Cost-effectiveness of functional cardiac imaging in the diagnostic work-up of coronary heart disease
Bibliographic record
Abstract
AIMS: The aim of this study was to assess the cost-effectiveness of eight common diagnostic work-up strategies for coronary heart disease (CHD) in patients with stable angina symptoms in Switzerland. METHODS AND RESULTS: A decision analytical model was used to perform a cost-effectiveness comparison of eight common multitest strategies to diagnose CHD using combinations of four diagnostic techniques: exercise treadmill test (ETT), single-photon emission computed tomography (SPECT), cardiac magnetic resonance imaging (CMR), and coronary angiography (CA). We used a Markov state transition model to extrapolate the results over a life-time horizon, from a third-party payer perspective. We used a CHD prevalence rate of 39% in patients and a base-case scenario with 60-year-old male patients with intermediate symptom severity Canadian Cardiovascular Society grading of angina pectoris 2 and at least one cardiovascular (CV) risk factor but without a history of myocardial infarction and without need for revascularization. Among the eight work-up strategies, one strategy was dominant, i.e. least costly and most effective: ETT followed by CMR if the ETT result was inconclusive and then CA if the CMR result was positive or inconclusive. The CMR features a favourable balance between false-negative diagnoses, associated with an elevated risk of CV events, and false-positive diagnoses, leading to unnecessary CA and related mortality. Key parameters guiding the diagnostic strategy are the prevalence of CHD in patients with angina symptoms and the diagnostic costs of CA and CMR. CONCLUSION: Cardiac magnetic resonance imaging appears to be a cost-effective work-up strategy compared with other regimens using SPECT or direct CA. Cardiac magnetic resonance imaging should be more widely recommended as a diagnostic procedure for patients with suspected angina symptoms.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".