Key result
Carotid ultrasound parameters, including intima-media thickness and plaque score, were incorporated into a predictive model that effectively identified coronary heart disease with an AUC of 0.866.
Why the study?
Coronary angiography is an invasive diagnostic tool, prompting investigation into non-invasive carotid ultrasound parameters for predicting and diagnosing coronary heart disease.
Do carotid ultrasound parameters accurately predict the occurrence and severity of coronary artery disease in symptomatic patients?
Observational (n=82)
Single-blind
No
Do carotid ultrasound parameters accurately predict the occurrence and severity of coronary artery disease in symptomatic patients?
Effect estimate: AUC 0.866 (95% CI 0.779-0.953)
Carotid ultrasound parameters, specifically intima-media thickness, plaque score, and plaque echogenicity, can effectively predict the presence and severity of coronary artery disease, offering a non-invasive diagnostic alternative to coronary angiography.
Carotid ultrasound parameters were associated with CHD in this retrospective cohort; leaves open whether they reliably predict disease or reduce angiography need.
Aims/Background 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. Methods 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. Results The CHD group had higher body mass index (BMI), smoking history, diabetes, total cholesterol (TC), and triglycerides (TG) levels (p < 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 (p < 0.05). Pearson correlation showed a positive correlation between Gensini score and IMT, plaque score, and a negative association with plaque echogenicity GSM (p < 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 (p < 0.05). IMT and plaque score were identified as CHD risk factors and plaque echogenicity GSM as a protective factor (p < 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. Conclusion 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.
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Wang et al. (2025) conducted an observational in Coronary Artery Disease (n=82). Carotid ultrasound parameters vs. Non-CHD patients was evaluated on Prediction of CHD using a nomogram model based on carotid ultrasound parameters (AUC 0.866, 95% CI 0.779-0.953). Carotid ultrasound parameters, including intima-media thickness and plaque score, were incorporated into a predictive model that effectively identified coronary heart disease with an AUC of 0.866.
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