODS: We developed a Bayesian Markov model comparing the costs and quality-adjusted life years (QALYs) accrued to patients receiving oral anticoagulation therapy through self-management or physician management for atrial fibrillation or for a mechanical heart valve. Five health states were defined: no events, minor hemorrhagic events, major hemorrhagic events, thrombotic events and death. Data from published literature were used for transition probabilities. Canadian 2003 costs were used, and utility estimates were obtained from various published sources. RESULTS: Self-management resulted in 3.50 fewer thrombotic events, 0.78 fewer major hemorrhagic events and 0.12 fewer deaths per 100 patients than physician management. The average discounted incremental cost of self-management over physician management was found to be 989 dollars (95% confidence interval [CI] 310 dollars-1655 dollars) per patient and the incremental QALYs gained was 0.07 (95% CI 0.06-0.08). The cost-effectiveness of self-management was 14,129 dollars per QALY gained. There was a 95% chance that self-management would be cost-effective at a willingness to pay of 23,800 dollars per QALY. Results were robust in probabilistic and deterministic sensitivity analyses. INTERPRETATION: This model suggests that self-management is a cost-effective strategy for those receiving long-term oral anticoagulation therapy for atrial fibrillation or for a mechanical heart valve.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".