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Quantitative Autonomic Testing
Published on: July 19, 2011
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Relation between QT interval variability and muscle sympathetic nerve activity in normal subjects
Fatima El-Hamad1, Elisabeth Lambert2, Derek Abbott1
1School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, South Australia, Australia; and.
American Journal of Physiology. Heart and Circulatory Physiology
|August 16, 2015
Summary
Beat-to-beat QT interval variability (QTV) does not significantly reflect muscle sympathetic nerve activity (MSNA) in healthy individuals. This suggests QTV may not be a reliable noninvasive marker for cardiac sympathetic nerve activity during mild orthostatic stress.
Area of Science:
- Cardiovascular Physiology
- Autonomic Nervous System Research
- Noninvasive Cardiac Monitoring
Background:
- Beat-to-beat QT interval variability (QTV) is proposed as a noninvasive index of sympathetic nerve activity.
- A precise quantification of the relationship between QTV and muscle sympathetic nerve activity (MSNA) is lacking.
Purpose of the Study:
- To quantitatively assess the relationship between QTV and MSNA using power contribution analysis.
- To determine if QTV can serve as a surrogate for cardiac sympathetic nerve activity.
Main Methods:
- ECG and MSNA were recorded in 10 healthy subjects under supine and head-up tilt (40°) conditions.
- Power spectrum analysis using a linear autoregressive model decomposed QTV into contributions from heart rate variability (RRV) and MSNA.
Main Results:
- MSNA contributed minimally to QTV and was unaffected by tilt.
- RRV significantly contributed to QTV, decreasing with tilt.
- The majority of QTV originated from sources independent of both RRV and MSNA.
Conclusions:
- Beat-to-beat QTV is not significantly influenced by MSNA modulations during mild orthostatic stress.
- MSNA oscillations may not be a reliable surrogate for cardiac sympathetic nerve activity at moderate activation levels.
- Complex sympathetic influences on QTV may not be captured by linear autoregressive models.
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