A nine-variable random forest model predicted epicardial coronary artery spasm with an area under the curve of 84.1% (95% CI 80.6-87.7%) in an external validation cohort.
Cohort (n=1,650)
Yes
Does an AI-driven risk score using routine clinical data accurately predict epicardial coronary artery spasm in patients without obstructive coronary artery disease?
A nine-variable random forest model using routine clinical data accurately predicts epicardial coronary artery spasm in patients without obstructive CAD, offering a noninvasive tool for risk stratification.
Effect estimate: AUC 84.1% (95% CI 80.6-87.7)
Background: Epicardial coronary artery spasm (CAS) is a frequent and important cause of myocardial ischemia. We aimed to develop and validate a noninvasive, artificial intelligence (AI)-driven risk score using routine clinical data to predict CAS in patients without obstructive coronary artery disease (CAD). Methods: This retrospective study analyzed a derivation cohort of 1050 patients and an external validation cohort of 600 patients who underwent intracoronary methylergonovine provocation testing between September 2008 and March 2025. A random forest (RF) model was developed using 15 clinical variables and simplified to a nine-variable model. Additionally, a convolutional neural network-long short-term memory (CNN-LSTM) deep learning model was implemented to predict CAS from raw digital electrocardiogram data (2611 electrocardiogram records). Results: The final nine-variable RF model, including predictors such as diastolic/systolic blood pressure, age, BSA, hemoglobin, smoking, heart rate, sex, and estimated glomerular filtration rate, demonstrated strong discriminatory power. The area under the curve was 85.8% (95% confidence interval CI: 85.8–89.9%) in the derivation cohort and 84.1% in the validation cohort (95% CI: 80.6–87.7%). A dose–response relationship was confirmed, with CAS prevalence increasing from 42.1% (0–1 risk factors) to 82.4% (≥5 risk factors). The electrocardiogram-based CNN-LSTM deep learning model achieved high sensitivity (91.4%) but limited specificity (11.9%); therefore, it should be considered a proof of concept rather than a clinical screening tool until further refinement is achieved. Conclusions: The nine-variable RF model provides a practical and accurate tool for early identification and risk stratification of CAS. The electrocardiogram deep learning model complements the RF model to improve clinical decisions and resource allocation in diagnosing CAS.
Hung et al. (Mon,) conducted a cohort in Epicardial coronary artery spasm in patients without obstructive coronary artery disease (n=1,650). Nine-variable random forest model was evaluated on Prediction of epicardial coronary artery spasm (AUC 84.1%, 95% CI 80.6-87.7). A nine-variable random forest model predicted epicardial coronary artery spasm with an area under the curve of 84.1% (95% CI 80.6-87.7%) in an external validation cohort.