AimCoronary artery calcification (CAC), as a sign of atherosclerosis, can be detected and progression quantified using computed tomography (CT). We develop a tool for predicting CAC progression.Methods and resultsIn 3481 participants (45–74 years, 53.1% women) CAC percentiles at baseline (CACb) and after five years (CAC5y) were evaluated, demonstrating progression along gender-specific percentiles, which showed exponentially shaped age-dependence. Using quantile regression on the log-scale (log(CACb+1)) we developed a tool to individually predict CAC5y, and compared to observed CAC5y. The difference between observed and predicted CAC5y (log-scale, mean±SD) was 0.08±1.11 and 0.06±1.29 in men and women. Agreement reached a kappa-value of 0.746 (95% confidence interval: 0.732–0.760) and concordance correlation (log-scale) of 0.886 (0.879–0.893). Explained variance of observed by predicted log(CAC5y+1) was 80.1% and 72.0% in men and women, and 81.0 and 73.6% including baseline risk factors. Evaluating the tool in 1940 individuals with CACb>0 and CACb<400 at baseline, of whom 242 (12.5%) developed CAC5y>400, yielded a sensitivity of 59.5%, specificity 96.1%, (+) and (−) predictive values of 68.3% and 94.3%. A pre-defined acceptance range around predicted CAC5y contained 68.1% of observed CAC5y; only 20% were expected by chance. Age, blood pressure, lipid-lowering medication, diabetes, and smoking contributed to progression above the acceptance range in men and, excepting age, in women.ConclusionCAC nearly inevitably progresses with limited influence of cardiovascular risk factors. This allowed the development of a mathematical tool for prediction of individual CAC progression, enabling anticipation of the age when CAC thresholds of high risk are reached.