Abstract 13906: Electrocardiographic Correlates of Cardiac MRI Findings in Women With MINOCA
Bibliographic record
Abstract
Introduction: Myocardial infarction with nonobstructive coronary arteries (MINOCA) is identified in 6-15% of patients with MI and disproportionately affects women. Cardiac magnetic resonance (CMR) imaging is important to identify the cause of MINOCA but is restricted by its technical complexity and limited accessibility. The electrocardiogram (ECG), a simple, readily available tool, may predict features associated with abnormal CMR. Methods: Women with a diagnosis of MI and <50% angiographic stenosis in all epicardial vessels were enrolled in the Women’s Heart Attack Research Program (HARP), a prospective, multicenter, observational study. Participants had CMR at a median of 6 days from MI (n=116). A CMR core laboratory interpreted images while blinded to clinical data and ECGs. Abnormal CMR findings included late gadolinium enhancement (LGE) or myocardial edema. The patient’s presenting ECG was analyzed. ECGs with ≥2 uninterpretable leads or with bundle branch block were excluded. ECGs were assessed for T-wave inversions (TWI), pathologic Q waves (QW), fractionated QRS (fQRS), and ST segment depression or elevation. The DETERMINE score is 2x the number of leads (#) with QW + # fQRS + # TWI, excluding leads V1 and aVR (maximum score 40). Results: (Figure, Table): Among 112 women with MINOCA, CMR abnormality was more likely in women with DETERMINE score ≥3 vs <3 (86% vs 65%, p = 0.01). DETERMINE score ≥3 was associated with abnormal CMR on logistic regression (OR 3.4, p = 0.01). Patients with any TWI were more likely to have abnormal CMR than those with no TWI (84% vs 64%, p = 0.02), and were more likely to have LGE on CMR (61% vs 34%, p = 0.005). In those with a DETERMINE score of 0, CMR was abnormal in 62% vs 78% with DETERMINE score ≥1 (p=0.09), and LGE was present in 28% vs 54%, p = 0.01. Conclusion: ECG abnormalities were associated with abnormal CMR among women with MINOCA. The ECG may provide risk stratification for CMR, but the absence of QW, fQRS and TWI does not preclude CMR abnormality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".