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Estimation of central pressure augmentation using automated radial artery tonometry
Melenovsky V, Borlaug BA, Fetics B, Kessler K, Shively L, Kass DA.
Jazyk angličtina Země Velká Británie
NLK
Journals@Ovid Ovid Full Text
od 1999-01-01 do 2010-02-01
- MeSH
- arteria radialis fyziologie MeSH
- arteriae carotides fyziologie MeSH
- dospělí MeSH
- financování organizované MeSH
- hypertenze diagnóza patofyziologie MeSH
- kontrakce myokardu fyziologie MeSH
- krevní tlak fyziologie MeSH
- lidé středního věku MeSH
- lidé MeSH
- manometrie metody přístrojové vybavení MeSH
- měření krevního tlaku metody přístrojové vybavení MeSH
- mladiství MeSH
- pružnost MeSH
- pulzatilní průtok fyziologie MeSH
- rychlost toku krve fyziologie MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
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.
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- $a 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.
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