Cost-effectiveness of an insertable cardiac monitor in a high-risk population in the US
Bibliographic record
Abstract
Objective: To evaluate the cost-effectiveness of insertable cardiac monitors (ICMs) compared to standard of care (SoC) for detecting atrial fibrillation (AF) in patients at high risk of stroke (CHADS2 >2), in the US. Background: ICMs are a clinically effective means of detecting AF in high-risk patients, prompting the initiation of non-vitamin K oral anticoagulants (NOACs). Their cost-effectiveness from a US clinical payer perspective is not yet known. Methods: Using patient data from the REVEAL AF trial (n= 446, average CHADS2 score= 2.9), a Markov model estimated the lifetime costs and benefits of detecting AF with an ICM or with SoC (namely, intermittent use of electrocardiograms [ECGs] and 24-hour Holter monitors). Ischemic and hemorrhagic strokes, intra- and extra-cranial hemorrhages, and minor bleeds were modelled. Diagnostic and device costs were included, plus costs of treating stroke and bleeding events and of NOACs. Costs and health outcomes, measured as quality-adjusted life years (QALYs), were discounted at 3% per annum. One-way deterministic and probabilistic sensitivity analyses (PSA) were undertaken. Results: Lifetime per-patient cost for ICM was $58,132 vs. $52,019 for SoC. ICMs generated a total 7.75 QALYs vs. 7.59 for SoC, with 34 fewer strokes projected per 1,000 patients. The incremental cost-effectiveness ratio (ICER) was $35,452 per QALY gained. ICMs were cost-effective in 72% of PSA simulations, using a $50,000 per QALY threshold. Conclusions: The use of ICMs to identify AF in a high-risk population is likely to be cost-effective in the US healthcare setting.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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) | 0.002 | 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".