Cost-Effectiveness of Extended Electrocardiogram Monitoring for Atrial Fibrillation After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Management of cryptogenic stroke involves the identification of modifiable risk factors, such as atrial fibrillation (AF). Extended rhythm monitoring increases AF detection rates but at an increased device cost compared with conventional Holter monitoring. The objective of the study was to identify and synthesize the existing literature on the cost-effectiveness of prolonged rhythm monitoring devices for AF detection in cryptogenic stroke. METHODS: We conducted a systematic review of available economic evaluations of prolonged ECG monitoring for AF detection following cryptogenic stroke compared with standard care. RESULTS: Of the 530 unique citations, 8 studies assessed the cost-utility of prolonged ECG monitoring compared with standard care following cryptogenic stroke. The prolonged ECG monitoring strategies included 7-day ambulatory monitoring, 30-day external loop recorders or intermittent ECG monitoring, and implantable loop recorders. The majority of cost-utility analyses reported incremental cost-effectiveness ratios below $50 000 per QALY gained; and two studies reported a cost-savings. CONCLUSIONS: There is limited economic literature on the cost-effectiveness of extended ECG monitoring devices for detection of atrial fibrillation in cryptogenic stroke. In patients with cryptogenic stroke, extended ECG monitoring for AF detection may be economically attractive when traditional willingness-to-pay thresholds are adopted. However, there was substantial variation in the reported ICERs. The direct comparison of cost-effectiveness across technologies is limited by heterogeneity in modeling assumptions.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.004 | 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".