High-sensitivity troponin I-guided cardiovascular risk assessment in a general asymptomatic population: a cost-effectiveness analysis in 4000 adults
Bibliographic record
Abstract
High-sensitivity cardiac troponin I (hs-TnI) is a promising biomarker for cardiovascular disease (CVD) risk stratification. This study assessed clinical outcomes and cost-effectiveness of an hs-TnI-guided CVD screening strategy in a general asymptomatic adult population. In a modeled observational program, 4000 adults aged 40-70 years without known CVD underwent hs-TnI testing. Participants were stratified into low (<4 ng/L), moderate (4-10 ng/L), and high (>10 ng/L) risk categories, and underwent further cardiac evaluation, and intervention, if indicated. A discrete-event microsimulation estimated CVD events, mortality, quality-adjusted life years (QALYs), and costs over 10 years. Among 4000 participants (mean age 57.1 ± 8.6 years; 52% women), 3548 (88.7%) were low-risk, 390 (9.8%) moderate-risk, and 62 (1.5%) high-risk. Noninvasive cardiac workup was performed in 452 (11.3%), and coronary angiography in 112 (2.8%). Significant coronary artery disease (CAD) was diagnosed in 49 (1.2%), with revascularization in 45. Compared to standard care, hs-TnI screening reduced CVD events by 37% and cardiovascular deaths by 34%, gaining 16.3 QALYs per 1000 participants. Incremental cost per person was €528, with an incremental cost-effectiveness ratio of €32 100/QALY, remaining cost effective in 93% of simulations. hs-TnI-guided cardiovascular risk assessment effectively stratifies asymptomatic adults, identifies subclinical CAD, and facilitates preventive intervention, appearing cost effective in reducing CVD burden.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".