An unsupervised clustering algorithm incorporating pulse wave velocity identified a higher-risk patient group with significantly elevated coronary calcium scores (42 vs. 4.5 Agatston units, p=0.012).
Cross-Sectional (n=377)
No
Does an unsupervised clustering algorithm incorporating cfPWV improve the identification of patients with higher coronary calcium scores and subclinical cardiovascular disease?
Machine learning clustering incorporating pulse wave velocity can effectively stratify patients into distinct cardiovascular risk phenotypes, potentially serving as a gatekeeper for coronary calcium scoring.
Absolute Event Rate: 42% vs 4.5%
p-value: p=0.012
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 post hoc based on cardiovascular risk. A “higher-risk group” had significantly higher left (0.76 vs. 0.70 mm, P 0.001) and right (0.71 vs. 0.66 mm, P = 0.003) intima-media thickness, CCS (42 vs. 4 Agatston units, P = 0.012), and ascending (3.40 vs. 3.20 cm, P 0.001) and descending (2.60 vs. 2.37 cm, P 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.
Portalis et al. (Tue,) conducted a cross-sectional in Cardiovascular risk (n=377). Higher-risk cluster assignment (based on age, weight, height, BP, HR, and cfPWV) vs. Lower-risk cluster assignment was evaluated on Coronary Calcium Score (Agatston units) (p=0.012). An unsupervised clustering algorithm incorporating pulse wave velocity identified a higher-risk patient group with significantly elevated coronary calcium scores (42 vs. 4.5 Agatston units, p=0.012).
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