Abstract 14447: Multi-Center Evaluation of Stress Myocardial Blood Flow by Cardiac Magnetic Resonance in Known and Suspected Ischemic Heart Disease: Preliminary Findings From the AQUA-MBF Initiative
Bibliographic record
Abstract
Introduction: Quantitative myocardial blood flow (MBF) analysis using stress cardiac magnetic resonance (CMR) has been shown to detect obstructive coronary artery disease (CAD) and coronary microvascular dysfunction (CMD) in several mostly small, single-center studies. The AQUA-MBF ( A ssessment of QUA ntitative MBF ) study is a multicenter initiative involving 16 centers. Hypothesis: The goal of this sub-study is to determine if MBF can differentiate CAD, CMD, and normal volunteers in this multicenter setting. Methods: We present data from 53 subjects (15 with CAD, 20 at risk for CMD and 18 controls) who underwent vasodilator stress CMR (Figure) using 1.5T and 3.0T MR scanners (General Electric). At risk for CMD was defined as having diabetes and 2 other risk factors in absence of ≥50% stenosis based on coronary CT. CAD was defined as the presence of stenosis ≥70% based on invasive coronary angiography. Stress perfusion images were acquired using the dual sequence technique. Stress MBF was measured in each of the 16 AHA segments using Fermi deconvolution (Circle Cvi42). In the CAD group, each segment was further classified as having late gadolinium enhancement (LGE), supplied by CAD, or a normal remote territory. The means of the 5 groups were compared using one-way analysis of variance. Results: The segmental stress MBF (ml/g/min) for the 5 groups are shown in figure. Compared to the normal group, segmental stress MBF in 4 disease groups were significantly lower (p<0.001). Segmental MBF in those at risk for CMD was lower than normal segments and greater than CAD segments (p<0.001). LGE and CAD segments had the lowest stress MBF but similar to each other (p=0.9). Conclusions: In this multicenter study, we show that quantification of MBF using the dual sequence stress perfusion CMR technique can differentiate diseased from healthy myocardium and also between obstructive CAD and those at risk for CMD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".