Computational clustering reveals differentiated coronary artery calcium progression at prevalent levels of pulse wave velocity by classifying high-risk patients.

Many studies found that increased arterial stiffness is significantly associated with the presence and progression of Coronary Calcium Score (CCS). However, none so far have used machine learning algorithms to improve their value. Therefore, this study aims to evaluate the association between carotid-femoral Pulse Wave Velocity (cfPWV) and CCS score through computational clustering. We conducted a retrospective cross-sectional study using data from a cardiovascular risk screening program that included 377 participants. We used an unsupervised clustering algorithm using age, weight, height, blood pressure, heart rate, and cfPWV as input variables. Differences between cluster groups were analyzed through Chi-square and T-student tests. The association between (i) cfPWV and age groups, (ii) log (CCS) and age groups, and (iii) cfPWV and log(CCS) were addressed through linear regression analysis. Clusters were labeled based on cardiovascular risk. A "higher-risk group" had significantly higher left (0.76 vs. 0.70 mm,  < 0.001) and right (0.71 vs. 0.66 mm,  = 0.003) intima-media thickness, CCS (42 vs. 4 Agatston units,  = 0.012), and ascending (3.40 vs. 3.20 cm,  < 0.001) and descending (2.60 vs. 2.37 cm,  < 0.001) aorta diameters. Association with age appeared linear for cfPWV and exponential for log (CCS). The progression of the log (CCS) and cfPWV through age groups was steeper in the "higher-risk group" than in the "lower-risk group". cfPWV strongly correlated with CCS, and CCS progression over cfPWV differed among clusters. This finding could improve PWV as a "gate-keeper" of CCS testing and potentially enhance cardiovascular risk stratification.