Complementary Prognostic Values of Stress Myocardial Perfusion and Late Gadolinium Enhancement Imaging by Cardiac Magnetic Resonance in Patients With Known or Suspected Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: Recent studies have demonstrated the significant prognostic value of stress cardiac magnetic resonance (CMR) myocardial perfusion imaging. Apart from characterizing reversible perfusion defect (RevPD) from flow-limiting coronary stenosis, CMR late gadolinium enhancement (LGE) imaging is currently the most sensitive method for detecting subendocardial infarction (MI). We therefore tested the hypothesis that characterization of these 2 processes from coronary artery disease by CMR can provide complementary prognostic values. METHODS AND RESULTS: We performed CMR myocardial perfusion imaging followed by LGE imaging on 254 patients referred with symptoms of myocardial ischemia. At a median follow-up of 17 months, 49 cardiac events occurred, including 12 cardiac deaths, 16 acute MIs, and 21 cardiac hospitalizations. RevPD and LGE both maintained a >3-fold association with cardiac death or acute MI (death/MI) when adjusted for each other and for the effects of patient age and gender (adjusted hazard ratio, 3.31; P=0.02; and hazard ratio, 3.43; P=0.01, respectively). In patients without a history of MI who had negative RevPD, LGE presence was associated with a >11-fold hazards increase in death/MI. Patients with neither RevPD nor LGE had a 98.1% negative annual event rate for death/MI. For association with major adverse cardiac events, RevPD was the strongest multivariable variable in the best overall model (hazard ratio, 10.92; P<0.0001). CONCLUSIONS: CMR imaging provides robust risk stratification for patients who present with symptoms of ischemia. Characterization of RevPD and LGE by CMR provides strong and complementary prognostic implication for cardiac death or acute MI.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".