Non-invasive Monitoring of Intracranial Pressure Using Transcranial Doppler Ultrasonography: Is It Possible?

Although intracranial pressure (ICP) is essential to guide management of patients suffering from acute brain diseases, this signal is often neglected outside the neurocritical care environment. This is mainly attributed to the intrinsic risks of the available invasive techniques, which have prevented ICP monitoring in many conditions affecting the intracranial homeostasis, from mild traumatic brain injury to liver encephalopathy. In such scenario, methods for non-invasive monitoring of ICP (nICP) could improve clinical management of these conditions. A review of the literature was performed on PUBMED using the search keywords 'Transcranial Doppler non-invasive intracranial pressure.' Transcranial Doppler (TCD) is a technique primarily aimed at assessing the cerebrovascular dynamics through the cerebral blood flow velocity (FV). Its applicability for nICP assessment emerged from observation that some TCD-derived parameters change during increase of ICP, such as the shape of FV pulse waveform or pulsatility index. Methods were grouped as: based on TCD pulsatility index; aimed at non-invasive estimation of cerebral perfusion pressure and model-based methods. Published studies present with different accuracies, with prediction abilities (AUCs) for detection of ICP ≥20 mmHg ranging from 0.62 to 0.92. This discrepancy could result from inconsistent assessment measures and application in different conditions, from traumatic brain injury to hydrocephalus and stroke. Most of the reports stress a potential advantage of TCD as it provides the possibility to monitor changes of ICP in time. Overall accuracy for TCD-based methods ranges around ±12 mmHg, with a great potential of tracing dynamical changes of ICP in time, particularly those of vasogenic nature.Cambridge Commonwealth, European & International Trust Scholarship (University of Cambridge) provided financial support in the form of Scholarship funding for DC. Woolf Fisher Trust provided financial support in the form of Scholarship funding for JD. Gates Cambridge Trust provided financial support in the form of Scholarship funding for XL. CNPQ provided financial support in the form of Scholarship funding for BCTC (Research Project 203792/2014-9). NIHR Brain Injury Healthcare Technology Co-operative, Cambridge, UK provided financial support in the form of equipment funding for DC, BC and MC. The sponsors had no role in the design or conduct of this manuscript.This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s12028-016-0258-

NON-INVASIVE MONITORING OF INTRACRANIAL PRESSURE USING TRANSCRANIAL DOPPLER ULTRASONOGRAPHY

Intracranial pressure (ICP) is an important monitoring modality in the clinical management of several neurological diseases carrying the risk of fatal intracranial hypertension. However, this parameter is not always considered due to its invasive assessment. In this scenario, a non-invasive estimation of ICP (nICP) may be essential, and indeed it has become a Holy Grail in Clinical Neurosciences: extensively searched, albeit never found. This thesis is devoted to the assessment, applications and development of transcranial Doppler (TCD)-based non-invasive methods for ICP and cerebral perfusion pressure (CPP) monitoring. The thesis is divided into three sections: I) The accuracy of existing TCD-based nICP estimators in various scenarios of varying ICP (traumatic brain injury, rise of ICP during plateau waves, and rise in ICP induced by infusion of cerebrospinal fluid during infusion test). The estimators of nICP consisted of a mathematical black box model, methods based on non-invasive CPP, and a method based on TCD pulsatility index. II) The feasibility of the best performing nICP estimator in clinical practice, including patients with closed TBI and brain midline shift, patients with acute liver failure during liver transplant surgery, and patients during non-neurosurgical surgery in the beach chair position. III) The description and assessment of a novel methodology for non-invasive assessment of cerebral perfusion pressure (nCPP) based on spectral arterial blood volume accounting. As main results, TCD-based non-invasive methods could replicate changes in direct ICP across time confidently, and could provide reasonable accuracy in comparison to the standard invasive techniques. Furthermore, in feasibility studies, nICP in association with other TCD physiological parameters provided a comprehensive interpretation of cerebral haemodynamics in conditions presenting impairment of cerebral blood flow circulation. The new method of nCPP estimation could identify changes in CPP across time reliably in conditions of decreasing and increasing CPP. These findings support the use of TCD-based nICP methods in a variety of clinical conditions requiring management of ICP and brain perfusion. More importantly, the low costs associated with nICP methods, since TCD is a widely available medical device, could contribute to its widespread use as a reliable alternative for ICP monitoring in everyday clinical practice.Cambridge Commonwealth European and Internation Trus

Enhancing health and public health through machine learning: decision support for smarter choices

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Comparison of existing aneurysm models and their path forward

The two most important aneurysm types are cerebral aneurysms (CA) and abdominal aortic aneurysms (AAA), accounting together for over 80\% of all fatal aneurysm incidences. To minimise aneurysm related deaths, clinicians require various tools to accurately estimate its rupture risk. For both aneurysm types, the current state-of-the-art tools to evaluate rupture risk are identified and evaluated in terms of clinical applicability. We perform a comprehensive literature review, using the Web of Science database. Identified records (3127) are clustered by modelling approach and aneurysm location in a meta-analysis to quantify scientific relevance and to extract modelling patterns and further assessed according to PRISMA guidelines (179 full text screens). Beside general differences and similarities of CA and AAA, we identify and systematically evaluate four major modelling approaches on aneurysm rupture risk: finite element analysis and computational fluid dynamics as deterministic approaches and machine learning and assessment-tools and dimensionless parameters as stochastic approaches. The latter score highest in the evaluation for their potential as clinical applications for rupture prediction, due to readiness level and user friendliness. Deterministic approaches are less likely to be applied in a clinical environment because of their high model complexity. Because deterministic approaches consider underlying mechanism for aneurysm rupture, they have improved capability to account for unusual patient-specific characteristics, compared to stochastic approaches. We show that an increased interdisciplinary exchange between specialists can boost comprehension of this disease to design tools for a clinical environment. By combining deterministic and stochastic models, advantages of both approaches can improve accessibility for clinicians and prediction quality for rupture risk.Comment: 46 pages, 5 figure

The Recurrence-Based Analysis of Intracranial Pressure

Modern computational approaches tied together with the power of mathematical science has pushed us closer to reach a deeper understanding of complex dynamical systems. Real-world biological and physiological systems now can be studied on account of the accessibility to fast, cheap and powerful computers. In particular, the field of neuroscience and brain data analysis has grown significantly in the recent years. Recurrence plots (RPs) are a relatively new approach for the analysis of nonlinear, non-stationary and noisy data. Rooted in topological properties of the system, RP visualizes the recurrence states of the dynamical system. Armed with the recurrence quantification measures, RP is even more rigorous in exploring and quantifying real-world dynamical system. In the present work, we benefit from the RP and RQA methods to study the behavior of intracranial pressure (ICP) waveforms. ICP is defined as the fluid pressure inside the skull which carries important information associated with the status of the patient. Our main goal is to detect sudden changes or extreme regime changing in these signals. Patterns appearing in RP can shed light on fundamental characteristics of the system. Our results suggest distinguishable patterns in the RPs of some subjects which are not detectable in the raw ICP signals. This work sets up the workflow for using RP analysis in online ICP monitoring of brain-injured patients

TRAUMATIC BRAIN INJURY ASSESSMENT USING THE INTEGRATION OF PATTERN RECOGNITION METHODS AND FINITE ELEMENT ANALYSIS

The overall goal of this research is to develop methods and algorithms to investigate the severity of Traumatic brain injury (TBI) and to estimate the intracranial pressure (ICP) level non-invasively. Brain x-ray computed tomography (CT) images and artificial intelligence methods are employed to estimate the level of ICP. Fully anisotropic complex wavelet transform features are proposed to extract directional textural features from brain images. Different feature selection and classification methods are tested to find the optimal feature vector and estimate the ICP using support vector regression. By using systematic feature extraction, selection and classification, promising results on ICP estimation are achieved. The results also indicate the reliability of the proposed algorithm. In the following, case-based finite element (FE) models are extracted from CT images using Matlab, Solidworks, and Ansys software tools. The ICP estimation obtained from image analysis is used as an input to the FE modeling to obtain stress/strain distribution over the tissue. Three in-plane modeling approaches are proposed to investigate the effect of ICP elevation on brain tissue stress/strain distribution. Moreover, the effect of intracranial bleeding on ICP elevation is studied in 2-D modeling. A mathematical relationship between the intracranial pressure and the maximum strain/stress over the brain tissue is obtained using linear regression method. In the following, a 3-D model is constructed using 3 slices of brain CT images. The effect of increased ICP on the tissue deformation is studied. The results show the proposed framework can accurately simulate the injury and provides an accurate ICP estimation non-invasively. The results from this study may be used as a base for developing a non-invasive procedure for evaluating ICP using FE methods