Cost-Effectiveness of Percutaneous Coronary Intervention Compared With Medical Therapy for Ischemic Heart Disease in Japan
Bibliographic record
Abstract
BACKGROUND: The cost-effectiveness of percutaneous coronary intervention (PCI) for ischemic heart disease is undetermined in Japan. The aim of this study was to analyze the cost-effectiveness of PCI compared with medical therapy for ST-elevation myocardial infarction (STEMI) and angina pectoris (AP) in Japan. METHODS AND RESULTS: We used Markov models for STEMI and AP to assess the costs and benefits associated with PCI or medical therapy from a health system perspective. We estimated the incremental cost-effectiveness ratio (ICER), expressed as quality-adjusted life-years (QALY), and ICER <¥5 m per QALY gained was judged to be cost-effective. The impact of PCI on cardiovascular events was based on previous publications. In STEMI patients, the ICER of PCI over medical treatment was ¥0.97 m per QALY gained. The cost-effectiveness probability of PCI was 99.9%. In AP patients, the ICER of fractional flow reserve (FFR)-guided PCI over medical treatment was ¥4.63 m per QALY gained. The cost-effectiveness probability of PCI was 50.4%. The ICER of FFR-guided PCI for asymptomatic patients was ¥23 m per QALY gained. CONCLUSIONS: In STEMI patients, PCI was cost-effective compared with medical therapy. In AP patients, FFR-guided PCI for symptomatic patients could be cost-effective compared with medical therapy. FFR-guided PCI for asymptomatic patients with myocardial ischemia was not cost-effective.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 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".