Modelling midline shift and ventricle collapse in cerebral oedema following acute ischaemic stroke.

In ischaemic stroke, a large reduction in blood supply can lead to the breakdown of the blood-brain barrier and to cerebral oedema after reperfusion therapy. The resulting fluid accumulation in the brain may contribute to a significant rise in intracranial pressure (ICP) and tissue deformation. Changes in the level of ICP are essential for clinical decision-making and therapeutic strategies. However, the measurement of ICP is constrained by clinical techniques and obtaining the exact values of the ICP has proven challenging. In this study, we propose the first computational model for the simulation of cerebral oedema following acute ischaemic stroke for the investigation of ICP and midline shift (MLS) relationship. The model consists of three components for the simulation of healthy blood flow, occluded blood flow and oedema, respectively. The healthy and occluded blood flow components are utilized to obtain oedema core geometry and then imported into the oedema model for the simulation of oedema growth. The simulation results of the model are compared with clinical data from 97 traumatic brain injury patients for the validation of major model parameters. Midline shift has been widely used for the diagnosis, clinical decision-making, and prognosis of oedema patients. Therefore, we focus on quantifying the relationship between ICP and midline shift (MLS) and identify the factors that can affect the ICP-MLS relationship. Three major factors are investigated, including the brain geometry, blood-brain barrier damage severity and the types of oedema (including rare types of oedema). Meanwhile, the two major types (stress and tension/compression) of mechanical brain damage are also presented and the differences in the stress, tension, and compression between the intraparenchymal and periventricular regions are discussed. This work helps to predict ICP precisely and therefore provides improved clinical guidance for the treatment of brain oedema.

Modelling Drug Delivery to the Small Airways: Optimization Using Response Surface Methodology.

AIM: The aim of this in silico study was to investigate the effect of particle size, flow rate, and tidal volume on drug targeting to small airways in patients with mild COPD. METHOD: Design of Experiments (DoE) was used with an in silico whole lung particle deposition model for bolus administration to investigate whether controlling inhalation can improve drug delivery to the small conducting airways. The range of particle aerodynamic diameters studied was 0.4 - 10 µm for flow rates between 100 - 2000 mL/s (i.e., low to very high), and tidal volumes between 40 - 1500 mL. RESULTS: The model accurately predicted the relationship between independent variables and lung deposition, as confirmed by comparison with published experimental data. It was found that large particles (~ 5 µm) require very low flow rate (~ 100 mL/s) and very small tidal volume (~ 110 mL) to target small conducting airways, whereas fine particles (~ 2 µm) achieve drug targeting in the region at a relatively higher flow rate (~ 500 mL/s) and similar tidal volume (~ 110 mL). CONCLUSION: The simulation results indicated that controlling tidal volume and flow rate can achieve targeted delivery to the small airways (i.e., > 50% of emitted dose was predicted to deposit in the small airways), and the optimal parameters depend on the particle size. It is hoped that this finding could provide a means of improving drug targeting to the small conducting airways and improve prognosis in COPD management.

Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet).

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA.

Dynamic cerebral autoregulation is governed by two time constants: Arterial transit time and feedback time constant.

Dynamic cerebral autoregulation (dCA) is the mechanism that describes how the brain maintains cerebral blood flow approximately constant in response to short-term changes in arterial blood pressure. This is known to be impaired in many different pathological conditions, including ischaemic and haemorrhagic stroke, dementia and traumatic brain injury. Many different approaches have thus been used both to analyse and to quantify this mechanism in a range of healthy and diseased subjects, including data-driven models (in both the time and the frequency domain) and biophysical models. However, despite the substantial body of work on both biophysical models and data-driven models of dCA, there remains little work that links the two together. One of the reasons for this is proposed to be the discrepancies between the time constants that govern dCA in models and in experimental data. In this study, the processes that govern dCA are examined and it is proposed that the application of biophysical models remains limited due to a lack of understanding about the physical processes that are being modelled, partly due to the specific model formulation that has been most widely used (the equivalent electrical circuit). Based on the analysis presented here, it is proposed that the two most important time constants are arterial transit time and feedback time constant. It is therefore time to revisit equivalent electrical circuit models of dCA and to develop a more physiologically realistic alternative, one that can more easily be related to experimental data. KEY POINTS: Dynamic cerebral autoregulation is governed by two time constants. The first time constant is the arterial transit time, rather than the traditional 'RC' time constant widely used in previous models. This arterial transit time is approximately 1 s in the brain. The second time constant is the feedback time constant, which is less accurately known, although it is somewhat larger than the arterial transit time. The equivalent electrical circuit model of dynamic cerebral autoregulation should be replaced with a more physiologically representative model.

Poroelastic modelling of brain tissue swelling and decompressive craniectomy treatment in ischaemic stroke.

Brain oedema or tissue swelling that develops after ischaemic stroke can cause detrimental effects, including brain herniation and increased intracranial pressure (ICP). These effects can be reduced by performing a decompressive craniectomy (DC) operation, in which a portion of the skull is removed to allow swollen brain tissue to expand outside the skull. In this study, a poroelastic model is used to investigate the effect of brain ischaemic infarct size and location on the severity of brain tissue swelling. Furthermore, the model will also be used to evaluate the effectiveness of DC surgery as a treatment for brain tissue swelling after ischaemia. The poroelastic model consists of two equations: one describing the elasticity of the brain tissue and the other describing the changes in the interstitial tissue pressure. The model is applied on an idealized brain geometry, and it is found that infarcts with radius larger than approximately 14 mm and located near the lateral ventricle produce worse brain midline shift, measured through lateral ventricle compression. Furthermore, the model is also able to show the positive effect of DC treatment in reducing the brain midline shift by allowing part of the brain tissue to expand through the skull opening. However, the model does not show a decrease in the interstitial pressure during DC treatment. Further improvement and validation could enhance the capability of the proposed poroelastic model in predicting the occurrence of brain tissue swelling and DC treatment post ischaemia.

Individual Patient Data Meta-Analysis of Dynamic Cerebral Autoregulation and Functional Outcome After Ischemic Stroke.

BACKGROUND: The relationship between dynamic cerebral autoregulation (dCA) and functional outcome after acute ischemic stroke (AIS) is unclear. Previous studies are limited by small sample sizes and heterogeneity. METHODS: We performed a 1-stage individual patient data meta-analysis to investigate associations between dCA and functional outcome after AIS. Participating centers were identified through a systematic search of the literature and direct invitation. We included centers with dCA data within 1 year of AIS in adults aged over 18 years, excluding intracerebral or subarachnoid hemorrhage. Data were obtained on phase, gain, coherence, and autoregulation index derived from transfer function analysis at low-frequency and very low-frequency bands. Cerebral blood velocity, arterial pressure, end-tidal carbon dioxide, heart rate, stroke severity and sub-type, and comorbidities were collected where available. Data were grouped into 4 time points after AIS: <24 hours, 24 to 72 hours, 4 to 7 days, and >3 months. The modified Rankin Scale assessed functional outcome at 3 months. Modified Rankin Scale was analyzed as both dichotomized (0 to 2 versus 3 to 6) and ordinal (modified Rankin Scale scores, 0-6) outcomes. Univariable and multivariable analyses were conducted to identify significant relationships between dCA parameters, comorbidities, and outcomes, for each time point using generalized linear (dichotomized outcome), or cumulative link (ordinal outcome) mixed models. The participating center was modeled as a random intercept to generate odds ratios with 95% CIs. RESULTS: The sample included 384 individuals (35% women) from 7 centers, aged 66.3±13.7 years, with predominantly nonlacunar stroke (n=348, 69%). In the affected hemisphere, higher phase at very low-frequency predicted better outcome (dichotomized modified Rankin Scale) at <24 (crude odds ratios, 2.17 [95% CI, 1.47-3.19]; P<0.001) hours, 24-72 (crude odds ratios, 1.95 [95% CI, 1.21-3.13]; P=0.006) hours, and phase at low-frequency predicted outcome at 3 (crude odds ratios, 3.03 [95% CI, 1.10-8.33]; P=0.032) months. These results remained after covariate adjustment. CONCLUSIONS: Greater transfer function analysis-derived phase was associated with improved functional outcome at 3 months after AIS. dCA parameters in the early phase of AIS may help to predict functional outcome.

Quantifying heterogeneity in an animal model of acute respiratory distress syndrome, a comparison of inspired sinewave technique to computed tomography.

The inspired sinewave technique (IST) is a non-invasive method to measure lung heterogeneity indices (including both uneven ventilation and perfusion or heterogeneity), which reveal multiple conditions of the lung and lung injury. To evaluate the reproducibility and predicted clinical outcomes of IST heterogeneity values, a comparison with a quantitative lung computed tomography (CT) scan is performed. Six anaesthetised pigs were studied after surfactant depletion by saline-lavage. Paired measurements of lung heterogeneity were then taken with both the IST and CT. Lung heterogeneity measured by the IST was calculated by (a) the ratio of tracer gas outputs measured at oscillation periods of 180 s and 60 s, and (b) by the standard deviation of the modelled log-normal distribution of ventilations and perfusions in the simulation lung. In the CT images, lungs were manually segmented and divided into different regions according to voxel density. A quantitative CT method to calculate the heterogeneity (the Cressoni method) was applied. The IST and CT show good Pearson correlation coefficients in lung heterogeneity measurements (ventilation: 0.71, and perfusion, 0.60, p < 0.001). Within individual animals, the coefficients of determination average ventilation (R2 = 0.53) and perfusion (R2 = 0.68) heterogeneity. Strong concordance rates of 98% in ventilation and 89% when the heterogeneity changes were reported in pairs measured by CT scanning and IST methods. This quantitative method to identify heterogeneity has the potential to replicate CT lung heterogeneity, and to aid individualised care in ARDS.

Neurological intervention transition model for dynamic prediction of good outcome in spontaneous subarachnoid haemorrhage.

Deterioration of neurovascular conditions can be rapid in patients with spontaneous subarachnoid haemorrhage (SAH) and often lead to poor clinical outcomes. Therefore, it is crucial to promptly assess and continually track the progression of the disease. This study incorporated baseline clinical conditions, repeatedly measured neurological grades and haematological biomarkers for dynamic outcome prediction in patients with spontaneous SAH. Neurological intervention, mainly aneurysm clipping and endovascular embolisation, was also incorporated as an intermediate event in developing a neurological intervention transition (NIT) joint model. A retrospective cohort study was performed on 701 patients in spontaneous SAH with a study period of 14 days from the MIMIC-IV dataset. A dynamic prognostic model predicting outcome of patients was developed based on combination of Cox model and piecewise linear mixed-effect models to incorporate different types of prognostic information. Clinical baseline covariates, including cerebral oedema, cerebral infarction, respiratory failure, hydrocephalus and vasospasm, as well as repeated measured Glasgow Coma Scale (GCS), glucose and white blood cell (WBC) levels were covariates contributing to the optimal model. Incorporation of neurological intervention as an intermediate event increases the prediction performance compared with baseline joint modelling approach. The average AUC of the optimal model proposed in this study is 0.7783 across different starting points of prediction and prediction intervals. The model proposed in this study can provide dynamic prognosis for spontaneous SAH patients and significant potential benefits in critical care management.

The conducted vascular response as a mediator of hypercapnic cerebrovascular reactivity: A modelling study.

It is well established that the cerebral blood flow (CBF) shows exquisite sensitivity to changes in the arterial blood partial pressure of CO2 ( [Formula: see text] ), which is reflected by an index termed cerebrovascular reactivity. In response to elevations in [Formula: see text] (hypercapnia), the vessels of the cerebral microvasculature dilate, thereby decreasing the vascular resistance and increasing CBF. Due to the challenges of access, scale and complexity encountered when studying the microvasculature, however, the mechanisms behind cerebrovascular reactivity are not fully understood. Experiments have previously established that the cholinergic release of the Acetylcholine (ACh) neurotransmitter in the cortex is a prerequisite for the hypercapnic response. It is also known that ACh functions as an endothelial-dependent agonist, in which the local administration of ACh elicits local hyperpolarization in the vascular wall; this hyperpolarization signal is then propagated upstream the vascular network through the endothelial layer and is coupled to a vasodilatory response in the vascular smooth muscle (VSM) layer in what is known as the conducted vascular response (CVR). Finally, experimental data indicate that the hypercapnic response is more strongly correlated with the CO2 levels in the tissue than in the arterioles. Accordingly, we hypothesize that the CVR, evoked by increases in local tissue CO2 levels and a subsequent local release of ACh, is responsible for the CBF increase observed in response to elevations in [Formula: see text] . By constructing physiologically grounded dynamic models of CBF and control in the cerebral vasculature, ones that integrate the available knowledge and experimental data, we build a new model of the series of signalling events and pathways underpinning the hypercapnic response, and use the model to provide compelling evidence that corroborates the aforementioned hypothesis. If the CVR indeed acts as a mediator of the hypercapnic response, the proposed mechanism would provide an important addition to our understanding of the repertoire of metabolic feedback mechanisms possessed by the brain and would motivate further in-vivo investigation. We also model the interaction of the hypercapnic response with dynamic cerebral autoregulation (dCA), the collection of mechanisms that the brain possesses to maintain near constant CBF despite perturbations in pressure, and show how the dCA mechanisms, which otherwise tend to be overlooked when analysing experimental results of cerebrovascular reactivity, could play a significant role in shaping the CBF response to elevations in [Formula: see text] . Such in-silico models can be used in tandem with in-vivo experiments to expand our understanding of cerebrovascular diseases, which continue to be among the leading causes of morbidity and mortality in humans.

Static autoregulation in humans.

The process by which cerebral blood flow (CBF) remains approximately constant in response to short-term variations in arterial blood pressure (ABP) is known as cerebral autoregulation. This classic view, that it remains constant over a wide range of ABP, has however been challenged by a growing number of studies. To provide an updated understanding of the static cerebral pressure-flow relationship and to characterise the autoregulation curve more rigorously, we conducted a comprehensive literature research. Results were based on 143 studies in healthy individuals aged 18 to 65 years. The mean sensitivities of CBF to changes in ABP were found to be 1.47 ± 0.71%/% for decreased ABP and 0.37 ± 0.38%/% for increased ABP. The significant difference in CBF directional sensitivity suggests that cerebral autoregulation appears to be more effective in buffering increases in ABP than decreases in ABP. Regression analysis of absolute CBF and ABP identified an autoregulatory plateau of approximately 20 mmHg (ABP between 80 and 100 mmHg), which is much smaller than the widely accepted classical view. Age and sex were found to have no effect on autoregulation strength. This data-driven approach provides a quantitative method of analysing static autoregulation that can be easily updated as more experimental data become available.

A Pilot Study Evaluating a Virtual Reality-Based Nontechnical Skills Training Application for Urology Trainees: Usability, Acceptability, and Impact.

OBJECTIVE: This study aimed to develop and evaluate a virtual reality (VR)-based nontechnical skills (NTS) training application for urology trainees and assess its effectiveness in improving their skills and confidence. DESIGN: A mixed-methods study was conducted to develop and evaluate a VR-based NTS training application for 32 urology trainees. The development process involved collaboration with 5 urology experts, 2 medical education specialists, and a human factors researcher. The study evaluated the application's usability, acceptability, and efficacy through 3 phases: scenario development with expert feedback integration, storyboarding and creation processes with facilitators and urology trainees, and a final evaluation by trainees. SETTING: The data were collected during a 4-day urology boot camp in October 2022. PARTICIPANTS: Thirty-two urology trainees participated in the study and completed 2 VR scenarios designed to enhance their NTS skills RESULTS: The System Usability Scale (SUS) showed a moderate usability score of 66. The Training Evaluation Inventory (TEI) and additional feedback demonstrated positive effects on trainees' learning and confidence in their NTS abilities. Most participants found the application easy to use, and effective and they expressed interest in using similar VR applications for other aspects of surgical training. CONCLUSIONS: VR-based NTS training applications show potential for enhancing urology trainees' nontechnical skills. The integration of expert feedback and immersive technology offers a promising, accessible, and cost-effective solution to the challenges of delivering NTS training. Future research should explore the long-term impact of VR-based NTS training on trainees' performance and patient outcomes and consider incorporating advanced AI technologies for personalized and dynamic learning experiences.

Applying systems thinking to unravel the mechanisms underlying orthostatic hypotension related fall risk.

Orthostatic hypotension (OH) is an established and common cardiovascular risk factor for falls. An in-depth understanding of the various interacting pathophysiological pathways contributing to OH-related falls is essential to guide improvements in diagnostic and treatment opportunities. We applied systems thinking to multidisciplinary map out causal mechanisms and risk factors. For this, we used group model building (GMB) to develop a causal loop diagram (CLD). The GMB was based on the input of experts from multiple domains related to OH and falls and all proposed mechanisms were supported by scientific literature. Our CLD is a conceptual representation of factors involved in OH-related falls, and their interrelatedness. Network analysis and feedback loops were applied to analyze and interpret the CLD, and quantitatively summarize the function and relative importance of the variables. Our CLD contains 50 variables distributed over three intrinsic domains (cerebral, cardiovascular, and musculoskeletal), and an extrinsic domain (e.g., medications). Between the variables, 181 connections and 65 feedback loops were identified. Decreased cerebral blood flow, low blood pressure, impaired baroreflex activity, and physical inactivity were identified as key factors involved in OH-related falls, based on their high centralities. Our CLD reflects the multifactorial pathophysiology of OH-related falls. It enables us to identify key elements, suggesting their potential for new diagnostic and treatment approaches in fall prevention. The interactive online CLD renders it suitable for both research and educational purposes and this CLD is the first step in the development of a computational model for simulating the effects of risk factors on falls.

Investigating spatial variations in dynamic cerebral autoregulation through a computational model of stenosis.

Objective. Dynamic cerebral autoregulation (dCA) is a well-established mechanism that acts to maintain cerebral blood flow (CBF) reasonably constant in response to short-term fluctuations in blood pressure. It is known to be impaired in many clinical conditions, including stenosis, which is also a major risk factor for ischaemic stroke. However, it is not yet well understood whether impairment in dCA in one brain region is independent or not on dCA impairment in other brain regions, for example, whether there are spatial effects of stenosis on dCA. This is due to the complex blood flow environment and the lack of physiological experiments.Approach. We thus establish and apply a novel computational stenosis model including the circle of Willis to investigate and to quantify the degree of dCA impairment and CBF patterns as a function of stenosis fraction, measured in different configurations of the cerebral vasculature.Main results. We find some evidence for dependence between dCA in different brain regions, although this is very preliminary and much more experimental data will be required to answer this question fully.Significance.Our study has provided a first attempt to consider the effect of stenosis in various arteries on cerebral autoregulation to investigate spatial variations in dCA. This has potential applications in the treatment of cerebrovascular diseases where the control of cerebral perfusion is critical but where measurements are scarce.

The need for a course to complete urological education for consultant practice using a simulated 'boot camp' structure at the end of specialist training: A survey-based study.

BACKGROUND AND AIMS: To obtain opinions from urology trainees and consultants regarding the need for, and structure of, a post-specialty training Urology Simulation Boot Camp (USBC) for consultant practice. METHODS AND RESULTS: A survey-based study was conducted, and 'Google Forms' were distributed electronically via social media. Urology specialist trainees (ST) in years 5-7 (ST5-ST7), post-certification of completion of training (CCT) fellows and ST3 boot camp faculty consultants in practice for ≤5 years and >5 years were included. One hundred and seven responses were received. 97.2% of responders thought a pre-consultant USBC was worthwhile; 55.1% selected the course duration to be 2 days. 47.7% felt that the USBC should be delivered post-exam in ST7. 91.6%, 43.9%, 73.8%, 87.9% and 74.8% considered that modules in emergency operative procedures, novel uro-technologies, delivering multidisciplinary team (MDT) meetings, non-clinical consultant roles and responsibilities, stress and burnout to be important, respectively. 62.6% and 31.8% felt that the course should be wholly or part-funded by Health Education England (HEE). CONCLUSIONS: A post-specialty training, pre-consultant, USBC delivered post-exam in ST7, is worthwhile and should include modules on emergency operative procedures, leading MDTs, non-clinical roles and responsibilities and managing stress and burnout in consultant careers. Ideally, it should be fully/part-funded by HEE.

A network-based model of dynamic cerebral autoregulation.

Cerebrovascular diseases continue to be one of the leading causes of morbidity and mortality in humans. Abnormalities in dynamic cerebral autoregulation (dCA) have been implicated in many of these disease conditions. Accurate models are therefore needed to better understand the complex pathophysiology behind impaired dCA. We thus present here a simple framework for modelling a vessel-driven network model of dCA in the microvasculature, as opposed to the conventional compartmental modelling approach. Network models incorporate the actual connectivity and anatomy of the vasculature, thereby allowing us to include and trace changes in the calibre and morphology of individual vessels, investigate the spatial specificity and heterogeneity of the various control mechanisms to help disentangle their contributions, and link the model parameters to the actual network physiology. The proposed control feedback mechanisms are incorporated at the level of the individual vessel, and the dynamic pressure and flow fields are solved for here within a simple vessel network. In response to an upstream pressure drop, the network is found to be able to recover cerebral blood flow (CBF) while exhibiting the characteristic autoregulatory behaviour in terms of changes in vessel calibre and the biphasic flow response. We assess the feasibility of our formulation in larger networks by comparing the simulation results to those obtained using a one-dimensional (1D) model of CBF applied to the same microvasculature network and find that our model results are in very good agreement with the 1D solution, while significantly reducing the computational cost, thus enabling more detailed models of network behaviour to be adopted in the future. Accurate and computationally feasible models of dCA that are more representative of the vasculature can help increase the translatability of haemodynamic models into the clinical environment, which would help develop more informed treatment guidelines for patients with cerebrovascular diseases.

Regional Cerebral Blood Flow Changes in Healthy Ageing and Alzheimer's Disease: A Narrative Review.

BACKGROUND: Cerebral blood flow is known to decline with increasing age and is a potential biomarker to distinguish between healthy and unhealthy ageing, where healthy ageing is defined as an absence of comorbidities in senescence. This review aims to synthesize evidence of cerebral blood flow changes over multiple brain regions, for use as a clinical reference or for in silico modelling. SUMMARY: The search identified 1,087 studies, of which 33 met the inclusion criteria to map the difference in cerebral blood flow reduction between healthy ageing and Alzheimer's disease. Analysis was also performed on the effect of imaging modality and brain region functionality as potential confounding factors. KEY MESSAGES: No significant difference was found between the specific functionality of a brain region and cerebral blood flow in healthy ageing (p = 0.65) or Alzheimer's disease (p = 0.42). Arterial spin labelling MRI imaging was shown to measure statistically larger decreases in flow in both healthy ageing (p = 0.0001) and Alzheimer's disease (p = 0.0465). Cerebral blood flow was shown to decrease 0.3-0.5% per year in healthy ageing, which increased to a decline of 2-5% per year in Alzheimer's disease. There was large variability both between and within individual brain regions, and this variability increased greatly in Alzheimer's disease. Future studies would add value by taking more cerebral blood flow measurements during Alzheimer's disease progression and by investigating ageing with comorbidities such as hypertension.

Transfer function analysis of dynamic cerebral autoregulation: A CARNet white paper 2022 update.

Cerebral autoregulation (CA) refers to the control of cerebral tissue blood flow (CBF) in response to changes in perfusion pressure. Due to the challenges of measuring intracranial pressure, CA is often described as the relationship between mean arterial pressure (MAP) and CBF. Dynamic CA (dCA) can be assessed using multiple techniques, with transfer function analysis (TFA) being the most common. A 2016 white paper by members of an international Cerebrovascular Research Network (CARNet) that is focused on CA strove to improve TFA standardization by way of introducing data acquisition, analysis, and reporting guidelines. Since then, additional evidence has allowed for the improvement and refinement of the original recommendations, as well as for the inclusion of new guidelines to reflect recent advances in the field. This second edition of the white paper contains more robust, evidence-based recommendations, which have been expanded to address current streams of inquiry, including optimizing MAP variability, acquiring CBF estimates from alternative methods, estimating alternative dCA metrics, and incorporating dCA quantification into clinical trials. Implementation of these new and revised recommendations is important to improve the reliability and reproducibility of dCA studies, and to facilitate inter-institutional collaboration and the comparison of results between studies.

Computational modelling of cerebral oedema and osmotherapy following ischaemic stroke.

In ischaemic stroke, a large reduction in blood supply can lead to the breakdown of the blood brain barrier and to cerebral oedema after reperfusion therapy. Cerebral oedema is marked by elevated intracranial pressure (ICP), tissue herniation and reduced cerebral perfusion pressure. In clinical settings, osmotherapy has been a common practice to decrease ICP. However, there are no guidelines on the choice of administration protocol parameters such as injection doses, infusion time and retention time. Most importantly, the effects of osmotherapy have been proven controversial since the infusion of osmotic agents can lead to a range of side effects. Here, a new Finite Element model of brain oedema and osmotherapy is thus proposed to predict treatment outcome. The model consists of three components that simulate blood perfusion, oedema, and osmotherapy, respectively. In the perfusion model (comprising arteriolar, venous, and capillary blood compartments), an anatomically accurate brain geometry is used to identify regions with a perfusion reduction and potential oedema occurrence in stroke. The oedema model is then used to predict ICP using a porous circulation model with four fluid compartments (arteriolar blood, venular blood, capillary blood, and interstitial fluid). In the osmotherapy model, the osmotic pressure is varied and the changes in ICP during different osmotherapy episodes are quantified. The simulation results of the model show excellent agreement with available clinical data and the model is employed to study osmotherapy under various parameters. Consequently, it is demonstrated how therapeutic strategies can be proposed for patients with different pathological parameters based on simulations.