Abstract
Carotid-femoral pulse wave velocity (cf-PWV) is an important but difficult to obtain measure of arterial stiffness and an independent predictor of cardiovascular events and all-cause mortality. The objective of this study was to develop a predictive model for cf-PWV based on brachial-ankle pulse wave velocity (baPWV) and other the accessible clinical parameters. This model aims to allow patients to estimate their cf-PWV in advance without the need for direct measurement. We selected participants of the Northern Shanghai community from 2013 to 2022 as the study object. The Pearson correlation coefficient was employed for correlation analysis in feature selection. The linear regression models demonstrated low root mean square error (RMSE), error term (ε), and R(2) values, indicating good predictive performance. A Cox proportional hazards model revealed a significant association between machine learning-predicted cf-PWV and mortality risk, supporting the validity of prediction model. Using a threshold of cf-PWV greater than 10 m/s as the criterion, a classification prediction model was developed. Shapley Additive Explanations (SHAP) analysis was then applied to the Gradient Boosting model to elucidate the predictive mechanism of the optimal model. Without precise instruments, doctors often cannot determine a patient's cf-PWV. When the cf-PWV value predicted by the machine learning algorithm is high, patients can be recommended for more precise measurements to confirm the prediction and emphasize the importance of follow-up health management and psychological support. It is feasible to use a machine learning algorithm based on baPWV and other readily available clinical parameters to predict cf-PWV.