Development of a nomogram that predicts the risk for coronary atherosclerotic heart disease

Abstract

Studies seldom combine biological, behavioral and psychological factors to estimate coronary atherosclerotic heart disease (CHD) risk. Here, we evaluated the associations between these factors and CHD to develop a predictive nomogram to identify those at high risk of CHD. This case-control study included 4392 participants (1578 CHD cases and 2814 controls) in southeast China. Thirty-three biological, behavioral and psychological variables were evaluated. Following multivariate logistic regression analysis, which revealed eight risk factors associated with CHD, a predictive nomogram was developed based on a final model that included the three non-modifiable (sex, age and family history of CHD) and five modifiable (hypertension, hyperlipidemia, diabetes, recent experience of a major traumatic event, and anxiety) variables. The higher total nomogram score, the greater the CHD risk. Final model accuracy (as estimated from the area under the receiver operating characteristic curve) was 0.726 (95% confidence interval: 0.709-0.747). Validation analysis confirmed the high accuracy of the nomogram. High risk of CHD was associated with several biological, behavioral and psychological factors. We have thus developed an intuitive nomogram that could facilitate development of preliminary prevention strategies for CHD.

Novel Insights into the Predictors of Obstructive Sleep Apnea Syndrome in Patients with Chronic Coronary Syndrome: Development of a Predicting Model

“…Moreover, it has been accepted that multiple clinical parameters, such as demographic characteristics, vital signs, laboratory data, and radiological data, are important for the management of patients with CAD, which are usually routinely collected at admission. A previous study indicated that a predictive nomogram based on clinical routine variables is important for early diagnosis and facilitates the development of new prevention and treatment strategies for CAD [ 36 ]. Further, a prospective cohort study has confirmed that incorporating available clinical variables into the existing predictive score is important for prognostic estimation after myocardial infarction [ 37 ].…”

Predictive nomogram for coronary heart disease in patients with type 2 diabetes mellitus

“…Some nomogram studies often obscure the classification of patients into normal and abnormal status without focusing on their specific values. For example, some studies simply classified patients into hyperlipidemic and non-hyperlipidemic groups ( 47 ). We used blood lipid, nerve conduction velocity, carotid artery plaque, and other results from medical insurance tests in Chinese patients with type 2 diabetes.…”

Development and Validation of a Predictive Model for Coronary Artery Disease Using Machine Learning

“…Therefore, an evolutionary CAD risk appraisal tool developed from recent information of Chinese population would be better generalized. Some recent studies utilized nomogram to assess CAD risks based on the results of multivariate logistic regression or Cox proportional hazard regression ( 47 – 50 ). Albeit these studies provided powerful clinical benefits, these models had the inherent drawback that the algorithm is sensitive to multicollinearity and missing values.…”