Economic evaluation of extended electrocardiogram monitoring for atrial fibrillation in patients with cryptogenic stroke
Bibliographic record
Abstract
BACKGROUND: Timely identification of occult atrial fibrillation following cryptogenic stroke facilitates consideration of oral anticoagulation therapy. Extended electrocardiography monitoring beyond 24 to 48 h Holter monitoring improves atrial fibrillation detection rates, yet uncertainty remains due to upfront costs and the projected long-term benefit. We sought to determine the cost-effectiveness of three electrocardiography monitoring strategies in detecting atrial fibrillation after cryptogenic stroke. METHODS: A decision-analytic Markov model was used to project the costs and outcomes of three different electrocardiography monitoring strategies (i.e. 30-day electrocardiography monitoring, three-year implantable loop recorder monitoring, and conventional Holter monitoring) in acute stroke survivors without previously documented atrial fibrillation. RESULTS: The lifetime discounted costs and quality-adjusted life years were $206,385 and 7.77 quality-adjusted life years for conventional monitoring, $207,080 and 7.79 quality-adjusted life years for 30-day extended electrocardiography monitoring, and $210,728 and 7.88 quality-adjusted life years for the implantable loop recorder strategy. Additional quality-adjusted life years could be attained at a more favorable incremental cost per quality-adjusted life year with the implantable loop recorder strategy, compared with the 30-day electrocardiography monitoring strategy, thereby eliminating the 30-day strategy by extended dominance. The implantable loop recorder strategy was associated with an incremental cost per quality-adjusted life year gained of $40,796 compared with conventional monitoring. One-way sensitivity analyses indicated that the model was most sensitive to the rate of recurrent ischemic stroke. CONCLUSIONS: An implantable loop recorder strategy for detection of occult atrial fibrillation in patients with cryptogenic stroke is more economically attractive than 30-day electrocardiography monitoring compared to conventional monitoring and is associated with a cost per quality-adjusted life year gained in the range of other publicly funded therapies. The value proposition is improved when considering patients at the highest risk of recurrent ischemic stroke. However, the implantable loop recorder strategy is associated with increased health care costs, and the opportunity cost of wide scale implementation must be considered.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".