Relationship between ischaemia, coronary artery calcium scores, and major adverse cardiovascular events
Bibliographic record
Abstract
AIMS: Positron emission tomography (PET) myocardial perfusion imaging (MPI) is often combined with coronary artery calcium (CAC) scanning, allowing for a combined anatomic and functional assessment. We evaluated the independent prognostic value of quantitative assessment of myocardial perfusion and CAC scores in patients undergoing PET. METHODS AND RESULTS: Consecutive patients who underwent Rb-82 PET with CAC scoring between 2010 and 2018, with follow-up for major adverse cardiovascular events (MACE), were identified. Perfusion was quantified automatically with total perfusion deficit (TPD). Our primary outcome was MACE including all-cause mortality, myocardial infarction (MI), admission for unstable angina, and late revascularization. Associations with MACE were assessed using multivariable Cox models adjusted for age, sex, medical history, and MPI findings including myocardial flow reserve.In total, 2507 patients were included with median age 70. During median follow-up of 3.9 years (interquartile range 2.1-6.1), 594 patients experienced at least one MACE. Increasing CAC and ischaemic TPD were associated with increased MACE, with the highest risk associated with CAC > 1000 [adjusted hazard ratio (HR) 1.67, 95% CI 1.24-2.26] and ischaemic TPD > 10% (adjusted HR 1.80, 95% CI 1.40-2.32). Ischaemic TPD and CAC improved overall patient classification, but ischaemic TPD improved classification of patients who experienced MACE while CAC mostly improved classification of low-risk patients. CONCLUSIONS: Ischaemic TPD and CAC were independently associated with MACE. Combining extent of atherosclerosis and functional measures improves the prediction of MACE risk, with CAC 0 identifying low-risk patients and regional ischaemia identifying high-risk patients in those with CAC > 0.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".