Rev Cardiovasc Med. 2026 Aug 13;27(8):48741. doi: 10.31083/RCM48741. eCollection 2026 Aug.
ABSTRACT
BACKGROUND: This study aimed to identify search for clinical indicators of ischemia with no obstructive coronary arteries (INOCA) and to develop a preliminary diagnostic and predictive model to identify high-risk patients in clinical practice.
METHOD: (1) We retrospectively reviewed patients admitted to the hospital for chest pain or related symptoms who were clinically suspected of having angina pectoris. Patients who received complete nuclear coronary flow reserve (CFR) measurements obtained by positron emission tomography/computed tomography (PET/CT) quantitative coronary artery assessment, had undergone coronary angiography, and had no obstructive coronary lesions were selected as study subjects. Based on the CFR results, patients with a minimum CFR <2.0 were classified as the INOCA group (diagnosed group), and those with a minimum CFR ≥2.5 were classified as the non-INOCA group (control group). Clinical indicators were compared between the two groups. A multiple logistic regression analysis was performed to identify high-risk factors and to establish an INOCA diagnostic model. Additionally, a random forest INOCA diagnostic model based on decision trees was constructed using machine learning methods. (2) An independent cohort of patients meeting the same inclusion criteria as in Method (1) was used for external validation and evaluation of the previously established multiple logistic regression and random forest models.
RESULTS: (1) During the model development phase, the results of the multiple logistic regression analysis indicated that smoking history, standard deviation of normal R-R interval (SDNN), low-density lipoprotein-cholesterol (LDL-C), hematocrit (HCT), and monocyte absolute value (MONO#) may be associated with the risk of INOCA. The preliminary logistic regression model, with internal validation and receiver operating characteristic (ROC) curve analysis, demonstrated moderate discrimination (area under the curve (AUC) = 0.70; 95% confidence interval (CI): 0.66-0.74; sensitivity = 79.80%; specificity = 54.12%). Calibration curve and decision curve analysis (DCA) suggested good predictive accuracy and clinical net benefit. (2) Using machine learning, a random forest model was constructed based on four features (SDNN, LDL-C, age, MONO#). Internal validation with ROC analysis showed excellent discrimination (AUC = 0.92; 95% CI: 0.89-0.95; sensitivity = 84.0%; specificity = 82.3%). (3) During the external validation phase, the logistic regression model showed limited performance (AUC = 0.59; 95% CI: 0.50-0.68; sensitivity = 58.92%; specificity = 58.24%), and the calibration curve and DCA curve showed only average model performance. In contrast, external validation of the random forest model yielded better discrimination (AUC = 0.71; 95% CI: 0.63-0.79; sensitivity = 84.5%; specificity = 53.0%).
CONCLUSION: (1) Decreases in resting and stress left ventricular ejection fraction (LVEF) in patients with INOCA are more readily detected by PET/CT examination. (2) The risk of INOCA is closely associated with smoking history, SDNN, LDL-C, MONO#, and HCT. (3) Compared with multiple logistic regression analysis, the random forest prediction model shows more accuracy in predicting INOCA.
PMID:42694910 | PMC:PMC13540022 | DOI:10.31083/RCM48741