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Beqiri E, Placek MM, Chu KH, Donnelly J, Cucciolini G, Motroni V, Smith CA, Czosnyka M, Hutchinson P, Smielewski P. Exploration of uncertainty of PRx time trends. BRAIN & SPINE 2024; 4:102795. [PMID: 38601774 PMCID: PMC11004690 DOI: 10.1016/j.bas.2024.102795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 03/26/2024] [Accepted: 03/30/2024] [Indexed: 04/12/2024]
Abstract
Introduction PRx can be used as surrogate measure of Cerebral Autoregulation (CA) in traumatic brain injury (TBI) patients. PRx can provide means for individualising cerebral perfusion pressure (CPP) targets, such as CPPopt. However, a recent Delphi consensus of clinicians concluded that consensus could not be reached on the accuracy, reliability, and validation of any current CA assessment method. Research question We aimed to quantify the short-term uncertainty of PRx time-trends and to relate this to other physiological measurements. Material and methods Intracranial pressure (ICP), arterial blood pressure (ABP), end-tidal CO2 (EtCO2) high-resolution recordings of 911 TBI patients were processed with ICM + software. Hourly values of metrics that describe the variability within modalities derived from ABP, ICP and EtCO2, were calculated for the first 24h of neuromonitoring. Generalized additive models were used to describe the time trend of the variability in PRx. Linear correlations were studied for describing the relationship between PRx variability and the other physiological modalities. Results The time profile of variability of PRx decreases over the first 12h and was higher for average PRx ∼0. Increased variability of PRx was not linearly linked with average ABP, ICP, or CPP. For coherence between slow waves of ABP and ICP >0.7, the variability in PRx decreased (R = -0.47, p < 0.001). Discussion and conclusion PRx is a highly variable parameter. PRx short-term dispersion was not related to average ICP, ABP or CPP. The determinants of uncertainty of PRx should be investigated to improve reliability of individualised CA assessment in TBI patients.
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Affiliation(s)
- Erta Beqiri
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
| | - Michal M. Placek
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
- Neurosurgery Department, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Ka Hing Chu
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
| | - Joseph Donnelly
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
- Department of Medicine, University of Auckland, New Zealand
| | - Giada Cucciolini
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
- Neurosurgery Department, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Department of Medicine, University of Auckland, New Zealand
- Department of Surgical, Medical, Molecular Pathology and Critical Care Medicine, University of Pisa, Italy
| | - Virginia Motroni
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
| | - Claudia A. Smith
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
| | - Marek Czosnyka
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
| | - Peter Hutchinson
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
- Neurosurgery Department, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Peter Smielewski
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
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Agrawal S, Abecasis F, Jalloh I. Neuromonitoring in Children with Traumatic Brain Injury. Neurocrit Care 2024; 40:147-158. [PMID: 37386341 PMCID: PMC10861621 DOI: 10.1007/s12028-023-01779-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/05/2023] [Indexed: 07/01/2023]
Abstract
Traumatic brain injury remains a major cause of mortality and morbidity in children across the world. Current management based on international guidelines focuses on a fixed therapeutic target of less than 20 mm Hg for managing intracranial pressure and 40-50 mm Hg for cerebral perfusion pressure across the pediatric age group. To improve outcome from this complex disease, it is essential to understand the pathophysiological mechanisms responsible for disease evolution by using different monitoring tools. In this narrative review, we discuss the neuromonitoring tools available for use to help guide management of severe traumatic brain injury in children and some of the techniques that can in future help with individualizing treatment targets based on advanced cerebral physiology monitoring.
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Affiliation(s)
- Shruti Agrawal
- Department of Paediatric Intensive Care, Cambridge University Hospitals National Health Service Foundation Trust, Level 3, Box 7, Addenbrookes Hospital Hills Road, Cambridge, UK.
- University of Cambridge, Cambridge, UK.
| | - Francisco Abecasis
- Paediatric Intensive Care Unit, Centro Hospitalar Universitário Lisboa Norte, Lisbon, Portugal
| | - Ibrahim Jalloh
- University of Cambridge, Cambridge, UK
- Department of Neurosurgery, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, UK
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3
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Sarwal A, Robba C, Venegas C, Ziai W, Czosnyka M, Sharma D. Are We Ready for Clinical Therapy based on Cerebral Autoregulation? A Pro-con Debate. Neurocrit Care 2023; 39:269-283. [PMID: 37165296 DOI: 10.1007/s12028-023-01741-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 04/19/2023] [Indexed: 05/12/2023]
Abstract
Cerebral autoregulation (CA) is a physiological mechanism that maintains constant cerebral blood flow regardless of changes in cerebral perfusion pressure and prevents brain damage caused by hypoperfusion or hyperperfusion. In recent decades, researchers have investigated the range of systemic blood pressures and clinical management strategies over which cerebral vasculature modifies intracranial hemodynamics to maintain cerebral perfusion. However, proposed clinical interventions to optimize autoregulation status have not demonstrated clear clinical benefit. As future trials are designed, it is crucial to comprehend the underlying cause of our inability to produce robust clinical evidence supporting the concept of CA-targeted management. This article examines the technological advances in monitoring techniques and the accuracy of continuous assessment of autoregulation techniques used in intraoperative and intensive care settings today. It also examines how increasing knowledge of CA from recent clinical trials contributes to a greater understanding of secondary brain injury in many disease processes, despite the fact that the lack of robust evidence influencing outcomes has prevented the translation of CA-guided algorithms into clinical practice.
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Affiliation(s)
- Aarti Sarwal
- Atrium Wake Forest School of Medicine, Winston-Salem, NC, USA.
| | | | - Carla Venegas
- Mayo Clinic School of Medicine, Jacksonville, FL, USA
| | - Wendy Ziai
- Johns Hopkins University School of Medicine and Johns Hopkins Bayview Medical Center, Baltimore, MD, USA
| | - Marek Czosnyka
- Division of Neurosurgery, Cambridge University Hospital, Cambridge, UK
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Shim Y, Kim J, Kim HS, Oh J, Lee S, Ha EJ. Intracranial Pressure Monitoring for Acute Brain Injured Patients: When, How, What Should We Monitor. Korean J Neurotrauma 2023; 19:149-161. [PMID: 37431379 PMCID: PMC10329885 DOI: 10.13004/kjnt.2023.19.e32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/13/2023] [Accepted: 06/16/2023] [Indexed: 07/12/2023] Open
Abstract
While there is no level I recommendation for intracranial pressure (ICP) monitoring, it is typically indicated for patients with severe traumatic brain injury (TBI) with a Glasgow Coma Scale (GCS) score of 3-8 (class II). Even for moderate TBI patients with GCS 9-12, ICP monitoring should be considered for risk of increased ICP. The impact of ICP monitoring on patient outcomes is still not well-established, but recent studies reported a reduction of early mortality (class III) in TBI patients. There is no standard protocol for the application of ICP monitoring. In cases where cerebrospinal fluid drainage is required, an external ventricular drain is commonly used. In other cases, parenchymal ICP monitoring devices are generally employed. Subdural or non-invasive forms are not suitable for ICP monitoring. The mean value of ICP is the parameter recommended for observation in many guidelines. In TBI, values above 22 mmHg are associated with increased mortality. However, recent studies proposed various parameters including cumulative time with ICP above 20 mmHg (pressure-time dose), pressure reactivity index, ICP waveform characteristics (pulse amplitude of ICP, mean ICP wave amplitude), and the compensatory reserve of the brain (reserve-amplitude-pressure), which are useful in predicting patient outcomes and guiding treatment. Further research is required for validation of these parameters compared to simple ICP monitoring.
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Affiliation(s)
- Youngbo Shim
- Department of Critical Care Medicine, Kangbuk Samsung Hospital, Seoul, Korea
| | - Jungook Kim
- Gachon University Gil Hospital Regional Trauma Center, Gachon, Korea
| | - Hye Seon Kim
- Department of Neurosurgery, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University, Incheon, Korea
| | - Jiwoong Oh
- Departments of Neurological Surgery and Critical Care Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Seungioo Lee
- Departments of Neurological Surgery and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Eun Jin Ha
- Department of Critical Care Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
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Hassett CE, Uysal SP, Butler R, Moore NZ, Cardim D, Gomes JA. Assessment of Cerebral Autoregulation Using Invasive and Noninvasive Methods of Intracranial Pressure Monitoring. Neurocrit Care 2023; 38:591-599. [PMID: 36050535 DOI: 10.1007/s12028-022-01585-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 08/05/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND Pulse amplitude index (PAx), a descriptor of cerebrovascular reactivity, correlates the changes of the pulse amplitude of the intracranial pressure (ICP) waveform (AMP) with changes in mean arterial pressure (MAP). AMP relies on cerebrovascular compliance, which is modulated by the state of the cerebrovascular reactivity. PAx can aid in prognostication after acute brain injuries as a tool for the assessment of cerebral autoregulation and could potentially tailor individual management; however, invasive measurements are required for its calculation. Our aim was to evaluate the relationship between noninvasive PAx (nPAx) derived from a novel noninvasive device for ICP monitoring and PAx derived from gold standard invasive methods. METHODS We retrospectively analyzed invasive ICP (external ventricular drain) and non-invasive ICP (nICP), via mechanical extensometer (Brain4Care Corp.). Invasive and non-invasive ICP waveform morphology data was collected in adult patients with brain injury with arterial blood pressure monitoring. The time series from all signals were first treated to remove movement artifacts. PAx and nPAx were calculated as the moving correlation coefficients of 10-s averages of AMP or non-invasive AMP (nAMP) and MAP. AMP/nAMP was determined by calculating the fundamental frequency amplitude of the ICP/nICP signal over a 10-s window, updated every 10-s. We then evaluated the relationship between invasive PAx and noninvasive nPAx using the methods of repeated-measures analysis to generate an estimate of the correlation coefficient and its 95% confidence interval (CI). The agreement between the two methods was assessed using the Bland-Altman test. RESULTS Twenty-four patients were identified. The median age was 53.5 years (interquartile range 40-70), and intracranial hemorrhage (84%) was the most common etiology. Twenty-one (87.5%) patients underwent mechanical ventilation, and 60% were sedated with a median Glasgow Coma Scale score of 8 (7-15). Mean PAx was 0.0296 ± 0.331, and nPAx was 0.0171 ± 0.332. The correlation between PAx and nPAx was strong (R = 0.70, p < 0.0005, 95% CI 0.687-0.717). Bland-Altman analysis showed excellent agreement, with a bias of - 0.018 (95% CI - 0.026 to - 0.01) and a localized regression trend line that did not deviate from 0. CONCLUSIONS PAx can be calculated by conventional and noninvasive ICP monitoring in a statistically significant evaluation with strong agreement. Further study of the applications of this clinical tool is warranted, with the goal of early therapeutic intervention to improve neurologic outcomes following acute brain injuries.
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Affiliation(s)
- Catherine E Hassett
- Cerebrovascular Center, Neurologic Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44102, USA.
| | - S Pinar Uysal
- Department of Neurology, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Robert Butler
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - Nina Z Moore
- Cerebrovascular Center, Neurologic Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44102, USA
- Department of Neurosurgery, Cleveland Clinic, Cleveland, OH, USA
| | - Danilo Cardim
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital, Dallas, TX, USA
| | - Joao A Gomes
- Cerebrovascular Center, Neurologic Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44102, USA
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Tas J, Bos KDJ, Le Feber J, Beqiri E, Czosnyka M, Haeren R, van der Horst ICC, van Kuijk SMJ, Strauch U, Brady KM, Smielewski P, Aries MJH. Inducing oscillations in positive end-expiratory pressure improves assessment of cerebrovascular pressure reactivity in patients with traumatic brain injury. J Appl Physiol (1985) 2022; 133:585-592. [PMID: 35796613 PMCID: PMC9448337 DOI: 10.1152/japplphysiol.00199.2022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/05/2022] [Accepted: 07/05/2022] [Indexed: 11/22/2022] Open
Abstract
The cerebral pressure reactivity index (PRx), through intracranial pressure (ICP) measurements, informs clinicians about the cerebral autoregulation (CA) status in adult-sedated patients with traumatic brain injury (TBI). Using PRx in clinical practice is currently limited by variability over shorter monitoring periods. We applied an innovative method to reduce the PRx variability by ventilator-induced slow (1/min) positive end-expiratory pressure (PEEP) oscillations. We hypothesized that, as seen in a previous animal model, the PRx variability would be reduced by inducing slow arterial blood pressure (ABP) and ICP oscillations without other clinically relevant physiological changes. Patients with TBI were ventilated with a static PEEP for 30 min (PRx period) followed by a 30-min period of slow [1/min (0.0167 Hz)] +5 cmH2O PEEP oscillations (induced (iPRx period). Ten patients with TBI were included. No clinical monitoring was discontinued and no additional interventions were required during the iPRx period. The PRx variability [measured as the standard deviation (SD) of PRx] decreased significantly during the iPRx period from 0.25 (0.22-0.30) to 0.14 (0.09-0.17) (P = 0.006). There was a power increase around the induced frequency (1/min) for both ABP and ICP (P = 0.002). In conclusion, 1/min PEEP-induced oscillations reduced the PRx variability in patients with TBI with ICP levels <22 mmHg. No other clinically relevant physiological changes were observed. Reduced PRx variability might improve CA-guided perfusion management by reducing the time to find "optimal" perfusion pressure targets. Larger studies with prolonged periods of PEEP-induced oscillations are required to take it to routine use.NEW & NOTEWORTHY Cerebral autoregulation assessment requires sufficient slow arterial blood pressure (ABP) waves. However, spontaneous ABP waves may be insufficient for reliable cerebral autoregulation estimations. Therefore, we applied a ventilator "sigh-function" to generate positive end-expiratory pressure oscillations that induce slow ABP waves. This method demonstrated a reduced variability of the pressure reactivity index, commonly used as continuous cerebral autoregulation measure in a traumatic brain injury population.
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Affiliation(s)
- Jeanette Tas
- Department of Intensive Care Medicine, University Maastricht, Maastricht University Medical Center+, Maastricht, The Netherlands
- School for Mental Health and Neuroscience (MHeNS), University Maastricht, Maastricht, The Netherlands
| | - Kirsten D J Bos
- Department of Intensive Care Medicine, University Maastricht, Maastricht University Medical Center+, Maastricht, The Netherlands
- Department of Clinical Neurophysiology, University of Twente, Enschede, The Netherlands
| | - Joost Le Feber
- Department of Clinical Neurophysiology, University of Twente, Enschede, The Netherlands
| | - Erta Beqiri
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Marek Czosnyka
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Roel Haeren
- School for Mental Health and Neuroscience (MHeNS), University Maastricht, Maastricht, The Netherlands
- Department of Neurosurgery, University Maastricht, Maastricht University Medical Center+ Maastricht, Maastricht, The Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care Medicine, University Maastricht, Maastricht University Medical Center+, Maastricht, The Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, (KEMTA), Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Ulrich Strauch
- Department of Intensive Care Medicine, University Maastricht, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Ken M Brady
- Division of Cardiovascular Anesthesia, Department of Anesthesiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Peter Smielewski
- Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Marcel J H Aries
- Department of Intensive Care Medicine, University Maastricht, Maastricht University Medical Center+, Maastricht, The Netherlands
- School for Mental Health and Neuroscience (MHeNS), University Maastricht, Maastricht, The Netherlands
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7
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Ayaz H, Baker WB, Blaney G, Boas DA, Bortfeld H, Brady K, Brake J, Brigadoi S, Buckley EM, Carp SA, Cooper RJ, Cowdrick KR, Culver JP, Dan I, Dehghani H, Devor A, Durduran T, Eggebrecht AT, Emberson LL, Fang Q, Fantini S, Franceschini MA, Fischer JB, Gervain J, Hirsch J, Hong KS, Horstmeyer R, Kainerstorfer JM, Ko TS, Licht DJ, Liebert A, Luke R, Lynch JM, Mesquida J, Mesquita RC, Naseer N, Novi SL, Orihuela-Espina F, O’Sullivan TD, Peterka DS, Pifferi A, Pollonini L, Sassaroli A, Sato JR, Scholkmann F, Spinelli L, Srinivasan VJ, St. Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report. NEUROPHOTONICS 2022; 9:S24001. [PMID: 36052058 PMCID: PMC9424749 DOI: 10.1117/1.nph.9.s2.s24001] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.
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Affiliation(s)
- Hasan Ayaz
- Drexel University, School of Biomedical Engineering, Science, and Health Systems, Philadelphia, Pennsylvania, United States
- Drexel University, College of Arts and Sciences, Department of Psychological and Brain Sciences, Philadelphia, Pennsylvania, United States
| | - Wesley B. Baker
- Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Giles Blaney
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - David A. Boas
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Heather Bortfeld
- University of California, Merced, Departments of Psychological Sciences and Cognitive and Information Sciences, Merced, California, United States
| | - Kenneth Brady
- Lurie Children’s Hospital, Northwestern University Feinberg School of Medicine, Department of Anesthesiology, Chicago, Illinois, United States
| | - Joshua Brake
- Harvey Mudd College, Department of Engineering, Claremont, California, United States
| | - Sabrina Brigadoi
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
| | - Erin M. Buckley
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States
| | - Stefan A. Carp
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Robert J. Cooper
- University College London, Department of Medical Physics and Bioengineering, DOT-HUB, London, United Kingdom
| | - Kyle R. Cowdrick
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Joseph P. Culver
- Washington University School of Medicine, Department of Radiology, St. Louis, Missouri, United States
| | - Ippeita Dan
- Chuo University, Faculty of Science and Engineering, Tokyo, Japan
| | - Hamid Dehghani
- University of Birmingham, School of Computer Science, Birmingham, United Kingdom
| | - Anna Devor
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Turgut Durduran
- ICFO – The Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
- Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Spain
| | - Adam T. Eggebrecht
- Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Lauren L. Emberson
- University of British Columbia, Department of Psychology, Vancouver, British Columbia, Canada
| | - Qianqian Fang
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Sergio Fantini
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - Maria Angela Franceschini
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Jonas B. Fischer
- ICFO – The Institute of Photonic Sciences, The Barcelona Institute of Science and Technology, Castelldefels, Barcelona, Spain
| | - Judit Gervain
- University of Padua, Department of Developmental and Social Psychology, Padua, Italy
- Université Paris Cité, CNRS, Integrative Neuroscience and Cognition Center, Paris, France
| | - Joy Hirsch
- Yale School of Medicine, Department of Psychiatry, Neuroscience, and Comparative Medicine, New Haven, Connecticut, United States
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Keum-Shik Hong
- Pusan National University, School of Mechanical Engineering, Busan, Republic of Korea
- Qingdao University, School of Automation, Institute for Future, Qingdao, China
| | - Roarke Horstmeyer
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
- Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, United States
- Duke University, Department of Physics, Durham, North Carolina, United States
| | - Jana M. Kainerstorfer
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
- Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States
| | - Tiffany S. Ko
- Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, Pennsylvania, United States
| | - Daniel J. Licht
- Children’s Hospital of Philadelphia, Division of Neurology, Philadelphia, Pennsylvania, United States
| | - Adam Liebert
- Polish Academy of Sciences, Nalecz Institute of Biocybernetics and Biomedical Engineering, Warsaw, Poland
| | - Robert Luke
- Macquarie University, Department of Linguistics, Sydney, New South Wales, Australia
- Macquarie University Hearing, Australia Hearing Hub, Sydney, New South Wales, Australia
| | - Jennifer M. Lynch
- Children’s Hospital of Philadelphia, Division of Cardiothoracic Anesthesiology, Philadelphia, Pennsylvania, United States
| | - Jaume Mesquida
- Parc Taulí Hospital Universitari, Critical Care Department, Sabadell, Spain
| | - Rickson C. Mesquita
- University of Campinas, Institute of Physics, Campinas, São Paulo, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, São Paulo, Brazil
| | - Noman Naseer
- Air University, Department of Mechatronics and Biomedical Engineering, Islamabad, Pakistan
| | - Sergio L. Novi
- University of Campinas, Institute of Physics, Campinas, São Paulo, Brazil
- Western University, Department of Physiology and Pharmacology, London, Ontario, Canada
| | | | - Thomas D. O’Sullivan
- University of Notre Dame, Department of Electrical Engineering, Notre Dame, Indiana, United States
| | - Darcy S. Peterka
- Columbia University, Zuckerman Mind Brain Behaviour Institute, New York, United States
| | | | - Luca Pollonini
- University of Houston, Department of Engineering Technology, Houston, Texas, United States
| | - Angelo Sassaroli
- Tufts University, Department of Biomedical Engineering, Medford, Massachusetts, United States
| | - João Ricardo Sato
- Federal University of ABC, Center of Mathematics, Computing and Cognition, São Bernardo do Campo, São Paulo, Brazil
| | - Felix Scholkmann
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
- University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
| | - Lorenzo Spinelli
- National Research Council (CNR), IFN – Institute for Photonics and Nanotechnologies, Milan, Italy
| | - Vivek J. Srinivasan
- University of California Davis, Department of Biomedical Engineering, Davis, California, United States
- NYU Langone Health, Department of Ophthalmology, New York, New York, United States
- NYU Langone Health, Department of Radiology, New York, New York, United States
| | - Keith St. Lawrence
- Lawson Health Research Institute, Imaging Program, London, Ontario, Canada
- Western University, Department of Medical Biophysics, London, Ontario, Canada
| | - Ilias Tachtsidis
- University College London, Department of Medical Physics and Biomedical Engineering, London, United Kingdom
| | - Yunjie Tong
- Purdue University, Weldon School of Biomedical Engineering, West Lafayette, Indiana, United States
| | - Alessandro Torricelli
- Politecnico di Milano, Dipartimento di Fisica, Milan, Italy
- National Research Council (CNR), IFN – Institute for Photonics and Nanotechnologies, Milan, Italy
| | - Tara Urner
- Georgia Institute of Technology, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
| | - Heidrun Wabnitz
- Physikalisch-Technische Bundesanstalt (PTB), Berlin, Germany
| | - Martin Wolf
- University of Zurich, University Hospital Zurich, Department of Neonatology, Biomedical Optics Research Laboratory, Zürich, Switzerland
| | - Ursula Wolf
- University of Bern, Institute of Complementary and Integrative Medicine, Bern, Switzerland
| | - Shiqi Xu
- Duke University, Department of Biomedical Engineering, Durham, North Carolina, United States
| | - Changhuei Yang
- California Institute of Technology, Department of Electrical Engineering, Pasadena, California, United States
| | - Arjun G. Yodh
- University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania, United States
| | - Meryem A. Yücel
- Boston University Neurophotonics Center, Boston, Massachusetts, United States
- Boston University, College of Engineering, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Wenjun Zhou
- University of California Davis, Department of Biomedical Engineering, Davis, California, United States
- China Jiliang University, College of Optical and Electronic Technology, Hangzhou, Zhejiang, China
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Sainbhi AS, Froese L, Gomez A, Batson C, Stein KY, Alizadeh A, Zeiler FA. Continuous Time-Domain Cerebrovascular Reactivity Metrics and Discriminate Capacity for the Upper and Lower Limits of Autoregulation: A Scoping Review of the Animal Literature. Neurotrauma Rep 2021; 2:639-659. [PMID: 35018365 PMCID: PMC8742280 DOI: 10.1089/neur.2021.0043] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Over a wide range of systemic arterial pressures, cerebral blood flow (CBF) is regulated fairly constantly by the cerebral vessels in a process termed cerebral autoregulation (CA), which is depicted by the Lassen autoregulatory curve. After traumatic brain injury (TBI), CA can get impaired and these impairments manifest in changes of the Lassen autoregulatory curve. Continuous surrogate metrics of pressure-based CA, termed cerebrovascular reactivity (CVR) metrics, evaluate the relationship between slow vasogenic fluctuations in a driving pressure for cerebral blood flow, and the most commonly studied and utilized measures are based in the time domain and have been increasingly applied in bedside TBI care and have sparked the investigation of individualized cerebral perfusion pressure targets. However, not all CVR metrics have been validated as true measures of autoregulation in the pre-clinical setting. We reviewed all available pre-clinical animal literature that assessed the association between continuous time-domain metrics of CVR and some aspect of the Lassen autoregulatory curve. All 15 articles found associated the evaluated continuous metrics to the lower limit of autoregulation curve whereas none looked at the upper limit. Most of the evaluated metrics showed the ability to discriminate the lower limit of autoregulation with various methods of perturbation. Further work is required to evaluate the utility of such surrogate measures against the upper limit of autoregulation, while also providing validation to the existing literature supporting specific indices and their ability to discriminate the lower limit.
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Affiliation(s)
- Amanjyot Singh Sainbhi
- Biomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Logan Froese
- Biomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Alwyn Gomez
- Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Carleen Batson
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Kevin Y. Stein
- Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Arsalan Alizadeh
- Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Frederick A. Zeiler
- Biomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, Manitoba, Canada
- Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
- Centre on Aging, University of Manitoba, Winnipeg, Manitoba, Canada
- Division of Anaesthesia, Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom
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Liu X, Tekes A, Perin J, Chen MW, Soares BP, Massaro AN, Govindan RB, Parkinson C, Chavez-Valdez R, Northington FJ, Brady KM, Lee JK. Wavelet Autoregulation Monitoring Identifies Blood Pressures Associated With Brain Injury in Neonatal Hypoxic-Ischemic Encephalopathy. Front Neurol 2021; 12:662839. [PMID: 33995258 PMCID: PMC8113412 DOI: 10.3389/fneur.2021.662839] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/22/2021] [Indexed: 11/15/2022] Open
Abstract
Dysfunctional cerebrovascular autoregulation may contribute to neurologic injury in neonatal hypoxic-ischemic encephalopathy (HIE). Identifying the optimal mean arterial blood pressure (MAPopt) that best supports autoregulation could help identify hemodynamic goals that support neurologic recovery. In neonates who received therapeutic hypothermia for HIE, we hypothesized that the wavelet hemoglobin volume index (wHVx) would identify MAPopt and that blood pressures closer to MAPopt would be associated with less brain injury on MRI. We also tested a correlation-derived hemoglobin volume index (HVx) and single- and multi-window data processing methodology. Autoregulation was monitored in consecutive 3-h periods using near infrared spectroscopy in an observational study. The neonates had a mean MAP of 54 mmHg (standard deviation: 9) during hypothermia. Greater blood pressure above the MAPopt from single-window wHVx was associated with less injury in the paracentral gyri (p = 0.044; n = 63), basal ganglia (p = 0.015), thalamus (p = 0.013), and brainstem (p = 0.041) after adjustments for sex, vasopressor use, seizures, arterial carbon dioxide level, and a perinatal insult score. Blood pressure exceeding MAPopt from the multi-window, correlation HVx was associated with less injury in the brainstem (p = 0.021) but not in other brain regions. We conclude that applying wavelet methodology to short autoregulation monitoring periods may improve the identification of MAPopt values that are associated with brain injury. Having blood pressure above MAPopt with an upper MAP of ~50–60 mmHg may reduce the risk of brain injury during therapeutic hypothermia. Though a cause-and-effect relationship cannot be inferred, the data support the need for randomized studies of autoregulation and brain injury in neonates with HIE.
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Affiliation(s)
- Xiuyun Liu
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Aylin Tekes
- Department of Radiology, Johns Hopkins University, Baltimore, MD, United States
| | - Jamie Perin
- Department of Pediatrics, Center for Child and Community Health Research, Johns Hopkins University, Baltimore, MD, United States
| | - May W Chen
- Division of Neonatology, Johns Hopkins University, Baltimore, MD, United States
| | - Bruno P Soares
- Department of Radiology, University of Vermont, Burlington, VT, United States
| | - An N Massaro
- Fetal Medicine Institute, Children's National Health System, Washington, DC, United States.,The George Washington University School of Medicine, Washington, DC, United States.,Division of Neonatology, Children's National Health System, Washington, DC, United States
| | - Rathinaswamy B Govindan
- Fetal Medicine Institute, Children's National Health System, Washington, DC, United States.,The George Washington University School of Medicine, Washington, DC, United States
| | | | - Raul Chavez-Valdez
- Division of Neonatology, Johns Hopkins University, Baltimore, MD, United States
| | | | - Ken M Brady
- Department of Anesthesiology, Lurie Children's Hospital, Chicago, IL, United States
| | - Jennifer K Lee
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, United States
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10
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Neonatal NIRS monitoring: recommendations for data capture and review of analytics. J Perinatol 2021; 41:675-688. [PMID: 33589724 PMCID: PMC7883881 DOI: 10.1038/s41372-021-00946-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 11/20/2020] [Accepted: 01/19/2021] [Indexed: 01/29/2023]
Abstract
Brain injury is one of the most consequential problems facing neonates, with many preterm and term infants at risk for cerebral hypoxia and ischemia. To develop effective neuroprotective strategies, the mechanistic basis for brain injury must be understood. The fragile state of neonates presents unique research challenges; invasive measures of cerebral blood flow and oxygenation assessment exceed tolerable risk profiles. Near-infrared spectroscopy (NIRS) can safely and non-invasively estimate cerebral oxygenation, a correlate of cerebral perfusion, offering insight into brain injury-related mechanisms. Unfortunately, lack of standardization in device application, recording methods, and error/artifact correction have left the field fractured. In this article, we provide a framework for neonatal NIRS research. Our goal is to provide a rational basis for NIRS data capture and processing that may result in better comparability between studies. It is also intended to serve as a primer for new NIRS researchers and assist with investigation initiation.
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11
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Comparison of wavelet and correlation indices of cerebral autoregulation in a pediatric swine model of cardiac arrest. Sci Rep 2020; 10:5926. [PMID: 32245979 PMCID: PMC7125097 DOI: 10.1038/s41598-020-62435-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 03/13/2020] [Indexed: 02/07/2023] Open
Abstract
Existing cerebrovascular blood pressure autoregulation metrics have not been translated to clinical care for pediatric cardiac arrest, in part because signal noise causes high index time-variability. We tested whether a wavelet method that uses near-infrared spectroscopy (NIRS) or intracranial pressure (ICP) decreases index variability compared to that of commonly used correlation indices. We also compared whether the methods identify the optimal arterial blood pressure (ABPopt) and lower limit of autoregulation (LLA). 68 piglets were randomized to cardiac arrest or sham procedure with continuous monitoring of cerebral blood flow using laser Doppler, NIRS and ICP. The arterial blood pressure (ABP) was gradually reduced until it dropped to below the LLA. Several autoregulation indices were calculated using correlation and wavelet methods, including the pressure reactivity index (PRx and wPRx), cerebral oximetry index (COx and wCOx), and hemoglobin volume index (HVx and wHVx). Wavelet methodology had less index variability with smaller standard deviations. Both wavelet and correlation methods distinguished functional autoregulation (ABP above LLA) from dysfunctional autoregulation (ABP below the LLA). Both wavelet and correlation methods also identified ABPopt with high agreement. Thus, wavelet methodology using NIRS may offer an accurate vasoreactivity monitoring method with reduced signal noise after pediatric cardiac arrest.
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12
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Hoiland RL, Sekhon MS, Cardim D, Wood MD, Gooderham P, Foster D, Griesdale DE. Lack of agreement between optimal mean arterial pressure determination using pressure reactivity index versus cerebral oximetry index in hypoxic ischemic brain injury after cardiac arrest. Resuscitation 2020; 152:184-191. [PMID: 32229218 DOI: 10.1016/j.resuscitation.2020.03.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 03/13/2020] [Accepted: 03/21/2020] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Invasive monitoring of cerebral autoregulation using the pressure reactivity index (PRx) allows for the determination of optimal mean arterial pressure (MAPOPT) in hypoxic ischemic brain injury (HIBI) patients following cardiac arrest. However, the utility of non-invasive surrogates to determine MAPOPT has not been addressed. We aimed to determine the agreement between PRx-derived MAPOPT versus MAPOPT determined by the near-infrared spectroscopy (NIRS) based cerebral oximetry index (COx). METHODS Ten HIBI patients were enrolled. PRx-derived MAPOPT, lower (LLA) and upper limits of autoregulation (ULA) were compared against COx-derived MAPOPT, LLA and ULA. Multimodal neuromonitoring included mean arterial pressure, intracranial pressure, brain tissue oxygenation, jugular venous oxygen saturation, and NIRS-derived regional cerebral oxygen saturation. RESULTS Repeated measures Bland-Altman plots demonstrated limited agreement between MAPOPT derived from COx and PRx (mean bias: 1.4 mmHg; upper limit of agreement: 25.9 mmHg; lower limit of agreement: -23.0 mmHg). Similarly, there was limited agreement between the absolute values of PRx and COx. Mean bias was 0.26 and the upper and lower limits of agreement were 1.05 and -0.53, respectively. Systematic bias was apparent, whereby at low PRx values COx overestimated PRx and at high PRx values, COx underestimated PRx. COx was limited in its ability to determine impaired autoregulation defined by PRx (receiver operator characteristic area under the curve was 0.488). CONCLUSION Collectively, we demonstrate that COx-based determination of MAPOPT lacks agreement with MAPOPT derived from PRx. Further research must be done to evaluate the physiologic and clinical efficacy of PRx derived MAPOPT in HIBI.
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Affiliation(s)
- Ryan L Hoiland
- Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada; Centre for Heart, Lung, & Vascular Health, School of Health and Exercise Sciences, University of British Columbia-Okanagan, Kelowna, BC, Canada
| | - Mypinder S Sekhon
- Division of Critical Care Medicine, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Danilo Cardim
- Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Michael D Wood
- Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Peter Gooderham
- Division of Neurosurgery, Department of Surgery, Vancouver General Hospital, West 12th Avenue, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Denise Foster
- Division of Critical Care Medicine, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Donald E Griesdale
- Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada; Division of Critical Care Medicine, Department of Medicine, University of British Columbia, Vancouver, BC, Canada; Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, Vancouver, BC, Canada.
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13
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Liu X, Vitt JR, Hetts SW, Gudelunas K, Ho N, Ko N, Hu X. Morphological changes of intracranial pressure quantifies vasodilatory effect of verapamil to treat cerebral vasospasm. J Neurointerv Surg 2020; 12:802-808. [PMID: 31959633 DOI: 10.1136/neurintsurg-2019-015499] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 12/18/2019] [Accepted: 01/05/2020] [Indexed: 11/04/2022]
Abstract
INTRODUCTION After aneurysmal subarachnoid hemorrhage (SAH), both proximal and distal cerebral vasospasm can contribute to the development of delayed cerebral ischemia. Intra-arterial (IA) vasodilators are a mainstay of treatment for distal arterial vasospasm, but no methods of assessing the efficacy of interventions in real time have been established. OBJECTIVE To introduce a new method for continuous intraprocedural assessment of endovascular treatment for cerebral vasospasm. METHODS The premise of our approach was that distal cerebral arterial changes induce a consistent pattern in the morphological changes of intracranial pressure (ICP) pulse. This premise was demonstrated using a published algorithm in previous papers. In this study, we applied the algorithm to calculate the likelihood of cerebral vasodilation (VDI) and cerebral vasoconstriction (VCI) from intraprocedural ICP signals that are synchronized with injection of the IA vasodilator, verapamil. Cerebral blood flow velocities (CBFVs) on bilateral cerebral arteries were studied before and after IA therapy. RESULTS 192 recordings of patients with SAH were reviewed, and 27 recordings had high-quality ICP waveforms. The VCI was significantly lower after the first verapamil injection (0.47±0.017) than VCI at baseline (0.49±0.020, p<0.001). A larger dose of injected verapamil resulted in a larger and longer VDI increase. CBFV of the middle cerebral artery increases across the days before the injection of verapamil and decreases after IA therapy. CONCLUSION This study provides preliminary validation of an algorithm for continuous assessment of distal cerebral arterial changes in response to IA vasodilator infusion in patients with vasospasm and aneurysmal SAH.
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Affiliation(s)
- Xiuyun Liu
- School of Physiological Nursing, UCSF, San Francisco, California, USA
| | - Jeffrey R Vitt
- School of Physiological Nursing, UCSF, San Francisco, California, USA
| | - Steven W Hetts
- Department of Radiology, UCSF, San Francisco, California, USA
| | - Koa Gudelunas
- School of Physiological Nursing, UCSF, San Francisco, California, USA
| | - Nhi Ho
- School of Physiological Nursing, UCSF, San Francisco, California, USA
| | - Nerissa Ko
- School of Physiological Nursing, UCSF, San Francisco, California, USA
| | - Xiao Hu
- School of Physiological Nursing, UCSF, San Francisco, California, USA
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14
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Abstract
OBJECTIVE This study applied a new external ventricular catheter, which allows intracranial pressure (ICP) monitoring and cerebral spinal fluid (CSF) drainage simultaneously, to study cerebral vascular responses during acute CSF drainage. METHODS Six patients with 34 external ventricular drain (EVD) opening sessions were retrospectively analyzed. A published algorithm was used to extract morphological features of ICP recordings, and a template-matching algorithm was applied to calculate the likelihood of cerebral vasodilation index (VDI) and cerebral vasoconstriction index (VCI) based on the changes of ICP waveforms during CSF drainage. Power change (∆P) of ICP B-waves after EVD opening was also calculated. Cerebral autoregulation (CA) was assessed through phase difference between arterial blood pressure (ABP) and ICP using a previously published wavelet-based algorithm. RESULTS The result showed that acute CSF drainage reduced mean ICP (P = 0.016) increased VCI (P = 0.02) and reduced ICP B-wave power (P = 0.016) significantly. VCI reacted to ICP changes negatively when ICP was between 10 and 25 mmHg, and VCI remained unchanged when ICP was outside the 10-25 mmHg range. VCI negatively (r = - 0.44) and VDI positively (r = 0.82) correlated with ∆P of ICP B-waves, indicating that stronger vasoconstriction resulted in bigger power drop in ICP B-waves. Better CA prior to EVD opening triggered bigger drop in the power of ICP B-waves (r = - 0.612). CONCLUSIONS This study demonstrates that acute CSF drainage reduces mean ICP, and results in vasoconstriction which can be detected through an index, VCI. Cerebral vessels actively respond to ICP changes or cerebral perfusion pressure (CPP) changes in a certain range; beyond which, the vessels are insensitive to the changes in ICP and CPP.
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15
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Govindan RB, Brady KM, Massaro AN, Perin J, Jennings JM, DuPlessis AJ, Koehler RC, Lee JK. Comparison of Frequency- and Time-Domain Autoregulation and Vasoreactivity Indices in a Piglet Model of Hypoxia-Ischemia and Hypothermia. Dev Neurosci 2019; 40:1-13. [PMID: 31048593 PMCID: PMC6824917 DOI: 10.1159/000499425] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 03/06/2019] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION The optimal method to detect impairments in cerebrovascular pressure autoregulation in neonates with hypoxic-ischemic encephalopathy (HIE) is unclear. Improving autoregulation monitoring methods would significantly advance neonatal neurocritical care. METHODS We tested several mathematical algorithms from the frequency and time domains in a piglet model of HIE, hypothermia, and hypotension. We used laser Doppler flowmetry and induced hypotension to delineate the gold standard lower limit of autoregulation (LLA). Receiver operating characteristics curve analyses were used to determine which indices could distinguish blood pressure above the LLA from that below the LLA in each piglet. RESULTS Phase calculation in the frequency band with maximum coherence, as well as the correlation between mean arterial pressure (MAP) and near-infrared spectroscopy relative total tissue hemoglobin (HbT) or regional oxygen saturation (rSO2), accurately discriminated functional from dysfunctional autoregulation. Neither hypoxia-ischemia nor hypothermia affected the accuracy of these indices. Coherence alone and gain had low diagnostic value relative to phase and correlation. CONCLUSION Our findings indicate that phase shift is the most accurate component of autoregulation monitoring in the developing brain, and it can be measured using correlation or by calculating phase when coherence is maximal. Phase and correlation autoregulation indices from MAP and rSO2 and vasoreactivity indices from MAP and HbT are accurate metrics that are suitable for clinical HIE studies.
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Affiliation(s)
- Rathinaswamy B Govindan
- Fetal Medicine Institute, Children's National Health System, Washington, District of Columbia, USA
- The George Washington University School of Medicine, Washington, District of Columbia, USA
| | - Ken M Brady
- Department of Anesthesiology, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, USA
| | - An N Massaro
- Fetal Medicine Institute, Children's National Health System, Washington, District of Columbia, USA
- The George Washington University School of Medicine, Washington, District of Columbia, USA
- Neonatology, Children's National Health System, Washington, District of Columbia, USA
| | - Jamie Perin
- Center for Child and Community Health Research, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jacky M Jennings
- Center for Child and Community Health Research, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Adre J DuPlessis
- Fetal Medicine Institute, Children's National Health System, Washington, District of Columbia, USA
- The George Washington University School of Medicine, Washington, District of Columbia, USA
| | - Raymond C Koehler
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jennifer K Lee
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA,
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Liu X, Xiao R, Gadhoumi K, Tran N, Smielewski P, Czosnykan M, Hetts SW, Ko N, Hu X. Continuous monitoring of cerebrovascular reactivity through pulse transit time and intracranial pressure. Physiol Meas 2019; 40:01LT01. [PMID: 30577032 PMCID: PMC7197410 DOI: 10.1088/1361-6579/aafab1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Cerebrovascular reactivity (CR) is a mechanism that maintains stable blood flow supply to the brain. Pressure reactivity index (PRx), the correlation coefficient between slow waves of invasive arterial blood pressure (ABP) and intracranial pressure (ICP) has been validated for CR assessment. However, in clinical ward, not every subarachnoid hemorrhage (SAH) patient has invasive ABP monitoring. Pulse transit time (PTT), the propagation time of a pulse wave travelling from the heart to peripheral arteries, has been suggested as a surrogate measure of ABP. In this study, we proposed to use PTT instead of invasive ABP to monitor CR. APPROACH Forty-five SAH patients with simultaneous recordings of invasive ABP, ICP, oxygen saturation level (SpO2) and electrocardiograph (ECG) were included. PTT was calculated as the time from the ECG R-wave peak to the onset of SpO2. PTT based pressure reactivity index (tPRx) was calculated as the correlation coefficient between slow waves of PTT and ICP. Wavelet tPRx (wtRx) was calculated as the cosine of wavelet phase shift between PTT and ICP. Meanwhile, PRx and wPRx were also calculated using invasive ABP and ICP as input. MAIN RESULTS The result showed a negative relationship between PTT and ABP (r = -0.58, p < 0.001). tPRx negatively correlated with PRx (r = -0.51, p = 0.003). Wavelet method correlated well with correlation method demonstrated through positive relationship between wPRx and PRx (r = 0.82, p < 0.001) as well as wtPRx and tPRx (r = 0.84, p < 0.001). SIGNIFICANCE PTT demonstrates great potential as a useful tool for CR assessment when invasive ABP is unavailable. Key points • Pulse transit time (PTT), defined as the propagation time of a pulse wave travelling from the heart to the peripheral arteries, has been proposed as a surrogate measure of ABP. The relationship between PTT and ABP in SAH patients remains unknown. • Cerebrovascular reactivity (CR) assessment through PTT has advantages over invasive ABP, as it avoids bleeding and infection risk, and can be used outside of the ICU. • We introduced a new method to assess CR using PTT and ICP through correlation based method and wavelet based method. • We found that beat-to-beat PTT was negatively related with invasive ABP in SAH patients. A significant linear relationship exists between PTT-based CR parameter and a well validated method, PRx. PTT demonstrates great potential as a useful tool for CR assessment when invasive ABP is unavailable in SAH patients.
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Affiliation(s)
- Xiuyun Liu
- Department of Physiological Nursing, University of California, San Francisco, USA
| | - Ran Xiao
- Department of Physiological Nursing, University of California, San Francisco, USA
| | - Kais Gadhoumi
- Department of Physiological Nursing, University of California, San Francisco, USA
| | - Nate Tran
- Department of Physiological Nursing, University of California, San Francisco, USA
| | - Peter Smielewski
- Brain Physics Laboratory, Department of Clinical Neurosciences, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK
| | - Marek Czosnykan
- Brain Physics Laboratory, Department of Clinical Neurosciences, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK
| | - Steve W. Hetts
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Nerissa Ko
- Department of Neurology, University of California, San Francisco, USA
| | - Xiao Hu
- Department of Physiological Nursing, University of California, San Francisco, USA
- Department of Neurosurgery, School of Medicine, University of California, Los Angeles, USA
- Department of Neurological Surgery, University of California, San Francisco, USA
- Institute of Computational Health Sciences, University of California, San Francisco, USA
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Panerai RB, Haunton VJ, Minhas JS, Robinson TG. Inter-subject analysis of transfer function coherence in studies of dynamic cerebral autoregulation. Physiol Meas 2018; 39:125006. [DOI: 10.1088/1361-6579/aaf160] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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18
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Highton D. A bright forecast for autoregulation monitoring? J Physiol 2018; 596:2637-2638. [DOI: 10.1113/jp276290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- David Highton
- Princess Alexandra Hospital; University of Queensland; Brisbane Australia
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