Combination of Lesion Stenosis and Myocardial Supply Area Assessed by Coronary Computed Tomography Angiography for Prediction of Myocardial Ischemia
Bibliographic record
Abstract
Recent clinical studies revealed that anatomical information assessed by coronary computed tomography angiography (CTA) may be used effectively to diagnose coronary artery disease (CAD). However, a physiological assessment, demonstrating myocardial ischemia, is required to justify a therapeutic strategy for CAD. This study aimed to investigate whether using CTA to assess myocardial supply area can improve the prediction of myocardial ischemia.We analyzed 201 vessels with moderate (luminal narrowing ≥ 50%, < 70%) and severe (luminal narrowing ≥ 70%, < 99%) stenosis on CTA from 174 patients, who were suspected of having stable angina and underwent measurement of fractional flow reserve (FFR). The myocardial area supplied by the coronary artery, distal to the stenosis, was evaluated with CTA, as reported previously (modified Alberta Provincial Project for Outcome Assessment in Coronary Heart score) and was classified into 3 groups (large, medium, and small).Both percentage area stenosis and myocardial supply area were significantly correlated with FFR (r = -0.46, P < 0.01, and r = -0.45, P < 0.01). Among patients who had coronary plaques, with moderate stenosis and a small myocardial supply area, only 3 of 42 lesions (7%) were identified as ischemic; deviation from the ischemic threshold (FFR = 0.80) was P < 0.01. The combined assessment of lesion stenosis and myocardial supply area, using CTA, improved the prediction of myocardial ischemia significantly compared to lesion stenosis alone (77% versus 59%, P < 0.01).Adding the assessment of myocardial supply area to standard CTA might help predict myocardial ischemia in patients with stable angina pectoris.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".