Quantitative evaluation model for cardiovascular disease incidence based on plaque characteristics in patients with coronary atherosclerosis

Research Article
Open access

Quantitative evaluation model for cardiovascular disease incidence based on plaque characteristics in patients with coronary atherosclerosis

Guanyu Wang 1*
  • 1 School of Clinical Medicine, North China University of Science and Technology, Tangshan, China    
  • *corresponding author 19833690730@163.com
JCTT Vol.3 Issue 2
ISSN (Print): 3049-5466
ISSN (Online): 3049-5458

Abstract

This study aims to construct a quantitative evaluation model for the incidence of cardiovascular disease based on plaque characteristics in patients with coronary atherosclerosis, with the goal of providing robust scientific support for risk prediction and personalized prevention and treatment strategies. Patients with coronary atherosclerosis were selected as research subjects. Imaging characteristics of coronary plaques, including plaque volume, lipid core size, and fibrous cap thickness, were assessed. Clinical data such as age, sex, medical history, and lifestyle habits were also collected. Additionally, the occurrence of cardiovascular disease—including disease types and time of onset—was recorded. Based on these data, a quantitative evaluation model was developed using machine learning algorithms to predict the risk of cardiovascular disease. A quantitative evaluation model for cardiovascular disease incidence was successfully constructed based on plaque characteristics in patients with coronary atherosclerosis. The model integrated imaging features such as plaque volume, lipid core size, and fibrous cap thickness, along with clinical variables, and was built using a random forest algorithm. On the test set, the model achieved an AUC of 0.85, an accuracy of 78.5%, a recall rate of 75.0%, and an F1 score of 76.7%. Among these variables, plaque volume and lipid plaque ratio were identified as the most important predictors. The model can effectively identify high-risk patients, providing strong support for early clinical intervention.

Keywords:

coronary atherosclerosis, plaque characteristics, cardiovascular disease, quantitative evaluation model, machine learning

Wang,G. (2025). Quantitative evaluation model for cardiovascular disease incidence based on plaque characteristics in patients with coronary atherosclerosis. Journal of Clinical Technology and Theory,3(2),1-9.
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References

[1]. Lin, L., Long, W., Shan, L. S., Yun, Z. Z., Mian, L., & Ge, W. T. (2019). Association between coronary atherosclerotic plaque composition and cardiovascular disease risk. Biomedical and Environmental Sciences, 32(2), 75–86.

[2]. Nissen, S. E., & Yock, P. (2001). Intravascular ultrasound: Novel pathophysiological insights and current clinical applications. Circulation, 103(4), 604–616.

[3]. Virmani, R., Kolodgie, F. D., Burke, A. P., Farb, A., & Schwartz, S. M. (2000). Lessons from sudden coronary death: A comprehensive morphological classification scheme for atherosclerotic lesions. Arteriosclerosis, Thrombosis, and Vascular Biology, 20(5), 1262–1275.

[4]. D’Agostino, R. B., Sr., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., et al. (2008). General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation, 117(6), 743–753.

[5]. Liu, C., Lan, X., & Zhang, Y. (2014). Traditional imaging and molecular imaging for the detection and evaluation of vulnerable atherosclerotic plaques. Chinese Journal of Radiology and Nuclear Medicine, 38(2), 101–105, 134.

[6]. Liu, J. (2004). Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study. Journal of the American College of Cardiology, 43(5), 901–906.

[7]. Stone, G. W., Maehara, A., Lansky, A. J., de Bruyne, B., Cristea, E., Mintz, G. S. (2011). A prospective natural-history study of coronary atherosclerosis. New England Journal of Medicine, 364(3), 226–235.

[8]. Haddaway, N. R., Woodcock, P., Macura, B., & Collins, A. (2015). Making literature reviews more reliable through application of lessons from systematic reviews. Conservation Biology, 29(6), 1596–1605.

[9]. Ross, R. (1999). Atherosclerosis—An inflammatory disease. New England Journal of Medicine, 340(2), 115–126.

[10]. Yang, H., Liu, C., Liu, S., Shao, Q., Yao, Y., & Fu, Z. (2025). Study on the correlation between residual cholesterol and vulnerable plaques that progress to major adverse cardiovascular events in non-culprit lesions. Chinese General Practice, 28(3), 299–304.

[11]. Yang, H., & Xiong, J. (2017). Advances in the study of glucose and lipid metabolism in patients with hyperandrogenism. Chinese Modern Doctors, 10, 19-21.

[12]. Wu, W., Xia, Y., Liao, S., Li, R., & Zhao, J. (2013). Analysis of risk factors for elderly patients with abnormal glucose metabolism complicated by cardiovascular and cerebrovascular diseases. Chinese and Foreign Medical Research, 17, 16-21.

[13]. Damen, J. A., Hooft, L., Schuit, E., Debray, T. P., Collins, G. S., & Tzoulaki, I. (2016). Prediction models for cardiovascular disease risk in the general population: A systematic review. BMJ, 353, i2416.

[14]. Ren, X., Li, Z., & Wang, W. (2014). Research progress on the correlation between apolipoprotein E gene polymorphism and carotid atherosclerosis. Chinese Medical Herald, 21, 102-106.

[15]. Meng, Y., Du, Z., Zhao, C., Dong, M., & Pienta, D. (2023). Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms. Technology and Health Care: Official Journal of the European Society for Engineering and Medicine, 19, 36-39.

[16]. Okatani, T., Liu, X., & Suganuma, M. (2023). Improving generalization ability of deep neural networks for visual recognition tasks. Computational Color Imaging. CCIW 2019. Lecture Notes in Computer Science, vol 11418. Springer, Cham. https://doi.org/10.1007/978-3-030-13940-7_1

Cite this article

Wang,G. (2025). Quantitative evaluation model for cardiovascular disease incidence based on plaque characteristics in patients with coronary atherosclerosis. Journal of Clinical Technology and Theory,3(2),1-9.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Journal：Journal of Clinical Technology and Theory

Volume number: Vol.3
Issue number: Issue 2
ISSN：3049-5458(Print) / 3049-5466(Online)

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References

[1]. Lin, L., Long, W., Shan, L. S., Yun, Z. Z., Mian, L., & Ge, W. T. (2019). Association between coronary atherosclerotic plaque composition and cardiovascular disease risk. Biomedical and Environmental Sciences, 32(2), 75–86.

[2]. Nissen, S. E., & Yock, P. (2001). Intravascular ultrasound: Novel pathophysiological insights and current clinical applications. Circulation, 103(4), 604–616.

[3]. Virmani, R., Kolodgie, F. D., Burke, A. P., Farb, A., & Schwartz, S. M. (2000). Lessons from sudden coronary death: A comprehensive morphological classification scheme for atherosclerotic lesions. Arteriosclerosis, Thrombosis, and Vascular Biology, 20(5), 1262–1275.

[4]. D’Agostino, R. B., Sr., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., et al. (2008). General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation, 117(6), 743–753.

[5]. Liu, C., Lan, X., & Zhang, Y. (2014). Traditional imaging and molecular imaging for the detection and evaluation of vulnerable atherosclerotic plaques. Chinese Journal of Radiology and Nuclear Medicine, 38(2), 101–105, 134.

[6]. Liu, J. (2004). Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study. Journal of the American College of Cardiology, 43(5), 901–906.

[7]. Stone, G. W., Maehara, A., Lansky, A. J., de Bruyne, B., Cristea, E., Mintz, G. S. (2011). A prospective natural-history study of coronary atherosclerosis. New England Journal of Medicine, 364(3), 226–235.

[8]. Haddaway, N. R., Woodcock, P., Macura, B., & Collins, A. (2015). Making literature reviews more reliable through application of lessons from systematic reviews. Conservation Biology, 29(6), 1596–1605.

[9]. Ross, R. (1999). Atherosclerosis—An inflammatory disease. New England Journal of Medicine, 340(2), 115–126.

[10]. Yang, H., Liu, C., Liu, S., Shao, Q., Yao, Y., & Fu, Z. (2025). Study on the correlation between residual cholesterol and vulnerable plaques that progress to major adverse cardiovascular events in non-culprit lesions. Chinese General Practice, 28(3), 299–304.

[11]. Yang, H., & Xiong, J. (2017). Advances in the study of glucose and lipid metabolism in patients with hyperandrogenism. Chinese Modern Doctors, 10, 19-21.

[12]. Wu, W., Xia, Y., Liao, S., Li, R., & Zhao, J. (2013). Analysis of risk factors for elderly patients with abnormal glucose metabolism complicated by cardiovascular and cerebrovascular diseases. Chinese and Foreign Medical Research, 17, 16-21.

[13]. Damen, J. A., Hooft, L., Schuit, E., Debray, T. P., Collins, G. S., & Tzoulaki, I. (2016). Prediction models for cardiovascular disease risk in the general population: A systematic review. BMJ, 353, i2416.

[14]. Ren, X., Li, Z., & Wang, W. (2014). Research progress on the correlation between apolipoprotein E gene polymorphism and carotid atherosclerosis. Chinese Medical Herald, 21, 102-106.

[15]. Meng, Y., Du, Z., Zhao, C., Dong, M., & Pienta, D. (2023). Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms. Technology and Health Care: Official Journal of the European Society for Engineering and Medicine, 19, 36-39.

[16]. Okatani, T., Liu, X., & Suganuma, M. (2023). Improving generalization ability of deep neural networks for visual recognition tasks. Computational Color Imaging. CCIW 2019. Lecture Notes in Computer Science, vol 11418. Springer, Cham. https://doi.org/10.1007/978-3-030-13940-7_1

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