Abstract
Coronary artery calcification (CAC) is a feature of coronary atherosclerosis and a well-known risk factor for cardiovascular disease (CVD). As the absence of CAC is associated with a lower incidence rate of CVD, measurement of a CAC score is helpful for risk stratification when the risk decision is uncertain. This was a retrospective study with an aim to build a model to predict the presence of CAC (i.e., CAC score = 0 or not) and evaluate the discrimination and calibration power of the model. Our data set was divided into two set (80% for training set and 20% for test set). Ten-fold cross-validation was applied with ten times of interaction in each fold. We built prediction models using logistic regression (LRM), classification and regression tree (CART), conditional inference tree (CIT), and random forest (RF). A total of 3302 patients from two cohorts (Soonchunhyang University Cheonan Hospital and Kangbuk Samsung Health Study) were enrolled. These patients’ ages were between 40 and 75 years. All models showed acceptable accuracies (LRM, 70.71%; CART, 71.32%; CIT, 71.32%; and RF, 71.02%). The decision tree model using CART and CIT showed a reasonable accuracy without complexity. It could be implemented in real-world practice.
Highlights
Coronary artery calcification (CAC), a feature of coronary atherosclerosis [1], is a well-known risk factor for cardiovascular disease (CVD) [2]
Measuring a CAC score can be burdensome in terms of the financial aspects of patients and insurance, previous cost-effectiveness analyses have revealed that CAC testing is cost-effective for asymptomatic patients [4,5,6]
We have shown that insulin resistance [9], lipoprotein(a) [10], hemoglobin glycation index [11], non-alcoholic fatty liver disease (NAFLD) [12], and systemic inflammation [12] are related to progression of CAC in Koreans, similar to the results of the Multi-Ethnic Study of Atherosclerosis (MESA)
Summary
Coronary artery calcification (CAC), a feature of coronary atherosclerosis [1], is a well-known risk factor for cardiovascular disease (CVD) [2]. A CAC score can be implemented for risk stratification when a risk decision is uncertain [2]. Measuring a CAC score may be a beneficial approach, especially for patients with atherosclerotic cardiovascular disease (ASCVD) risk of 5–7.5% [3]. The absence of CAC is associated with a lower incidence rate of CVD [4]. Measuring a CAC score can be burdensome in terms of the financial aspects of patients and insurance, previous cost-effectiveness analyses have revealed that CAC testing is cost-effective for asymptomatic patients [4,5,6]
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