Cardiac Magnetic Resonance Imaging for Noninvasive Assessment of Cardiovascular Disease During the Follow-Up of Patients With Kawasaki Disease
Bibliographic record
Abstract
BACKGROUND: Kawasaki disease (KD) is the most common cause of acquired coronary artery disease in childhood. In KD, the American Heart Association recommends echocardiography for routine coronary artery surveillance and nuclear perfusion scans and conventional coronary angiography in select patients. Cardiac MRI (CMRI) may be a noninvasive and radiation-free alternative. We applied CMRI during the follow-up of patients with KD and assessed the performance of CMRI compared with echocardiography. METHODS AND RESULTS: Patients with KD aged ≥8 years were consecutively included. Sixty-three patients (median age, 14.6 years; 74.6% male sex) underwent a comprehensive CMRI protocol including adenosine stress testing to evaluate coronary artery anatomy, ischemia, and myocardial infarction. All patients underwent CMRI without significant complications. On CMRI, 23 coronary artery aneurysms (CAAs) were identified in 15 patients. CMRI detected thrombus formation in 6 CAAs in 4 patients, wall motion disturbances and ischemia in 4 patients, and delayed hyperenhancement indicating myocardial infarction in 5 patients. Wall motion and perfusion abnormalities were noted in territories supplied by affected coronary arteries. CMRI results were compared with recent echocardiography findings. In 6 of the 15 patients with CAAs on CMRI, CAAs were not detected by echocardiography. CONCLUSIONS: A comprehensive CMRI protocol including adenosine stress testing is feasible to identify coronary artery pathology, ischemia, and myocardial infarction in former patients with KD and compares favorably with echocardiography. CMRI may be used as a noninvasive and radiation-free imaging method for coronary artery surveillance during the long-term follow-up of patients with KD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".