Predictive value of CAC score combined with clinical features for obstructive coronary heart disease on coronary computed tomography angiography: a machine learning method

Ren, Yongkui1,5; Li, Yulin1,2,3; Pan, Weili5; Yin, Da4; Du, Jie1,2,3

2022-12-26

10.1186/s12872-022-03022-9

BMC Cardiovascular Disorders   Impact Factor & Quartile

1471-2261

Objective: We investigated the predictive value of clinical factors combined with coronary artery calcium (CAC) score based on a machine learning method for obstructive coronary heart disease (CAD) on coronary computed tomography angiography (CCTA) in individuals with atypical chest pain. Methods: The study included data from 1,906 individuals undergoing CCTA and CAC scanning because of atypical chest pain and without evidence for the previous CAD. A total of 63 variables including traditional cardiovascular risk factors, CAC score, laboratory results, and imaging parameters were used to build the Random forests (RF) model. Among all the participants, 70% were randomly selected to train the models on which fivefold cross-validation was done and the remaining 30% were regarded as a validation set. The prediction performance of the RF model was compared with two traditional logistic regression (LR) models. Results: The incidence of obstructive CAD was 16.4%. The area under the receiver operator characteristic (ROC) for obstructive CAD of the RF model was 0.841 (95% CI 0.820-0.860), the CACS model was 0.746 (95% CI 0.722-0.769), and the clinical model was 0.810 (95% CI 0.788-0.831). The RF model was significantly superior to the other two models (p < 0.05). Furthermore, the calibration curve and Hosmer-Lemeshow test showed that the RF model had good classification performance (p = 0.556). CAC score, age, glucose, homocysteine, and neutrophil were the top five important variables in the RF model. Conclusion: RF model was superior to the traditional models in the prediction of obstructive CAD. In clinical practice, the RF model may improve risk stratification and optimize individual management.

Machine learning Random forest Coronary artery calcification score Obstructive coronary artery disease

SCI

English

Corresponding

[2019-ZD-0635]

Cardiovascular System & Cardiology

Cardiac & Cardiovascular Systems

WOS:000904141600002

BMC

Web of Science

1.Capital Med Univ, Beijing Anzhen Hosp, 2 Anzhen Rd, Beijing, Peoples R China
2.Minist Educ, Key Lab Remodeling Related Cardiovasc Dis, Beijing, Peoples R China
3.Beijing Inst Heart Lung & Blood Vessel Dis, Beijing, Peoples R China
4.Southern Univ Sci & Technol, JINAN Univ, Shenzhen Peoples Hosp, Dept Cardiol,Affiliated Hosp 1,Clin Med Coll 2, Shenzhen, Peoples R China
5.Dalian Med Univ, Dept Cardiol, Affiliated Hosp 1, Dalian, Peoples R China

Ren, Yongkui,Li, Yulin,Pan, Weili,et al. Predictive value of CAC score combined with clinical features for obstructive coronary heart disease on coronary computed tomography angiography: a machine learning method[J]. BMC Cardiovascular Disorders,2022,22(1).

Ren, Yongkui,Li, Yulin,Pan, Weili,Yin, Da,&Du, Jie.(2022).Predictive value of CAC score combined with clinical features for obstructive coronary heart disease on coronary computed tomography angiography: a machine learning method.BMC Cardiovascular Disorders,22(1).

Ren, Yongkui,et al."Predictive value of CAC score combined with clinical features for obstructive coronary heart disease on coronary computed tomography angiography: a machine learning method".BMC Cardiovascular Disorders 22.1(2022).