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Coupling between arterial pressure, cerebral blood velocity, and cerebral tissue oxygenation with spontaneous and forced oscillations
This content has been downloaded from IOPscience. Please scroll down to see the full text. 2015 Physiol. Meas. 36 785 (http://iopscience.iop.org/0967-3334/36/4/785) View the table of contents for this issue, or go to the journal homepage for more
Download details: IP Address: 149.150.51.237 This content was downloaded on 28/03/2015 at 14:32
Please note that terms and conditions apply.
Institute of Physics and Engineering in Medicine Physiol. Meas. 36 (2015) 785–801
Physiological Measurement doi:10.1088/0967-3334/36/4/785
Coupling between arterial pressure, cerebral blood velocity, and cerebral tissue oxygenation with spontaneous and forced oscillations Caroline A Rickards1, Justin D Sprick1, Hannah B Colby1, Victoria L Kay1 and Yu-Chieh Tzeng2 1
Department of Integrative Physiology & Anatomy, University of North Texas Health Science Center, 3500 Camp Bowie Boulevard, Fort Worth, TX 76107, USA 2 Cardiovascular Systems Laboratory, Centre for Translational Physiology, University of Otago, 23A Mein St, Wellington, 6242, New Zealand E-mail: [email protected] Received 6 October 2014, revised 19 December 2014 Accepted for publication 26 January 2015 Published 23 March 2015
Abstract
We tested the hypothesis that transmission of arterial pressure to brain tissue oxygenation is low under conditions of arterial pressure instability. Two experimental models of hemodynamic instability were used in healthy human volunteers; (1) oscillatory lower body negative pressure (OLBNP) (N = 8; 5 male, 3 female), and; (2) maximal LBNP to presyncope (N = 21; 13 male, 8 female). Mean arterial pressure (MAP), middle cerebral artery velocity (MCAv), and cerebral tissue oxygen saturation (ScO2) were measured noninvasively. For the OLBNP protocol, between 0 and −60 mmHg negative pressure was applied for 20 cycles at 0.05 Hz, then 20 cycles at 0.1 Hz. For the maximal LBNP protocol, progressive 5 min stages of chamber decompression were applied until the onset of presyncope. Spectral power of MAP, mean MCAv, and ScO2 were calculated within the VLF (0.04–0.07 Hz), and LF (0.07–0.2 Hz) ranges, and cross-spectral coherence was calculated for MAP-mean MCAv, MAP-ScO2, and mean MCAv-ScO2 at baseline, during each OLBNP protocol, and at the level prior to pre-syncope during maximal LBNP (sub-max). The key findings are (1) both 0.1 Hz OLBNP and sub-max LBNP elicited increases in LF power for MAP, mean MCAv, and ScO2 (p ≤ 0.08); (2) 0.05 Hz OLBNP increased VLF power in MAP and ScO2 only (p ≤ 0.06); (3) coherence between MAP-mean MCAv was consistently higher (≥0.71) compared with MAP-ScO2, and mean MCAv-ScO2 (≤0.43) 0967-3334/15/040785+17$33.00 © 2015 Institute of Physics and Engineering in Medicine Printed in the UK
785
C A Rickards et al
Physiol. Meas. 36 (2015) 785
during both OLBNP protocols, and sub-max LBNP (p ≤ 0.04). These data indicate high linearity between pressure and cerebral blood flow variations, but reduced linearity between cerebral tissue oxygenation and both arterial pressure and cerebral blood flow. Measuring arterial pressure variability may not always provide adequate information about the downstream effects on cerebral tissue oxygenation, the key end-point of interest for neuronal viability. Keywords: oscillations, cerebral, tissue oxgyenation (Some figures may appear in colour only in the online journal) 1. Introduction Conventional cardiovascular risk-stratification relies heavily on the assumption that static measurements of brachial blood pressure alone accounts for all blood pressure-related risk of vascular events. However, there is growing recognition that this paradigm is overly simplistic and that blood pressure may have significant limitations for detecting adverse changes in the intra-cranial environment. While blood pressure is commonly monitored on the premise that it informs the state of cerebral perfusion, it is also well established that cerebral blood flow is affected not only by blood pressure, but also by physiological processes such as cerebral autoregulation (Zhang et al 1998a, Panerai et al 2002). Consequently, individuals with similar blood pressure can have disparate levels of brain perfusion depending on their individual autoregulation capacity. Similarly, measuring the oxygenation of arterial blood using conventional digit pulse oximetry may not accurately reflect brain tissue oxygenation (Tobias 2008), which is a complex variable that is influenced by local metabolic conditions of oxygen supply and consumption. These are some reasons why direct monitoring of cerebral blood flow, brain tissue oxygenation, and cerebral autoregulation have become conceptually attractive strategies for optimizing outcomes in critical care settings (Kirkpatrick et al 1998, Johnston and Czosnyka 2003, Reinhard et al 2005). Despite decades of research, informed hemodynamic management in many areas of neurocritical care remains enigmatic (Grise and Adeoye 2012). For example, observational studies have shown that blood pressure is frequently elevated during acute stroke and recent data indicate that rapid blood pressure lowering improves functional outcome in hemorrhagic stroke (Anderson et al 2013). However, innumerable clinical trials on the effectiveness of blood pressure lowering therapy in acute ischemic stroke have generated divergent results (Hubert et al 2013). This has prompted speculation that hemodynamic management based on systemic blood pressure monitoring may be too crude to achieve consistent optimization of local cerebral perfusion and oxygenation (Hadjiev and Mineva 2013). In light of these practical challenges, the goal of this study is to determine the extent to which blood pressure changes can be used as surrogate indices of cerebral blood flow and brain tissue oxygenation under situations where blood pressure and cerebral perfusion may be threatened. We used static and oscillatory lower body negative pressure (OLBNP) as experimental models of hemodynamic instability and linear transfer function analysis to define the timescale and magnitude of the input–output relations between blood pressure, cerebral blood flow, and brain tissue oxygenation. We hypothesized that the transmission of pressure to brain tissue oxygenation is low across all frequency ranges (0.04–0.40 Hz) under both experimental models of hemodynamic instability. 786
C A Rickards et al
Physiol. Meas. 36 (2015) 785
2. Methods Data for this study were generated from two independent protocols; (1) OLBNP at 0.05 and 0.1 Hz, and; (2) maximal LBNP to presyncope. Each protocol is described separately. 2.1. Oscillatory lower body negative pressure 2.1.1. Subjects. Eight healthy, normotensive, non-smoking subjects (5 male, 3 female; age,
28 ± 1 years; height, 169 ± 3 cm; weight, 75 ± 5 kg; mean ± SE) volunteered to participate in this study. This protocol is part of an ongoing larger study consisting of additional experiments and interventions that will be published separately at a later date, but do not impact the outcome of the data presented herein. The experiments were conducted in the Department of Integrative Physiology & Anatomy at the University of North Texas Health Science Center (UNTHSC), and all experimental protocols and procedures were reviewed and approved by the UNTHSC Institutional Review Board. A complete medical history and physical examination were conducted on each potential subject prior to their participation in the study. All female subjects were tested within days 1–4 of the early follicular phase of their menstrual cycle, and were also administered a urine pregnancy test prior to experimentation to ensure they were not pregnant. Due to the potential effects on autonomic function, all subjects were instructed to maintain their normal sleep patterns in addition to abstaining from exercise, alcohol, caffeine and other pharmacological stimulants 24 h before each protocol. After familiarization with the laboratory, subjects were briefed with a description of all procedures and risks associated with the experiments and each gave written informed consent to participate in the study. 2.1.2. Instrumentation. Subjects were placed in the supine position with their lower body
inside a LBNP chamber (VUV Analytics, Austin, TX) and positioned on a bicycle seat to ensure they did not move during chamber decompression. Durable plastic and a neoprene band were wrapped around the subject’s waist to create an airtight seal with the LBNP chamber. Subjects were instrumented with a standard lead II ECG (shielded leads, cable and amplifier, AD Instruments, Bella Vista, NSW, Australia), infrared finger photoplethysmography to measure beat-to-beat arterial blood pressure (Finometer®, TNO-TPD Biomedical Instrumentation, Amsterdam, The Netherlands), a 2 MHz transcranial Doppler (TCD) ultrasound probe for assessment of blood velocity in the middle cerebral artery (MCAv; ST3, Spencer Technologies, Seattle, WA), a flat near infrared spectroscopy (NIRS) sensor over the right or left frontal lobe for assessment of oxy-hemoglobin (HbO2), deoxy-hemoglobin (dHb), and cerebral oxygen saturation (ScO2; OxiplexTS, ISS Inc., Champaign-Urbana, IL), and a nasal cannula to capture expired gases for assessment of end-tidal CO2 (etCO2) and respiration rate (ML206 Gas Analyzer, AD Instruments, Bella Vista, NSW, Australia). For the majority of subjects, MCAv and ScO2 were measured on the same side. All signals were recorded at 1000 Hz using commercially available data acquisition hardware and software (PowerLab and LabChart Pro, AD Instruments, Bella Vista, NSW, Australia). 2.1.3. Study design. Following instrumentation, 5 min of resting baseline data was collected.
OLBNP between 0 and −60 mmHg was applied for 20 cycles at 0.05 Hz (very low frequency, VLF), then 20 cycles at 0.1 Hz (low frequency, LF), with a 5 min rest period between each protocol. Subjects remained as relaxed as possible, and breathed spontaneously throughout the OLBNP profiles. Following completion of the OLBNP protocols, subjects remained in the supine posture for an additional 5 min recovery period. 787
C A Rickards et al
Physiol. Meas. 36 (2015) 785
2.1.4. Data analysis. All waveform data was exported into commercial analysis software (WinCPRS, Absolute Aliens, Turku, Finland). R waves generated from the ECG signal were detected and marked at their occurrence in time. Heart rate (HR) was derived from the R–R interval (RRI) signal. Diastolic arterial pressure (DAP) and systolic arterial pressure (SAP) were marked from the arterial blood pressure tracings. Mean arterial pressure (MAP) and mean MCAv were automatically calculated from the area under the arterial pressure and MCAv tracings via the WinCPRS software. Stroke volume (SV) was estimated on a beat-tobeat basis by the Finometer® using the established pulse contour method (Jansen et al 1990) and recorded directly to the LabChart file. Cardiac output (CO) was calculated as the product of HR and SV, total peripheral resistance (TPR) was calculated as MAP divided by CO, and cerebrovascular resistance (CVR) was calculated as MAP divided by mean MCAv. All variables were calculated from the 300 s baseline period, and from the 20-cycles of 0.05 Hz OLBNP (400 s) and 0.1 Hz OLBNP (200 s). Oscillatory patterns of MAP, mean MCAv, and ScO2 were determined with fast Fourier power spectral analysis. Data were made equidistant by interpolating linearly and re-sampling at 5 Hz. Data were then passed through a low-pass filter with a cutoff frequency of 0.5 Hz. Data sets were fast Fourier transformed with a Hanning window to obtain power spectra. Spectral power was expressed as the integrated area within the VLF range (0.04–0.07 Hz), and LF range (0.07–0.2 Hz). The coherence between MAP and mean MCAv, MAP and ScO2, and mean MCAv and ScO2 was calculated by dividing the squared cross-spectral densities of the two signals by the product of the individual autospectra (60 s windows). 2.1.5. Statistics. One-way repeated measures ANOVAs were used for comparison of all responses (baseline, 0.05 Hz OLBNP, 0.1 Hz OLBNP). For frequency domain variables, comparisons were made only within a frequency band (VLF, LF). Holm–Sidak post-hoc tests were used to compare responses to the OLBNP protocols versus baseline only. Paired t-tests were used to compare MAP-ScO2 coherence and mean MCAv-ScO2 coherence to MAP-mean MCAv coherence within a specific frequency band. Unless otherwise stated, all data are presented as mean ± SE, and exact p values are reported for comparisons. 2.2. Maximal LBNP to presyncope 2.2.1. Subjects. Twenty-one healthy, normotensive, non-smoking subjects (13 male, 8 female; age, 27 ± 1 years; height, 171 ± 2 cm; weight, 77 ± 3 kg; mean ± SE) were included in the analysis of this study. Twenty-five subjects completed this protocol, but four did not reach true pre-syncope according to the criteria described below. Six of the final 21 subjects also participated in the OLBNP study. The experiments were conducted in the Department of Integrative Physiology & Anatomy at the University of North Texas Health Science Center (UNTHSC), and all experimental protocols and procedures were reviewed and approved by the UNTHSC Institutional Review Board. A complete medical history and physical examination were conducted on each potential subject prior to their participation in the study. All inclusion and exclusion criteria were identical to the OLBNP study. After familiarization with the laboratory, subjects were briefed with a description of all procedures and risks associated with the experiments and each gave written informed consent to participate in the study. 2.2.2. Study design. Subject instrumentation was identical to the OLBNP study. The maximal LBNP protocol consisted of a 5 min baseline period followed by progressive 5 min stages of chamber decompression at −15, −30, −45, −60, −70, −80, −90, and −100 mmHg. The protocol was terminated following completion of 5 min at −100 mmHg LBNP, or with the onset 788
C A Rickards et al
Physiol. Meas. 36 (2015) 785
Table 1. Time domain cardiovascular responses to OLBNP at 0.05 Hz (VLF) and
0.1 Hz (LF) (N = 8).
Physiological parameter
Baseline
0.05 Hz OLBNP
0.1 Hz OLBNP
RRI (ms) HR (beats min−1) MAP (mmHg) SV (ml) CO (l min−1) TPR (mmHg l−1 min−1) Mean MCAv (cm s−1) CVR (mmHg cm−1 s−1) ScO2 (%) HbO2 (µM) dHb (µM) etCO2 (mmHg) Respiration rate (breaths min−1)
1004 ± 36 62.0 ± 2.6 91.9 ± 3.1 98.6 ± 4.3 6.1 ± 0.4 15.2 ± 0.5 69.2 ± 4.2 1.3 ± 0.1 66.9 ± 1.7 33.0 ± 3.0 16.1 ± 1.2 41.4 ± 1.1 12.3 ± 1.3
989 ± 36 61.8 ± 2.6 90.7 ± 2.6 99.0 ± 4.7 6.1 ± 0.3 15.0 ± 0.4 65.4 ± 4.3 1.4 ± 0.1a 66.1 ± 2.1 32.3 ± 3.3 16.1 ± 1.2 40.0 ± 1.3 13.4 ± 1.3
986 ± 40 62.1 ± 2.9 93.6 ± 2.9 99.0 ± 4.9 6.1 ± 0.3 15.5 ± 0.5 66.8 ± 4.7 1.4 ± 0.1a 66.2 ± 2.1 32.2 ± 3.2 16.0 ± 1.2 40.0 ± 1.3 11.9 ± 1.7
a
p ≤ 0.04 compared to baseline (one-way repeated measures ANOVA). OLBNP, oscillatory lower body negative pressure; RRI, R–R interval; HR, heart rate; MAP, mean arterial pressure; SV, stroke volume; CO, cardiac output; TPR, total peripheral resistance; MCAv, middle cerebral artery velocity; CVR, cerebrovascular resistance; ScO2, cerebral oxygen saturation; HbO2, oxy-hemoglobin; dHb, deoxy-hemoglobin; etCO2, end-tidal carbon dioxide.
of presyncope, defined as one or a combination of the following criteria: sudden bradycardia, SAP
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My IOPscience
Coupling between arterial pressure, cerebral blood velocity, and cerebral tissue oxygenation with spontaneous and forced oscillations
This content has been downloaded from IOPscience. Please scroll down to see the full text. 2015 Physiol. Meas. 36 785 (http://iopscience.iop.org/0967-3334/36/4/785) View the table of contents for this issue, or go to the journal homepage for more
Download details: IP Address: 149.150.51.237 This content was downloaded on 28/03/2015 at 14:32
Please note that terms and conditions apply.
Institute of Physics and Engineering in Medicine Physiol. Meas. 36 (2015) 785–801
Physiological Measurement doi:10.1088/0967-3334/36/4/785
Coupling between arterial pressure, cerebral blood velocity, and cerebral tissue oxygenation with spontaneous and forced oscillations Caroline A Rickards1, Justin D Sprick1, Hannah B Colby1, Victoria L Kay1 and Yu-Chieh Tzeng2 1
Department of Integrative Physiology & Anatomy, University of North Texas Health Science Center, 3500 Camp Bowie Boulevard, Fort Worth, TX 76107, USA 2 Cardiovascular Systems Laboratory, Centre for Translational Physiology, University of Otago, 23A Mein St, Wellington, 6242, New Zealand E-mail: [email protected] Received 6 October 2014, revised 19 December 2014 Accepted for publication 26 January 2015 Published 23 March 2015
Abstract
We tested the hypothesis that transmission of arterial pressure to brain tissue oxygenation is low under conditions of arterial pressure instability. Two experimental models of hemodynamic instability were used in healthy human volunteers; (1) oscillatory lower body negative pressure (OLBNP) (N = 8; 5 male, 3 female), and; (2) maximal LBNP to presyncope (N = 21; 13 male, 8 female). Mean arterial pressure (MAP), middle cerebral artery velocity (MCAv), and cerebral tissue oxygen saturation (ScO2) were measured noninvasively. For the OLBNP protocol, between 0 and −60 mmHg negative pressure was applied for 20 cycles at 0.05 Hz, then 20 cycles at 0.1 Hz. For the maximal LBNP protocol, progressive 5 min stages of chamber decompression were applied until the onset of presyncope. Spectral power of MAP, mean MCAv, and ScO2 were calculated within the VLF (0.04–0.07 Hz), and LF (0.07–0.2 Hz) ranges, and cross-spectral coherence was calculated for MAP-mean MCAv, MAP-ScO2, and mean MCAv-ScO2 at baseline, during each OLBNP protocol, and at the level prior to pre-syncope during maximal LBNP (sub-max). The key findings are (1) both 0.1 Hz OLBNP and sub-max LBNP elicited increases in LF power for MAP, mean MCAv, and ScO2 (p ≤ 0.08); (2) 0.05 Hz OLBNP increased VLF power in MAP and ScO2 only (p ≤ 0.06); (3) coherence between MAP-mean MCAv was consistently higher (≥0.71) compared with MAP-ScO2, and mean MCAv-ScO2 (≤0.43) 0967-3334/15/040785+17$33.00 © 2015 Institute of Physics and Engineering in Medicine Printed in the UK
785
C A Rickards et al
Physiol. Meas. 36 (2015) 785
during both OLBNP protocols, and sub-max LBNP (p ≤ 0.04). These data indicate high linearity between pressure and cerebral blood flow variations, but reduced linearity between cerebral tissue oxygenation and both arterial pressure and cerebral blood flow. Measuring arterial pressure variability may not always provide adequate information about the downstream effects on cerebral tissue oxygenation, the key end-point of interest for neuronal viability. Keywords: oscillations, cerebral, tissue oxgyenation (Some figures may appear in colour only in the online journal) 1. Introduction Conventional cardiovascular risk-stratification relies heavily on the assumption that static measurements of brachial blood pressure alone accounts for all blood pressure-related risk of vascular events. However, there is growing recognition that this paradigm is overly simplistic and that blood pressure may have significant limitations for detecting adverse changes in the intra-cranial environment. While blood pressure is commonly monitored on the premise that it informs the state of cerebral perfusion, it is also well established that cerebral blood flow is affected not only by blood pressure, but also by physiological processes such as cerebral autoregulation (Zhang et al 1998a, Panerai et al 2002). Consequently, individuals with similar blood pressure can have disparate levels of brain perfusion depending on their individual autoregulation capacity. Similarly, measuring the oxygenation of arterial blood using conventional digit pulse oximetry may not accurately reflect brain tissue oxygenation (Tobias 2008), which is a complex variable that is influenced by local metabolic conditions of oxygen supply and consumption. These are some reasons why direct monitoring of cerebral blood flow, brain tissue oxygenation, and cerebral autoregulation have become conceptually attractive strategies for optimizing outcomes in critical care settings (Kirkpatrick et al 1998, Johnston and Czosnyka 2003, Reinhard et al 2005). Despite decades of research, informed hemodynamic management in many areas of neurocritical care remains enigmatic (Grise and Adeoye 2012). For example, observational studies have shown that blood pressure is frequently elevated during acute stroke and recent data indicate that rapid blood pressure lowering improves functional outcome in hemorrhagic stroke (Anderson et al 2013). However, innumerable clinical trials on the effectiveness of blood pressure lowering therapy in acute ischemic stroke have generated divergent results (Hubert et al 2013). This has prompted speculation that hemodynamic management based on systemic blood pressure monitoring may be too crude to achieve consistent optimization of local cerebral perfusion and oxygenation (Hadjiev and Mineva 2013). In light of these practical challenges, the goal of this study is to determine the extent to which blood pressure changes can be used as surrogate indices of cerebral blood flow and brain tissue oxygenation under situations where blood pressure and cerebral perfusion may be threatened. We used static and oscillatory lower body negative pressure (OLBNP) as experimental models of hemodynamic instability and linear transfer function analysis to define the timescale and magnitude of the input–output relations between blood pressure, cerebral blood flow, and brain tissue oxygenation. We hypothesized that the transmission of pressure to brain tissue oxygenation is low across all frequency ranges (0.04–0.40 Hz) under both experimental models of hemodynamic instability. 786
C A Rickards et al
Physiol. Meas. 36 (2015) 785
2. Methods Data for this study were generated from two independent protocols; (1) OLBNP at 0.05 and 0.1 Hz, and; (2) maximal LBNP to presyncope. Each protocol is described separately. 2.1. Oscillatory lower body negative pressure 2.1.1. Subjects. Eight healthy, normotensive, non-smoking subjects (5 male, 3 female; age,
28 ± 1 years; height, 169 ± 3 cm; weight, 75 ± 5 kg; mean ± SE) volunteered to participate in this study. This protocol is part of an ongoing larger study consisting of additional experiments and interventions that will be published separately at a later date, but do not impact the outcome of the data presented herein. The experiments were conducted in the Department of Integrative Physiology & Anatomy at the University of North Texas Health Science Center (UNTHSC), and all experimental protocols and procedures were reviewed and approved by the UNTHSC Institutional Review Board. A complete medical history and physical examination were conducted on each potential subject prior to their participation in the study. All female subjects were tested within days 1–4 of the early follicular phase of their menstrual cycle, and were also administered a urine pregnancy test prior to experimentation to ensure they were not pregnant. Due to the potential effects on autonomic function, all subjects were instructed to maintain their normal sleep patterns in addition to abstaining from exercise, alcohol, caffeine and other pharmacological stimulants 24 h before each protocol. After familiarization with the laboratory, subjects were briefed with a description of all procedures and risks associated with the experiments and each gave written informed consent to participate in the study. 2.1.2. Instrumentation. Subjects were placed in the supine position with their lower body
inside a LBNP chamber (VUV Analytics, Austin, TX) and positioned on a bicycle seat to ensure they did not move during chamber decompression. Durable plastic and a neoprene band were wrapped around the subject’s waist to create an airtight seal with the LBNP chamber. Subjects were instrumented with a standard lead II ECG (shielded leads, cable and amplifier, AD Instruments, Bella Vista, NSW, Australia), infrared finger photoplethysmography to measure beat-to-beat arterial blood pressure (Finometer®, TNO-TPD Biomedical Instrumentation, Amsterdam, The Netherlands), a 2 MHz transcranial Doppler (TCD) ultrasound probe for assessment of blood velocity in the middle cerebral artery (MCAv; ST3, Spencer Technologies, Seattle, WA), a flat near infrared spectroscopy (NIRS) sensor over the right or left frontal lobe for assessment of oxy-hemoglobin (HbO2), deoxy-hemoglobin (dHb), and cerebral oxygen saturation (ScO2; OxiplexTS, ISS Inc., Champaign-Urbana, IL), and a nasal cannula to capture expired gases for assessment of end-tidal CO2 (etCO2) and respiration rate (ML206 Gas Analyzer, AD Instruments, Bella Vista, NSW, Australia). For the majority of subjects, MCAv and ScO2 were measured on the same side. All signals were recorded at 1000 Hz using commercially available data acquisition hardware and software (PowerLab and LabChart Pro, AD Instruments, Bella Vista, NSW, Australia). 2.1.3. Study design. Following instrumentation, 5 min of resting baseline data was collected.
OLBNP between 0 and −60 mmHg was applied for 20 cycles at 0.05 Hz (very low frequency, VLF), then 20 cycles at 0.1 Hz (low frequency, LF), with a 5 min rest period between each protocol. Subjects remained as relaxed as possible, and breathed spontaneously throughout the OLBNP profiles. Following completion of the OLBNP protocols, subjects remained in the supine posture for an additional 5 min recovery period. 787
C A Rickards et al
Physiol. Meas. 36 (2015) 785
2.1.4. Data analysis. All waveform data was exported into commercial analysis software (WinCPRS, Absolute Aliens, Turku, Finland). R waves generated from the ECG signal were detected and marked at their occurrence in time. Heart rate (HR) was derived from the R–R interval (RRI) signal. Diastolic arterial pressure (DAP) and systolic arterial pressure (SAP) were marked from the arterial blood pressure tracings. Mean arterial pressure (MAP) and mean MCAv were automatically calculated from the area under the arterial pressure and MCAv tracings via the WinCPRS software. Stroke volume (SV) was estimated on a beat-tobeat basis by the Finometer® using the established pulse contour method (Jansen et al 1990) and recorded directly to the LabChart file. Cardiac output (CO) was calculated as the product of HR and SV, total peripheral resistance (TPR) was calculated as MAP divided by CO, and cerebrovascular resistance (CVR) was calculated as MAP divided by mean MCAv. All variables were calculated from the 300 s baseline period, and from the 20-cycles of 0.05 Hz OLBNP (400 s) and 0.1 Hz OLBNP (200 s). Oscillatory patterns of MAP, mean MCAv, and ScO2 were determined with fast Fourier power spectral analysis. Data were made equidistant by interpolating linearly and re-sampling at 5 Hz. Data were then passed through a low-pass filter with a cutoff frequency of 0.5 Hz. Data sets were fast Fourier transformed with a Hanning window to obtain power spectra. Spectral power was expressed as the integrated area within the VLF range (0.04–0.07 Hz), and LF range (0.07–0.2 Hz). The coherence between MAP and mean MCAv, MAP and ScO2, and mean MCAv and ScO2 was calculated by dividing the squared cross-spectral densities of the two signals by the product of the individual autospectra (60 s windows). 2.1.5. Statistics. One-way repeated measures ANOVAs were used for comparison of all responses (baseline, 0.05 Hz OLBNP, 0.1 Hz OLBNP). For frequency domain variables, comparisons were made only within a frequency band (VLF, LF). Holm–Sidak post-hoc tests were used to compare responses to the OLBNP protocols versus baseline only. Paired t-tests were used to compare MAP-ScO2 coherence and mean MCAv-ScO2 coherence to MAP-mean MCAv coherence within a specific frequency band. Unless otherwise stated, all data are presented as mean ± SE, and exact p values are reported for comparisons. 2.2. Maximal LBNP to presyncope 2.2.1. Subjects. Twenty-one healthy, normotensive, non-smoking subjects (13 male, 8 female; age, 27 ± 1 years; height, 171 ± 2 cm; weight, 77 ± 3 kg; mean ± SE) were included in the analysis of this study. Twenty-five subjects completed this protocol, but four did not reach true pre-syncope according to the criteria described below. Six of the final 21 subjects also participated in the OLBNP study. The experiments were conducted in the Department of Integrative Physiology & Anatomy at the University of North Texas Health Science Center (UNTHSC), and all experimental protocols and procedures were reviewed and approved by the UNTHSC Institutional Review Board. A complete medical history and physical examination were conducted on each potential subject prior to their participation in the study. All inclusion and exclusion criteria were identical to the OLBNP study. After familiarization with the laboratory, subjects were briefed with a description of all procedures and risks associated with the experiments and each gave written informed consent to participate in the study. 2.2.2. Study design. Subject instrumentation was identical to the OLBNP study. The maximal LBNP protocol consisted of a 5 min baseline period followed by progressive 5 min stages of chamber decompression at −15, −30, −45, −60, −70, −80, −90, and −100 mmHg. The protocol was terminated following completion of 5 min at −100 mmHg LBNP, or with the onset 788
C A Rickards et al
Physiol. Meas. 36 (2015) 785
Table 1. Time domain cardiovascular responses to OLBNP at 0.05 Hz (VLF) and
0.1 Hz (LF) (N = 8).
Physiological parameter
Baseline
0.05 Hz OLBNP
0.1 Hz OLBNP
RRI (ms) HR (beats min−1) MAP (mmHg) SV (ml) CO (l min−1) TPR (mmHg l−1 min−1) Mean MCAv (cm s−1) CVR (mmHg cm−1 s−1) ScO2 (%) HbO2 (µM) dHb (µM) etCO2 (mmHg) Respiration rate (breaths min−1)
1004 ± 36 62.0 ± 2.6 91.9 ± 3.1 98.6 ± 4.3 6.1 ± 0.4 15.2 ± 0.5 69.2 ± 4.2 1.3 ± 0.1 66.9 ± 1.7 33.0 ± 3.0 16.1 ± 1.2 41.4 ± 1.1 12.3 ± 1.3
989 ± 36 61.8 ± 2.6 90.7 ± 2.6 99.0 ± 4.7 6.1 ± 0.3 15.0 ± 0.4 65.4 ± 4.3 1.4 ± 0.1a 66.1 ± 2.1 32.3 ± 3.3 16.1 ± 1.2 40.0 ± 1.3 13.4 ± 1.3
986 ± 40 62.1 ± 2.9 93.6 ± 2.9 99.0 ± 4.9 6.1 ± 0.3 15.5 ± 0.5 66.8 ± 4.7 1.4 ± 0.1a 66.2 ± 2.1 32.2 ± 3.2 16.0 ± 1.2 40.0 ± 1.3 11.9 ± 1.7
a
p ≤ 0.04 compared to baseline (one-way repeated measures ANOVA). OLBNP, oscillatory lower body negative pressure; RRI, R–R interval; HR, heart rate; MAP, mean arterial pressure; SV, stroke volume; CO, cardiac output; TPR, total peripheral resistance; MCAv, middle cerebral artery velocity; CVR, cerebrovascular resistance; ScO2, cerebral oxygen saturation; HbO2, oxy-hemoglobin; dHb, deoxy-hemoglobin; etCO2, end-tidal carbon dioxide.
of presyncope, defined as one or a combination of the following criteria: sudden bradycardia, SAP
