Prediction of cardiovascular events by algorithm- and formula-based pulse wave velocity

Carotid-femoral pulse wave velocity (PWV), a marker of arterial stiffness, is a recognized cardiovascular disease risk factor. As measuring PWV is time-consuming, reliable estimation methods have been developed, but their ability to inform cardiovascular risk prediction beyond what is achievable with current clinical risk tools is uncertain.

This study includes participants aged between 40 and 69 years from the population-based CARTaGENE cohort. PWV estimations (ePWV) were obtained using published formulas (ePWV f ) or algorithmic transformation of pulse waveforms (ePWV algo ) and 10-year cardiovascular risk for each participant was computed using the ASCVD and the SCORE-2 risk equations. Participants were followed during 10 years for major adverse cardiovascular events occurrence (MACE: cardiovascular death, myocardial infarction, stroke). Associations of ePWV f and ePWV algo with MACE were obtained using Cox models adjusted for ASCVD or SCORE-2 in the overall population and in a subpopulation representative of the ePWV f derivation cohort.

Of 17 548 eligible participants, 2263 (12.9%) experienced a MACE during follow-up. Both ePWVf and ePWV algo were associated with MACE in unadjusted analyses, but only ePWV algo remained significant after adjustments for ASCVD [hazard ratio (HR) = 1.16 [1.09-1.22]] and SCORE-2 (HR = 1.07 [1.00-1.13]). In contrast, ePWV f was not associated with MACE after adjustment for either risk score, and only after adjustment with ASCVD when it was tested in the subpopulation representative of its derivation cohort.

Algorithm-based PWV improved cardiovascular risk prediction beyond what is achievable from recognized risk equations, whereas the predictive ability of ePWV f may not be generalizable outside of its reference population.