Development of a multiparametric nomogram model for coronary lesion-specific ischemia prediction based on coronary CTA technology

BackgroundCoronary artery disease (CAD) is a leading cause of ischemic heart disease, and accurate identification of coronary lesion-specific ischemia (CLSI) is crucial for treatment. Coronary computed tomography angiography (CCTA) provides detailed visualization of coronary lesions, but its multiparameter analysis for predicting ischemia remains underexplored.ObjectiveTo develop a nomogram prediction model for CLSI based on multiparameters derived from CCTA.MethodsA total of 160 patients with CAD were divided into non-ischemic and ischemic groups according to the target-vessel CT-fractional flow reserve (CT-FFR). The baseline data of the two groups were collected, and the quantitative parameters of CCTA were compared. The predictive value of these parameters for CLSI was analyzed by the receiver operator characteristic (ROC) curve, and independent risk factors were analyzed by logistic regression.ResultsThe ischemic group showed significant differences in maximum diameter stenosis (MDS), maximum area stenosis (MAS), minimum lumen area (MLA), plaque burden (PB), pericoronary fat attenuation index (FAI), and low-attenuation plaque compared to the non-ischemic group (P < 0.05). Logistic regression revealed that MAS, MLA, FAI, and PB were independent risk factors for CLSI. The area under the curve (AUC) for MAS, MLA, FAI, and PB were 0.783, 0.947, 0.804, and 0.935, respectively. The calibration curve of the nomogram showed a good fit to the actual values [0.995 (95%CI: 0.988-1.000)].ConclusionsThis study constructed a nomogram risk prediction model for CLSI based on MAS, MLA, FAI, and PB, which holds significant clinical value.