BACKGROUND: Peripheral wave reflection augments central blood pressure and contributes to cardiac load. This pressure augmentation is not quantifiable from brachial cuff pressure but can be determined from carotid pulsations using the augmentation index (AI). However, carotid tonometry is technically challenging and difficult to standardize in practice. We tested whether automated radial pressure analysis provides a viable alternative. METHODS AND RESULTS: Carotid and radial AI (cAI, rAI) were measured in 46 volunteers with a broad range of arterial properties. Data were assessed at rest, during a cold-pressor test, and following 0.4 mg of sublingual nitroglycerin. cAI correlated with rAI independent of age, mean blood pressure (BP), gender or body mass (cAI = 0.79 x rAI - 0.467, r = 0.81, P < 0.00001), with zero mean bias. There was individual variability in the prediction (difference of -4 +/- 23%), though 65% of the estimates fell within 15% of each other. Change in rAI and cAI with provocative maneuvers also correlated (r = 0.77, P < 0.001). Both cAI and rAI were nonlinearly related to late-systolic pressure-time integral (PTI), an index of cardiac load. At cAI < 0.1 or rAI < 0.69, PTI was unaltered, while greater values correlated with increased PTI. rAI accurately predicted this cut-off in 88% of cases, with a 5.5% false negative rate. CONCLUSIONS: Automated rAI analysis is an easily applied method to assess basal and dynamic central pressure augmentation. While individual predictive accuracy of cAI was variable, overall population results were consistent, supporting use of rAI in clinical trials. Its prediction of when AI is associated with greater LV loading (i.e. cardiac risk) is good and may help stratify individual risk along with brachial cuff pressure.
BACKGROUND: Peripheral wave reflection augments central blood pressure and contributes to cardiac load. This pressure augmentation is not quantifiable from brachial cuff pressure but can be determined from carotid pulsations using the augmentation index (AI). However, carotid tonometry is technically challenging and difficult to standardize in practice. We tested whether automated radial pressure analysis provides a viable alternative. METHODS AND RESULTS: Carotid and radial AI (cAI, rAI) were measured in 46 volunteers with a broad range of arterial properties. Data were assessed at rest, during a cold-pressor test, and following 0.4 mg of sublingual nitroglycerin. cAI correlated with rAI independent of age, mean blood pressure (BP), gender or body mass (cAI = 0.79 x rAI - 0.467, r = 0.81, P < 0.00001), with zero mean bias. There was individual variability in the prediction (difference of -4 +/- 23%), though 65% of the estimates fell within 15% of each other. Change in rAI and cAI with provocative maneuvers also correlated (r = 0.77, P < 0.001). Both cAI and rAI were nonlinearly related to late-systolic pressure-time integral (PTI), an index of cardiac load. At cAI < 0.1 or rAI < 0.69, PTI was unaltered, while greater values correlated with increased PTI. rAI accurately predicted this cut-off in 88% of cases, with a 5.5% false negative rate. CONCLUSIONS: Automated rAI analysis is an easily applied method to assess basal and dynamic central pressure augmentation. While individual predictive accuracy of cAI was variable, overall population results were consistent, supporting use of rAI in clinical trials. Its prediction of when AI is associated with greater LV loading (i.e. cardiac risk) is good and may help stratify individual risk along with brachial cuff pressure.
Authors: Vasiliki Bikia; Terence Fong; Rachel E Climie; Rosa-Maria Bruno; Bernhard Hametner; Christopher Mayer; Dimitrios Terentes-Printzios; Peter H Charlton Journal: Eur Heart J Digit Health Date: 2021-10-18