Abstract 16817: Comparison of Diagnostic Performance of Quantitative Perfusion Stress Myocardial Perfusion Imaging With Cardiac Magnetic Resonance Between Different Vasodilators
Bibliographic record
Abstract
Introduction: Adenosine and regadenoson are commonly used vasodilators in myocardial perfusion imaging for assessment of suspected coronary artery disease. Hypothesis: The aim of this study is to determine the difference between the different vasodilators by quantifying stress and rest myocardial perfusion using cardiovascular magnetic resonance (CMR). Methods: Subjects with known or suspected CAD from 10 centers undergoing invasive coronary angiography or CT angiography were enrolled for rest and stress first-pass perfusion images using dual sequence. First-pass stress perfusion images were acquired on 1.5T or 3.0T GE scanner during adenosine or Regadenoson. Fully quantitative perfusion values were determined using Fermi deconvolution. Significant CAD was defined by: presence of≥50% stenosis in the left main coronary artery or ≥70% in the one major vessel. Diagnostic performance of stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) was measured using receiver operating characteristics curves. Results: A total of 89 subjects were recruited with a median age of 66 yrs, 69% men, 55% significant CAD, history of hypertension in 78%, diabetes in 48%, and hyperlipidemia in 74%. 36 subjects used adenosine, while 53 used regadenoson as the vasodilator agent. Stress MBF had a good area under the curve for adenosine vs. regadenoson [ 0.89 (0.78-1.00) vs. 0.82 (0.66-0.98), p=0.43], sensitivity (100% vs. 84%), and specificity (80% vs. 80%). MPR had a higher area under the curve for adenosine than regadenoson [0.87 (0.75-0.98) vs. 0.68 (0.52-0.84), p=0.06], sensitivity (100% vs. 61%), and specificity (68% vs. 73%). (Figure) Conclusions: Our study showed that fully quantitative perfusion using CMR, regadenoson, and adenosine have similar good diagnostic accuracy when stress MBF was used as the diagnostic parameter. However, adenosine outperformed regadenoson for the detection of significant CAD when MPR was used as the diagnostic parameter.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".