Multimodal Machine Learning Integrating N-13 Ammonia PET and Clinical Variables Predicts Major Adverse Cardiac Events

The need for dynamic and static acquisitions under stress and rest in myocardial perfusion positron emission tomography (PET) is burdensome, and the short half-life of N-13 ammonia places additional constraints on scanning protocols. This study investigated whether combining static PET-derived images with clinical parameters via machine learning predicts major adverse cardiac events (MACE) to expand the utility of ammonia PET without dynamic scanning. The cohort comprised 386 patients, and during a mean follow-up of 345 days, MACE occurred in 35 patients. We applied stratified fivefold cross-validation based on MACE prediction with balanced and random shuffles. A logistic regression model was trained using all the explanatory variables after removing highly collinear features. Based on the cumulative importance of MACE prediction, a model was developed using the minimum number of top-ranked features accounting for > 50% of total cumulative importance. The predictive performance of a simple threshold-based classification using myocardial flow reserve (MFR) < 2.0 was also evaluated for comparison. The model was trained on 308 cases using three features: age, dyslipidemia, and resting end-diastolic volume. When tested on an independent set of 78 cases with fivefold cross-validation, it achieved an accuracy of 0.74 ± 0.06, a sensitivity of 0.74 ± 0.23, and a specificity of 0.74 ± 0.07. The accuracy, sensitivity, and specificity of simple MFR < 2.0 prediction were 0.58 ± 0.05, 0.77 ± 0.13, and 0.56 ± 0.05, respectively. A multimodal machine learning approach potentially serves as a clinically useful alternative to dynamic PET scans.