Constructing a clinical prediction model for patients with coronary heart disease: a study based on carotid
Pingjuan Ni1,2,3, Wenrong Wang1,2,4, Nan Wang3
1Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Insights
This study developed a simple coronary heart disease (CHD) prediction model using carotid ultrasound and clinical factors. The model accurately identifies patients at risk, aiding clinical diagnosis and treatment strategies.
Area of Science:
- Cardiology
- Medical Imaging
- Predictive Analytics
Background:
- Coronary heart disease (CHD) has a high incidence and insidious onset, often linked to atherosclerotic plaques in coronary and carotid arteries.
- Carotid artery atherosclerosis can indicate systemic cardiovascular risk, necessitating effective prediction tools.
Purpose of the Study:
- To construct and validate a predictive model for CHD using carotid ultrasound (CDU) and integrated clinical factors.
- To assess the clinical utility of CDU in identifying patients with CHD.
Main Methods:
- A cohort of 649 patients underwent CDU before coronary angiography (CAG).
- Logistic regression analysis identified independent predictors of CHD in a training set (70% of patients).
- Model performance was validated using ROC curves, calibration curves, and decision curve analysis.
Main Results:
- Male sex, chest pain, diabetes, triglyceride levels, Apolipoprotein A, plaque score, and carotid stenosis were identified as independent predictors of CHD.
- The nomogram-based prediction model demonstrated high predictive performance, calibration, and clinical value in both training and validation sets.
Conclusions:
- A prediction model combining CDU and clinical factors offers a simple, practical, and stable approach for CHD risk stratification.
- This model can guide clinical diagnosis and treatment decisions for patients with suspected CHD.
Background And Objective:
The incidence of coronary heart disease(CHD) is high and its onset is insidious. CHD often occurs due to the formation of atherosclerotic plaques in the coronary arteries, which leads to narrowing or even occlusion of the lumen, and is often accompanied by the formation of atherosclerotic plaques in the carotid arteries. This study aims to construct a prediction model for patients with CHD by evaluating the carotid ultrasound(CDU) and integrating clinical related factors.
Methods:
A total of 649 patients were enrolled and divided into the CHD and the non-CHD groups according to coronary angiography (CAG) results. CDU assessment was performed 24 h before CAG, and basic patient information and clinical related factors were recorded. Patients were randomly divided into training and validation sets in a 7:3 ratio. In the training set, univariate and multivariate logistic regression analyses were used to screen independent risk factors for patients with CHD, and a risk prediction model was constructed based on these factors. The model was validated using the receiver operating characteristic and calibration curves, decision curve analysis, and clinical impact curve.
Results:
Multivariate logistic regression analysis of the training set exhibited that male chest pain, diabetes, triglyceride, Apolipoprotein A, plaque score, and carotid stenosis were independent predictors of CHD (p < 0.05). The nomogram constructed using these indicators exhibited high predictive performance, calibration, and clinical value in training and validation sets.
Conclusion:
The CHD prediction model constructed by CDU combined with clinical related factors is simple, practical, and has stable prediction performance, which can stratify suspicious patients and further guide clinical diagnosis and treatment.
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