Diagnostic Accuracy of Cardiac Magnetic Resonance Versus Fractional Flow Reserve: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Fractional flow reserve (FFR) is considered the gold standard for diagnosis of coronary artery disease (CAD). Stress Cardiac magnetic resonance (SCMR) has been recently gaining traction as a non-invasive alternative to FFR. METHODS: Studies comparing the diagnostic accuracy of SCMR versus FFR were identified and analyzed using Review Manager (RevMan) 5.3 and Stata software. RESULTS: A total of 28 studies, comprising 2,387 patients, were included. The pooled sensitivity and specificity for SCMR were 86% and 86% at the patient level, and 82% and 88% at the vessel level, respectively. When the patient-level data were stratified based on the FFR thresholds, higher sensitivity and specificity (both 90%) were noted with the higher cutoff (0.75) and lower cutoff (0.8), respectively. At the vessel level, sensitivity and specificity at the lower FFR threshold were significantly higher at 88% and 89%, compared to the corresponding values for higher cutoff at 0.75. Similarly, meta-regression analysis of SCMR at higher (3T) resolution showed a higher sensitivity of 87% at the patient level and higher specificity of 90% at the vessel level. The highest sensitivity and specificity of SCMR (92% and 94%, respectively) were noted in studies with CAD prevalence greater than 60%. CONCLUSIONS: SCMR has high diagnostic accuracy for CAD comparable to FFR at a spatial resolution of 3T and an FFR cut-off of 0.80. An increase in CAD prevalence further improved the specificity of SCMR.
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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 | 0.006 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.023 | 0.007 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".