Clinical features, sex differences and outcomes of myocardial infarction with nonobstructive coronary arteries: a registry analysis
Bibliographic record
Abstract
INTRODUCTION: Clinical characteristics and outcomes of patients diagnosed with myocardial infarction (MI) with nonobstructive coronary arteries (MINOCA) remain largely unknown. Furthermore, we do not yet understand if women with MINOCA have worse outcomes similar to what has historically been observed with MI. The aims of the current study were to evaluate the (1) incidence of MINOCA in patients presenting with MI, (2) compare in-hospital outcomes of MINOCA and obstructive atherosclerotic coronary artery disease MI (OACD-MI), and (3) comparison of in-hospital clinical outcomes of patients with MINOCA stratified by sex. METHODS AND RESULTS: In this observational study, we combined data from two large university hospitals from Canada and Australia. Clinical characteristics and in-hospital outcomes of MINOCA and OACD-MI were analyzed by matching these patients in a 1:1 ratio after selecting patients with OACD-MI by systematic random sampling. Clinical characteristics associated with MINOCA were identified through multivariate logistic regression. Primary outcome of interest was net adverse cardiovascular events (NACE) defined as death, heart failure, stroke, and major bleeding. The incidence rate of MINOCA was 9.5%. Women, absence of traditional cardiac risk factors, and absence of ST-deviations on ECG were associated with diagnosis of MINOCA on angiography. NACE (P = 0.0001), death (P = 0.019), stroke (P = 0.002), and heart failure (P = 0.001) were significantly lower in patients with MINOCA. Subgroup analysis of women and men diagnosed with MINOCA revealed similar in-hospital outcomes. CONCLUSION: The incidence of MINOCA was 9.5%. Compared to OACD-MI, patients with MINOCA have less cardiac risk factors. In-hospital outcomes of patients diagnosed with MINOCA were better than OACD-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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".