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Abstract
Blood pressure variability (BPV) is emerging as an important risk factor across numerous disease states, including cerebrovascular and neurodegenerative disease in older adults. However, there is no current consensus regarding specific use cases for the numerous available BPV metrics. There is also little published data supporting the ability to reliably measure BPV across metrics in older adults. The present study derived BPV metrics from continuous beat-to-beat blood pressure monitoring data. Two sequential 7 min waveforms were analyzed. Absolute and relative reliability testing was performed. Differences between antihypertensive medication users and non-users on BPV metric reliability was also assessed. All sequence and dispersion based BPV metrics displayed good test–retest reliability. A measure of BP instability displayed only moderate reliability. Systolic and diastolic average real variability displayed the highest levels of reliability at ICC = 0.87 and 0.82 respectively. Additionally, systolic average real variability was the most reliable metric in both the antihypertensive use group, and the no antihypertensive use group. In conclusion, beat-to-beat dispersion and sequence-based metrics of BPV can be reliably obtained in older adults using noninvasive continuous blood pressure monitoring. Average real variability may be the most reliable and specific beat-to-beat blood pressure variability metric due to its decreased susceptibility to outliers and low frequency blood pressure oscillations.
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Details
1 University of Southern California, Leonard Davis School of Gerontology, Los Angeles, USA (GRID:grid.42505.36) (ISNI:0000 0001 2156 6853)
2 University of Southern California, Department of Psychology, Los Angeles, USA (GRID:grid.42505.36) (ISNI:0000 0001 2156 6853)
3 University of California, Department of Psychological Science, Irvine, USA (GRID:grid.266093.8) (ISNI:0000 0001 0668 7243)
4 University of Southern California, Leonard Davis School of Gerontology, Los Angeles, USA (GRID:grid.42505.36) (ISNI:0000 0001 2156 6853); University of Southern California, Keck School of Medicine, Los Angeles, USA (GRID:grid.42505.36) (ISNI:0000 0001 2156 6853)