Key result
Polynomial Regression and Neural Network models accurately estimated arterial pulse wave velocity using common clinical markers across different vascular conditions (R2 > 0.90).
Why the study?
Assessing arterial stiffness via arterial pulse wave velocity requires specialized equipment, limiting accessibility in primary healthcare centers and low-resource settings.
Can machine learning models accurately estimate arterial pulse wave velocity using common clinical markers?
Can machine learning models accurately estimate arterial pulse wave velocity using common clinical markers?
Effect estimate: R2 > 0.90
Machine learning models, specifically Polynomial Regression and Neural Networks, can accurately estimate arterial pulse wave velocity using routine clinical variables, offering a cost-effective tool for cardiovascular risk stratification.
Facilitates routine PWV estimation for CV risk stratification; extends ML validation across vascular conditions.
Arterial stiffness (AS) is a well-established predictor of cardiovascular events, including myocardial infarction and stroke. One of the most recognized methods for assessing AS is through arterial pulse wave velocity (aPWV), which provides valuable clinical insights into vascular health. However, its measurement typically requires specialized equipment, making it inaccessible in primary healthcare centers and low-resource settings. In this study, we developed and validated different machine learning models to estimate aPWV using common clinical markers routinely collected in standard medical examinations. Thus, we trained five regression models: Linear Regression, Polynomial Regression (PR), Gradient Boosting Regression, Support Vector Regression, and Neural Networks (NNs) on the EVasCu dataset, a cohort of apparently healthy individuals. A 10-fold cross-validation demonstrated that PR and NN achieved the highest predictive performance, effectively capturing nonlinear relationships in the data. External validation on two independent datasets, VascuNET (a healthy population) and ExIC-FEp (a cohort of cardiopathic patients), confirmed the robustness of PR and NN (R2>0.90) across different vascular conditions. These results indicate that by using easily accessible clinical variables and AI-driven insights, it is possible to develop a cost-effective tool for aPWV estimation, enabling early cardiovascular risk stratification in underserved and rural areas where specialized AS measurement devices are unavailable.
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Martínez‐Rodrigo et al. (2025) studied Arterial stiffness. Machine learning models for aPWV estimation was evaluated on Predictive performance (R2) for aPWV estimation (R2 > 0.90). Polynomial Regression and Neural Network models accurately estimated arterial pulse wave velocity using common clinical markers across different vascular conditions (R2 > 0.90).
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