PET/CT Correlates and Random Forest Model for Ischemia in Nonobstructive Coronary Artery Disease

A random forest model using PET/CT and clinical factors (heart rate variability, LDL cholesterol, age, monocytes) predicted ischemia in nonobstructive coronary artery disease (INOCA) with internal AUC 0.92 (95% CI 0.89-0.95) and external AUC 0.71 (95% CI 0.63-0.79).

• External validation of the random forest model showed reduced discrimination (AUC 0.71; 95% CI 0.63-0.79; sensitivity 84.5%; specificity 53.0%).
• Clinical risk factors associated with INOCA included smoking, SDNN, LDL-C, hematocrit, and monocyte count; stress/rest LVEF decrease was more common in INOCA.
• The multiple logistic regression model had limited external performance (AUC 0.59; 95% CI 0.50-0.68).

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