Abstract
Current distance measurement techniques for pulse wave velocity (PWV) calculation are susceptible to intercenter variability. The aim of this study was to derive and validate a formula for this distance measurement. Based on carotid femoral distance in 1183 whole-body magnetic resonance angiograms, a formula was derived for calculating distance. This was compared with distance measurements in 128 whole-body magnetic resonance angiograms from a second study. The effects of recalculation of PWV using the new formula on association with risk factors, disease discrimination, and prediction of major adverse cardiovascular events were examined within 1242 participants from the multicenter SUMMIT study (Surrogate Markers of Micro- and Macrovascular Hard End-Points for Innovative Diabetes Tools) and 825 participants from the Caerphilly Prospective Study. The distance formula yielded a mean error of 7.8 mm (limits of agreement =-41.1 to 56.7 mm; P<0.001) compared with the second whole-body magnetic resonance angiogram group. Compared with an external distance measurement, the distance formula did not change associations between PWV and age, blood pressure, or creatinine (P<0.01) but did remove significant associations between PWV and body mass index (BMI). After accounting for differences in age, sex, and mean arterial pressure, intercenter differences in PWV persisted using the external distance measurement (F=4.6; P=0.004), whereas there was a loss of between center difference using the distance formula (F=1.4; P=0.24). PWV odds ratios for cardiovascular mortality remained the same using both the external distance measurement (1.14; 95% confidence interval, 1.06-1.24; P=0.001) and the distance formula (1.17; 95% confidence interval, 1.08-1.28; P<0.001). A population-derived automatic distance calculation for PWV obtained from routinely collected clinical information is accurate and removes intercenter measurement variability without impacting the diagnostic utility of carotid-femoral PWV.
Highlights
Abstract —Current distance measurement techniques for pulse wave velocity (PWV) calculation are susceptible to intercenter variability
The aim of this study was 4-fold: first to develop a formula for measuring arterial path length using only routine clinical data by training it against an magnetic resonance imaging (MRI)-based gold standard; second, to examine the performance of the formula in predicting the MRI-based path length in a validation data set; the third aim was to test whether use of this formula in 2 separate data sets from the training data set reduces intercenter differences in the distribution of PWV measurements from similar clinical groups; and to test whether covariate associations with PWV using the new MRI learned path length formula differ from the associations using the current routinely used distance techniques in the 2 validation data sets
Using this formula strengthens the association of PWV with traditional risk factors and removes differences between centers but does not reduce discrimination between those with and without cardiovascular disease, nor does it reduce the prognostic strength of PWV
Summary
Abstract —Current distance measurement techniques for pulse wave velocity (PWV) calculation are susceptible to intercenter variability. Arteriosclerosis is the stiffening of the arterial wall, which occurs with advancing age and is strongly associated with risk of future cardiovascular events.[1] Carotid–femoral pulse wave velocity (PWV) is the current gold standard for the assessment of aortic stiffness and has been included in guidelines on blood pressure management and as an end point in randomized clinical trials.[2,3]. The aim of this study was 4-fold: first to develop a formula for measuring arterial path length using only routine clinical data by training it against an MRI-based gold standard; second, to examine the performance of the formula in predicting the MRI-based path length in a validation data set; the third aim was to test whether use of this formula in 2 separate data sets from the training data set reduces intercenter differences in the distribution of PWV measurements from similar clinical groups; and to test whether covariate associations with PWV using the new MRI learned path length formula differ from the associations using the current routinely used distance techniques in the 2 validation data sets.
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