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

Area of Science:

• Cardiology
• Vascular Biology
• Preventive Medicine

Background:

• Carotid-femoral pulse wave velocity (PWV) is a key indicator of arterial stiffness and a cardiovascular disease risk factor.
• Current methods for measuring PWV are time-consuming, prompting the development of estimation techniques.
• The clinical utility of estimated PWV (ePWV) in improving cardiovascular risk prediction beyond existing tools remains uncertain.

Purpose of the Study:

• To evaluate the ability of two ePWV methods—formula-based (ePWV f) and algorithm-based (ePWV algo)—to predict major adverse cardiovascular events (MACE).
• To determine if ePWV improves cardiovascular risk prediction when added to established risk scores like ASCVD and SCORE-2.

Main Methods:

• Utilized data from the population-based CARTaGENE cohort (40-69 years).
• Calculated ePWV using published formulas (ePWV f) and algorithmic transformation (ePWV algo).
• Assessed 10-year MACE risk using ASCVD and SCORE-2 equations, then employed Cox models to analyze associations between ePWV and MACE, adjusting for risk scores.

Main Results:

• Of 17,548 participants, 2263 (12.9%) experienced MACE.
• ePWV algo was significantly associated with MACE after adjustment for ASCVD (HR=1.16) and SCORE-2 (HR=1.07).
• ePWV f did not show significant association with MACE after adjustment for either risk score in the overall cohort.

Conclusions:

• Algorithm-based ePWV enhances cardiovascular risk prediction beyond conventional risk equations.
• The predictive performance of formula-based ePWV may be limited to specific populations from which it was derived.
• Algorithmic ePWV offers a more generalizable and valuable tool for cardiovascular risk assessment.