Microvolt T-wave alternans and the selective use of implantable cardioverter defibrillators for primary prevention: A cost-effectiveness study
Bibliographic record
Abstract
OBJECTIVES: Implantable cardioverter defibrillators (ICDs) are an effective but expensive treatment for the prevention of sudden cardiac deaths in patients with severe left-ventricular dysfunction. Recent studies suggest that microvolt T-wave alternans (MTWA) predicts mortality and severe arrhythmic events in this population. However, the impact of MTWA on ICD cost-effectiveness is unknown. METHODS: A Markov decision-analysis model evaluated three treatment strategies for primary prevention in patients with severe left-ventricular dysfunction: (i) medical therapy for all; (ii) ICD therapy for all; and (iii) selective ICD therapy based on non-negative (positive or indeterminate) MTWA test results. Incremental cost-effectiveness ratios (ICER) were calculated from the perspective of a third party payer using a 10-year time horizon. Sensitivity analyses examined the robustness of the estimates. RESULTS: A treatment strategy involving ICD therapy in all patients was associated with an ICER of $121,800/quality-adjusted life-year (QALY) compared with medical therapy, whereas a treatment strategy involving the selective use of ICDs based on MTWA test results was associated with an ICER of $108,900/QALY compared with medical therapy. Sensitivity analyses suggest that, under most scenarios, the selective use of ICDs based on MTWA results does not decrease the ICER to below $100,000/QALY. CONCLUSION: MTWA only marginally improves the cost-effectiveness of ICDs for primary prevention in patients with severe left-ventricular dysfunction. There remains a need for improved means to effectively identify which patients will derive the greatest benefit from ICD implantation.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".