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Intracranial Pressure

Overview of attention for book
Cover of 'Intracranial Pressure & Neuromonitoring XVI'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Cerebral Perfusion Pressure Variability Between Patients and Between Centres
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    Chapter 2 Pre-hospital Predictors of Impaired ICP Trends in Continuous Monitoring of Paediatric Traumatic Brain Injury Patients
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    Chapter 3 Prognosis of Severe Traumatic Brain Injury Outcomes in Children
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    Chapter 4 Do ICP-Derived Parameters Differ in Vegetative State from Other Outcome Groups After Traumatic Brain Injury?
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    Chapter 5 Cerebral Arterial Compliance in Traumatic Brain Injury
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    Chapter 6 The Cerebrovascular Resistance in Combined Traumatic Brain Injury with Intracranial Hematomas
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    Chapter 7 Computed Tomography Indicators of Deranged Intracranial Physiology in Paediatric Traumatic Brain Injury
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    Chapter 8 Mean Square Deviation of ICP in Prognosis of Severe TBI Outcomes in Children
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    Chapter 9 KidsBrainIT: A New Multi-centre, Multi-disciplinary, Multi-national Paediatric Brain Monitoring Collaboration
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    Chapter 10 Increased ICP and Its Cerebral Haemodynamic Sequelae
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    Chapter 11 What Determines Outcome in Patients That Suffer Raised Intracranial Pressure After Traumatic Brain Injury?
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    Chapter 12 Visualisation of the ‘Optimal Cerebral Perfusion’ Landscape in Severe Traumatic Brain Injury Patients
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    Chapter 13 Is There a Relationship Between Optimal Cerebral Perfusion Pressure-Guided Management and PaO2/FiO2 Ratio After Severe Traumatic Brain Injury?
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    Chapter 14 Cognitive Outcomes of Patients with Traumatic Bifrontal Contusions
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    Chapter 15 Non-invasive Intracranial Pressure Assessment in Brain Injured Patients Using Ultrasound-Based Methods
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    Chapter 16 Analysis of a Minimally Invasive Intracranial Pressure Signals During Infusion at the Subarachnoid Spinal Space of Pigs
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    Chapter 17 Comparison of Different Calibration Methods in a Non-invasive ICP Assessment Model
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    Chapter 18 An Embedded Device for Real-Time Noninvasive Intracranial Pressure Estimation
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    Chapter 19 Transcranial Bioimpedance Measurement as a Non-invasive Estimate of Intracranial Pressure
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    Chapter 20 Pulsed Electromagnetic Field (PEMF) Mitigates High Intracranial Pressure (ICP) Induced Microvascular Shunting (MVS) in Rats
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    Chapter 21 Volumetric Ophthalmic Ultrasound for Inflight Monitoring of Visual Impairment and Intracranial Pressure
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    Chapter 22 Does the Variability of Evoked Tympanic Membrane Displacement Data (V m) Increase as the Magnitude of the Pulse Amplitude Increases?
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    Chapter 23 Analysis of a Non-invasive Intracranial Pressure Monitoring Method in Patients with Traumatic Brain Injury
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    Chapter 24 A Wearable Transcranial Doppler Ultrasound Phased Array System
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    Chapter 25 Quantification of Macrocirculation and Microcirculation in Brain Using Ultrasound Perfusion Imaging
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    Chapter 26 HDF5-Based Data Format for Archiving Complex Neuro-monitoring Data in Traumatic Brain Injury Patients
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    Chapter 27 Are Slow Waves of Intracranial Pressure Suppressed by General Anaesthesia?
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    Chapter 28 Critical Closing Pressure During a Controlled Increase in Intracranial Pressure
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    Chapter 29 Effect of Mild Hypocapnia on Critical Closing Pressure and Other Mechanoelastic Parameters of the Cerebrospinal System
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    Chapter 30 Occurrence of CPPopt Values in Uncorrelated ICP and ABP Time Series
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    Chapter 31 Simultaneous Transients of Intracranial Pressure and Heart Rate in Traumatic Brain Injury: Methods of Analysis
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    Chapter 32 Increasing the Contrast-to-Noise Ratio of MRI Signals for Regional Assessment of Dynamic Cerebral Autoregulation
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    Chapter 33 Comparing Models of Spontaneous Variations, Maneuvers and Indexes to Assess Dynamic Cerebral Autoregulation
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    Chapter 34 ICP and Antihypertensive Drugs
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    Chapter 35 ICP: From Correlation to Causation
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    Chapter 36 A Waveform Archiving System for the GE Solar 8000i Bedside Monitor
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    Chapter 37 Deriving the PRx and CPPopt from 0.2-Hz Data: Establishing Generalizability to Bedmaster Users
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    Chapter 38 Medical Waveform Format Encoding Rules Representation of Neurointensive Care Waveform Data
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    Chapter 39 Multi-Scale Peak and Trough Detection Optimised for Periodic and Quasi-Periodic Neuroscience Data
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    Chapter 40 Room Air Readings of Brain Tissue Oxygenation Probes
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    Chapter 41 What Do We Mean by Cerebral Perfusion Pressure?
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    Chapter 42 Investigation of the Relationship Between the Burden of Raised ICP and the Length of Stay in a Neuro-Intensive Care Unit
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    Chapter 43 Pressure Reactivity-Based Optimal Cerebral Perfusion Pressure in a Traumatic Brain Injury Cohort
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    Chapter 44 Spaceflight-Induced Visual Impairment and Globe Deformations in Astronauts Are Linked to Orbital Cerebrospinal Fluid Volume Increase
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    Chapter 45 Ventriculomegaly in the Elderly: Who Needs a Shunt? A MRI Study on 90 Patients
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    Chapter 46 Is There a Link Between ICP-Derived Infusion Test Parameters and Outcome After Shunting in Normal Pressure Hydrocephalus?
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    Chapter 47 Mathematical Modelling of CSF Pulsatile Flow in Aqueduct Cerebri
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    Chapter 48 Cerebrospinal Fluid and Cerebral Blood Flows in Idiopathic Intracranial Hypertension
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    Chapter 49 Significant Association of Slow Vasogenic ICP Waves with Normal Pressure Hydrocephalus Diagnosis
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    Chapter 50 ICP Monitoring and Phase-Contrast MRI to Investigate Intracranial Compliance
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    Chapter 51 Numerical Cerebrospinal System Modeling in Fluid-Structure Interaction
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    Chapter 52 Differential Systolic and Diastolic Regulation of the Cerebral Pressure-Flow Relationship During Squat-Stand Manoeuvres
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    Chapter 53 Normative Ranges of Transcranial Doppler Metrics
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    Chapter 54 Autoregulating Cerebral Tissue Selfishly Exploits Collateral Flow Routes Through the Circle of Willis
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    Chapter 55 ICP Monitoring by Open Extraventricular Drainage: Common Practice but Not Suitable for Advanced Neuromonitoring and Prone to False Negativity
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    Chapter 56 Comparison of Intracranial Pressure and Pressure Reactivity Index Obtained Through Pressure Measurements in the Ventricle and in the Parenchyma During and Outside Cerebrospinal Fluid Drainage Episodes in a Manipulation-Free Patient Setting
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    Chapter 57 Visualizing Cerebrovascular Autoregulation Insults and Their Association with Outcome in Adult and Paediatric Traumatic Brain Injury
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    Chapter 58 Assessing Cerebral Hemodynamic Stability After Brain Injury
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    Chapter 59 Systolic and Diastolic Regulation of the Cerebral Pressure-Flow Relationship Differentially Affected by Acute Sport-Related Concussion
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    Chapter 60 Induced Dynamic Intracranial Pressure and Cerebrovascular Reactivity Assessment of Cerebrovascular Autoregulation After Traumatic Brain Injury with High Intracranial Pressure in Rats
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    Chapter 61 Prediction of the Time to Syncope Occurrence in Patients Diagnosed with Vasovagal Syncope
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    Chapter 62 Statistical Signal Properties of the Pressure-Reactivity Index (PRx)
Attention for Chapter 58: Assessing Cerebral Hemodynamic Stability After Brain Injury
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Chapter title
Assessing Cerebral Hemodynamic Stability After Brain Injury
Chapter number 58
Book title
Intracranial Pressure & Neuromonitoring XVI
Published in
Acta neurochirurgica Supplement, January 2018
DOI 10.1007/978-3-319-65798-1_58
Pubmed ID
Book ISBNs
978-3-31-965797-4, 978-3-31-965798-1
Authors

Bianca Pineda, Colin Kosinski, Nam Kim, Shabbar Danish, William Craelius

Abstract

 Following brain injury, unstable cerebral hemodynamics can be characterized by abnormal rises in intracranial pressure (ICP). This behavior has been quantified by the RAP index: the correlation (R) between ICP pulse amplitude (A) and mean (P). While RAP could be a valuable indicator of autoregulatory processes, its prognostic ability is not well established and its validity has been questioned due to potential errors in measurement. Here, we test (1) whether RAP is a consistent measure of intracranial hemodynamics and (2) whether RAP has prognostic value in predicting hemodynamic instability following brain injury.  RAP was tested in seven brain injured patients treated in a surgical intensive care unit. A sample of ICP data was randomly chosen and segmented into 1 hour periods. Hours were then categorized as either stable, which contained no sharp rises in ICP, or unstable, which contained ≥1 sharp rise-where a sharp rise is defined as ICP exceeding a mean slope of 0.15 mmHg/s. Equal numbers of stable and unstable segments were then selected for each patient. RAP was calculated as the Pearson's correlation coefficient between ICP pulse amplitude (AMP) and mean (mICP), determined in 6 second windows, according to established methods.  Results showed that (1) average AMP and ICP levels were similar between stable and unstable periods and (2) unstable periods were identified by RAP values exceeding 0.6 with an average positive predictive value of 74%.  We conclude that RAP can provide a valid measure of ICP dynamics, is not affected by sensor drift, and can better distinguish periods of instability than ICP or AMP alone.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 15%
Student > Bachelor 1 8%
Other 1 8%
Student > Master 1 8%
Student > Ph. D. Student 1 8%
Other 0 0%
Unknown 7 54%
Readers by discipline Count As %
Medicine and Dentistry 2 15%
Neuroscience 2 15%
Psychology 1 8%
Biochemistry, Genetics and Molecular Biology 1 8%
Engineering 1 8%
Other 0 0%
Unknown 6 46%