Key result
Decision tree models predict coronary artery calcification with ~71% accuracy using basic clinical variables.
Why the study?
Because the absence of CAC is associated with lower CVD incidence and helps risk stratification, the authors sought to build a model predicting the presence of CAC.
Can basic clinical and demographic variables accurately predict the presence of coronary artery calcification in asymptomatic adults aged 40-75?
Cohort (n=3,302)
Yes
Can basic clinical and demographic variables accurately predict the presence of coronary artery calcification in asymptomatic adults aged 40-75?
Effect estimate: Accuracy 71.32% for CART and CIT
Simple clinical and demographic decision tree models can reliably predict coronary artery calcification, which may help physicians selectively target patients for coronary artery calcium CT scanning.
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 3,302 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.
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Park et al. (2021) conducted a cohort in Cardiovascular risk screening (n=3,302). Clinical and demographic risk factors (age, sex, BMI, non-HDL cholesterol, eGFR, HTN, DM) was evaluated on Predictive accuracy for presence of coronary artery calcification (CAC score > 0) (Accuracy 71.32% for CART and CIT). Decision tree models (CART and CIT) accurately predicted the presence of coronary artery calcification with 71.32% accuracy based on basic clinical and demographic variables.
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