Lifetime cost-effectiveness of prophylactic implantation of a cardioverter defibrillator in patients with reduced left ventricular systolic function: results of Markov modelling in a European population
Bibliographic record
Abstract
AIMS: Current European guidelines recommend prophylactic implantation of cardioverter defibrillators (ICDs) in patients with a reduced left ventricular ejection fraction (LVEF) who are not in NYHA class IV and have reasonable life expectancy. Cost and benefit implications of this recommendation have not been reported from a European perspective. METHODS AND RESULTS: Markov modelling estimated lifetime costs and effects [life years (LY) and quality-adjusted LY (QALY) gained] of prophylactic ICD implantation vs. conventional treatment, among patients with a reduced LVEF. Efficacy was estimated from a meta-analysis of mortality rates in the six primary prevention trials with inclusion criteria matching ACC/AHA/ESC Class I or IIa recommendations. Direct medical costs were estimated using Belgian national references. Costs and effects were discounted at 3 and 1.5% per annum, respectively. Probabilistic sensitivity and scenario analyses estimated the uncertainty around the incremental cost-effectiveness ratio. An ICD implantation increased the lifetime direct costs by euro 46,413. Estimated mean LY/QALY gained were 1.88/1.57, respectively. Probabilistic analysis estimated mean lifetime cost per QALY gained as euro 31,717 (95% CI: euro 19,760-euro 61,316). Cost-effectiveness was influenced most by ICD efficacy, time to replacement, utility, and patient age at implantation. CONCLUSION: In a European healthcare setting, prophylactic ICD implantation may be cost-effective if current guidelines for patients with a reduced LVEF are followed.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| 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 | 0.002 | 0.002 |
| 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".