Assessing the Predictive Significance of Carotid Ultrasound Parameters for Coronary Artery Disease: A 3-Year Single-Center Experience.

Coronary angiography is a widely used invasive approach for diagnosing coronary atherosclerotic heart disease (CHD). However, carotid ultrasound may predict CHD by assessing carotid atherosclerosis. Therefore, this study explores the predictive significance of carotid ultrasound parameters in accurately diagnosing coronary artery disease. This retrospective analysis included 82 CHD patients who underwent carotid ultrasound scans at the Funan County Hospital of Traditional Chinese Medicine, China, between July 2021 and February 2024. Based on coronary angiography results, patients were divided into the CHD (n = 48) and non-CHD (n = 34) groups. Differences in clinical data, biochemical indicators, and carotid ultrasound parameters were evaluated between the two experimental groups. Furthermore, correlation analysis assessed the association between ultrasound parameters and CHD occurrence and severity. Additionally, multivariable logistic regression analyses were performed, followed by developing a CHD prediction nomogram model. Finally, the model's performance was evaluated through analyses of receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. The CHD group had higher body mass index (BMI), smoking history, diabetes, total cholesterol (TC), and triglycerides (TG) levels ( < 0.05). Furthermore, significantly higher intima-media thickness (IMT) and plaque score and lower plaque echogenicity grey scale median (GSM) were observed in the CHD group ( < 0.05). Pearson correlation showed a positive correlation between Gensini score and IMT, plaque score, and a negative association with plaque echogenicity GSM ( < 0.05). Spearman correlation revealed positive correlations between BMI, smoking history, diabetes, TG, TC, IMT, plaque score, and CHD diagnosis, and a negative correlation with plaque echogenicity GSM ( < 0.05). IMT and plaque score were identified as CHD risk factors and plaque echogenicity GSM as a protective factor ( < 0.05). The model based on carotid ultrasound parameters demonstrated high predictive performance for CHD, with an area under the curve (AUC) of 0.866 (95% confidence interval [CI]: 0.779-0.953). DCA and calibration curves supported the model's accuracy. Carotid ultrasound parameters differ significantly between CHD and non-CHD patients. The developed model using these parameters effectively predicts CHD occurrence, providing a valuable diagnostic alternative for coronary angiography.