Development and validation of a novel nomogram to predict coronary artery calcification: a single center study

To identify the factors influencing coronary artery calcification (CAC), develop a predictive model for CAC, and evaluate its effectiveness.

This retrospective study included 1,526 patients who underwent coronary CT scans at our hospital between January 2020 and October 2024. Patient data included basic demographic information (age, sex, BMI, smoking, alcohol consumption, hypertension, coronary artery disease, hyperlipidemia, diabetes, cerebrovascular disease, etc.) and laboratory test results (TSH, FT4, FT3, LDL, HDL, VLDL, TG, TCHO, etc.). Lasso and logistic regression analyses were conducted to identify significant factors, and a nomogram was developed. The model's performance was evaluated using calibration plots, ROC curves, AUC values, and decision curve analysis (DCA).

Univariate analysis showed that the non-calcification group had a longer history of hyperlipidemia and alcohol consumption, and higher triglyceride levels. In the calcification group, patients were older, predominantly male, had higher triglyceride blood glucose index (TyG) levels, and had longer durations of coronary artery disease, hypertension, diabetes, cerebral infarction, and smoking (P< 0.05). Multivariate analysis revealed that age, sex, and the duration of coronary artery disease, hypertension, diabetes, hyperlipidemia, smoking, and alcohol consumption were significantly associated with coronary artery calcification (P< 0.05).

This study identified sex, age, and the duration of coronary artery disease, hypertension, diabetes, hyperlipidemia, smoking, and alcohol consumption as key factors influencing coronary artery calcification. The predictive model developed using these factors demonstrated strong predictive performance.