Participants
Participants were recruited from Los Angeles County and Orange County communities, and all procedures were conducted as part of the VaSC Study at the University of Southern California (USC) and University of California Irvine (UCI). Older adults aged 55 to 89 years who were living independently were included. Exclusion criteria were history of clinical stroke, dementia, major neurological or psychiatric disorder or medications impairing the central nervous system, current organ failure or other uncontrolled systemic illness, or contraindication for brain MRI. Study inclusions and exclusions were verified by a structured clinical health interview and review of current medications with the participant and, when available, an informed study partner. This study was approved by the USC and UCI Institutional Review Boards, all participants gave informed consent, and the study was performed in accordance with all relevant guidelines and regulations. The data that support the findings of this study are available upon reasonable request from the corresponding author, DN.
Continuous BP Data Processing and Analysis
Participants were asked to take medications as normally prescribed and abstain from caffeine the morning of data collection. Beat-to-beat BP measurements were obtained continuously during supine rest in a 3T Siemens MRI scanner, using an MRI compatible non-invasive continuous BP finger cuff device (Biopac®). First, the participant rests for 3 minutes in the supine position prior to the calibration period. During calibration, BP waveforms are acquired by the continuous monitoring device and 2 static pressures are simultaneously acquired using a calibrated, MRI compatible automatic BP device with an inflatable brachial artery cuff (TeslaDUO). These static pressures are used to calibrate the continuous BP monitor using the Caretaker® system (Biopac®). After calibration, continuous BP was monitored during 2 sequential, 7-minute MRI scans.
The Calib upsample utility (Biopac®) was used to extract continuous arterial pressure data obtained during the 2 sequential, 7-minute MRI scans at a sample rate of 100 BP readings per second. Data segments free from obvious motion artifacts were selected from each 7-minute continuous BP data segment for further processing. Waveforms were excluded if more than 10% of the data needed to be excluded to remove obvious motion artifacts. Two example 300-second waveform segments, one with an obvious motion artifact and one without, are shown in Figure 1 for illustration purposes.
A peak detection algorithm was used to identify SBP peaks which served as the basis for further cardiovascular parameter calculation. Peaks were detected using the find_peaks function from the scipy.signal library (40), with default parameters set to a minimum detection height of 80 mmHg, and a minimum peak separation of 40 milliseconds by default. Diastolic troughs were identified as the lowest BP reading between two systolic peaks. Each waveform was then visually inspected using the VaSC BP Signal Toolbox application for erroneous or missing peaks and troughs by TL. Occasionally, default data filtering parameters were adjusted as needed to ensure accurate peak detection. A visual illustration of this process is shown in Figure 2. The VaSC BP Signal Toolbox can be accessed at https://github.com/BP-Signal-Toolbox.git after requesting repository access from the corresponding author, DN.
Calculation of Blood Pressure Variability Metrics
In addition to measurement time, BPV metrics can also be categorized by index type (frequency, dispersion, sequence, or instability) (41, 42). Three measures of BP dispersion (SD, CV, and VIM), 1 measure of BP instability (DSBP), and 1 measure of BP sequence (ARV) were calculated for test-retest comparison in the present study.
The standard deviation of SBP and DBP amplitude measurements (Figure 3A) was obtained across the waveform’s duration as shown in Figure 3B and 3C. BP SD was then further processed into CV and VIM (3). Similar processes were repeated for diastolic BP metrics. Of these metrics, BP SD is used most often due to its straightforward calculation and interpretation, however, it may be correlated with sample mean BP (25, 43). BP CV and BP VIM measures may compliment BP SD because they are independent of mean BP (11, 24, 39, 44), allowing for comparison of samples with different means in the case of CV (45) without average BP adjustment. BP CV is calculated as (BP SD/BP mean)*100 (Figure 3C), while BP VIM is calculated by taking BP SD readings divided by mean BP raised to the power of x, where where x was derived from a non-linear fitting of BP standard deviation (SD) against average BP using the nls package in R (Figure 3C). This is then multiplied by the sample mean BP raised to the power of x and rescaled as needed. Nonlinear curve fitting was performed using the nls function in base R (46).
The difference between the maximum SBP reading and minimum SBP reading (DSBP) was included as a measure of BP instability. DSBP is the difference between the maximum and minimum systolic BP readings in a specified window, 7-minutes for the present study.
Systolic and diastolic ARV measures were calculated by taking the absolute differences between consecutive peaks and troughs respectively, and then averaging them across the 7-minute continuous BP waveform (25, 47) (Figure 3C). To further confirm the reliability of the continuous BP monitoring methodology the intrasession test-retest reliability of HR and BP were also assessed by comparing the HR, mean SBP, and DBP across each selected 7-minute waveform.
Data Analysis
All statistical analyses were carried out using R (46). Paired t-tests were used to compare mean values of waveform 1 and 2 (test – re-test). Intraclass correlation coefficient (ICC) with a 95% confidence interval was used to assess relative reliability using Munro’s criteria (48) for interpretation while absolute reliability was assessed using the standard error of measurement (SEM), SEM%, smallest real difference (SRD), and SRD%. SEM is calculated as the SD of differences between paired measurements divided by the square root of the sample size (49, 50) while SRD represents the smallest change in a measurement that likely represents a true change rather than a measurement error (49, 51). ICC, SEM, and SRD are commonly used measures of test-retest reliability and are specifically used for this purpose in literature (52-55).