Abstract 4144431: Machine Learning to Differentiate Myocardial Infarction with Obstructive versus Non-Obstructive Coronary Artery Disease
Bibliographic record
Abstract
Background: Myocardial infarction (MI) can occur with or without coronary artery disease, classified as myocardial infarction with non-obstructive coronary artery disease (MINOCA) and MI with obstructive coronary artery disease (MICAD), respectively. Differentiating between these conditions is important as they require potentially different diagnostic and therapeutic approaches. No pre-test probability score currently exists to aid in this differentiation. This study aims to develop and validate machine learning (ML) models to predict whether a patient with suspected MI has MINOCA or MICAD. Methods: Data from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database, including patients who underwent their first cardiac catheterization for chest pain or an anginal equivalent from 2002 to 2017, was used. We analyzed a cohort of 53,348 patients suspected of having myocardial infarction. For machine learning model development, 20 demographic and clinical features were selected using the Boruta algorithm. The dataset was divided into training (80%) and testing (20%) sets. Model performance was evaluated using 5-fold cross-validation, and the area under the receiver operating characteristic curve (AUROC) was the primary metric for model evaluation. Results: Among the five machine learning models trained, the XGBoost model showed superior performance, achieving an AUROC of 0.76 [95% CI: 0.76–0.77] and an accuracy of 0.86 during internal validation. Logistic regression achieved an AUROC of 0.74 [95% CI: 0.74–0.74]. Calibration of the XGBoost model was assessed via a calibration plot, which showed good agreement between predicted probabilities and observed event probabilities, with a Brier score of 0.10. Conclusions: The developed ML models demonstrated moderate accuracy for predicting MICAD versus MINOCA using clinically relevant features. Implementing such models in clinical practice could guide alternative diagnostic approaches for patients with suspected MINOCA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".