Quantification of myocardial blood flow using stress cardiac magnetic resonance for the detection of coronary artery disease
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): GE Healthcare. Background Myocardial blood flow (MBF) analysis using stress cardiac magnetic resonance (CMR) has been shown to detect obstructive coronary artery disease (CAD); however, evaluation of its diagnostic performance has primarily been limited to single-center studies. AQUA-MBF (Assessment of QUAntitative MBF using stress CMR) is an international study with the goal of assessing the diagnostic performance of stress MBF for the detection of CAD. In this study, we aim to determine how stress MBF assessment compared against visual analysis (VA) of stress CMR images for the detection of CAD. Methods. 144 individuals (89 (62%) men, age 62±16 years, 97 (68%) hypertension, 54 (38%) diabetes, 92 (64%) hyperlipidemia) from 9 centers who underwent dual sequence stress CMR (1.5T or 3.0T GE Healthcare) and also had either a coronary computed tomography angiography (CTA, n=31), invasive coronary angiogram (ICA, n=95), or low pre-test probability for CAD (n=18) were included. CAD was defined as the presence of: (1) a stenosis ≥50% in the left main coronary artery or ≥70% in the 1 major vessel based on ICA or CTA or (2) an invasive fractional flow reserve (FFR) ≤ 0.8. Absence of obstructive coronary disease (NOCAD) was defined as a no history of myocardial infarction and stenosis <50% by ICA or CTA, 50–70% stenosis with FFR>0.8, or a young individual with no cardiac risk factors. Myocardial perfusion imaging was performed during first pass perfusion of a gadolinium-based contrast agent following the administration of adenosine or regadenoson with a low-resolution image acquired to assess the arterial input function and 2–3 short axis slices acquired to assess myocardial perfusion. VA was performed by 2 experienced cardiologists who assigned a grade of 1–5 based on the probability a study was abnormal. Stress MBF values were determined for each of the 16 myocardial segments using Fermi deconvolution (CircleCVI). The global stress MBF was calculated as the average value of two segments with the lowest values from each of the three coronary artery territories. Unpaired t-test was used to compare stress MBF values between CAD vs NOCAD. Receiver-operating characteristics curves were used to determine diagnostic performance. Results. 60 patients had CAD (20: 1-vessel, 26: 2-vessel, and 14: 3-vessel) (Figure 1A) while 84 had NOCAD (Figure 1B). The global stress MBF in CAD was lower than in NOCAD (1.63±0.52ml/g/min vs 2.41±0.68ml/g/min, p<0.0001) (Figure 2A). Stress MBF had a higher AUC than VA (reader 1 – 0.83 vs 0.73, p = 0.07: reader 2–0.83 vs 0.71, p = 0.02). The optimal stress MBF cut-off value for detecting CAD was 2.05ml/g/min (Figure 2B). Conclusions. In this multicenter study, we show that global stress MBF reported as a single value can identify patients with CAD at least as accurately as VA performed by physicians experienced in the interpretation of stress CMR images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".