update per September 2020

Population and morphology models

The aim of WP2 was to develop and validate a population model based on available clinical and imaging data. This model should consist of a set of probability density distributions of relevant parameters, including known correlations, obtained from real patient data. Populations of virtual stroke patients can be selected to undergo virtual stroke treatments followed by outcome assessment. The virtual population model consists of a statistical module that has been fitted based on large database of real stroke patients and will be validated based on another dataset.

Virtual stroke patients are then a sample of this model and need to be realistic representatives of real stroke patients incorporating important prognostic clinical and imaging characteristics such as age, sex, cardiovascular comorbidities, collateral status and location of the intracranial arterial occlusion. The first version of the population model included 12 clinical and imaging characteristics. The discriminative ability of the first version of the stroke population model was poor, indicating that it was difficult to differentiate virtual stroke patients from real stroke patients. Distributions of the virtual stroke population characteristics as well ass the correlations between the parameters were similar to what has been observed in cohorts of real stroke patients (Figure 1).  

 

Figure1 WP2

Figure 1. A selection of the distributions in the virtual stroke population versus a real stroke patient cohort

Currently, we are extending the first version of the virtual population model with three additional prognostic clinical characteristics. Anatomical features, like intracranial vessel geometries and clot histological parameters will be linked to clinical and imaging characteristics incorporated in the virtual stroke population model. The updated versions of the population model will be externally validated in the coming months.

Furthermore, we have developed an online tool from which cohorts of virtual stroke patients can be sampled based on in-and exclusion criteria of interest. This tool will be integrated within the in silico stroke trial platform (Figure 2).

Figure2 WP2

 

Figure 2. The virtual stroke population dashboard

Clinical outcome estimation after virtual stroke treatment

In work package 2, we are developing statistical models to estimate clinical outcome for cohorts of virtual stroke patients after virtual stroke treatment. For this estimation, the outcome of virtual treatment models (e.g. recanalization success) and tissue perfusion models (follow-up infarct volume) will be used as predictors. Besides, the virtual patient characteristics will also be included in the statistical models improve the prediction of clinical outcomes like National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Score (mRS).

 

Imaging features of clots related to clot composition

We have created a broad analysis of clot characteristics derived from non-contrast computed tomography and CT angiography, conducted at admission of 332 ischemic stroke patients in the MR CLEAN Registry. After this, we have assessed the relationship between the imaging features of these clots to their histological composition. These findings served as input for work packages 3 and 4.

WP2 1

Figure 3. Measurements of clot characteristics on admission computed tomography of stroke patients. (A: clot length, B: distance from ICA-terminus to the clot).

Biomechanical thrombus analysis

From July 1st to October 3rd, we have performed biomechanical characterization and histological analysis of 41 clots from 19 acute ischemic stroke patients in the Erasmus Medical Center in Rotterdam. The results of this study are currently being prepared for scientific publication.

WP2 2 

Figure 4. Close-up of the in-house developed force tester, that was used for the mechanical characterization of human stroke thrombi in Rotterdam.

 

In silico models for thrombosis and thrombolysis

 

WP3 has developed three numerical models, with different purposes:

(1) a 1-D macroscopic thrombolysis model aimed to provide calibration and sensitivity analysis.
(2) A 0-D macroscopic model of combined thrombosis and thrombolysis able to describe and understand  in-vitro experiments.
(3) A 3-D mesoscopic, parallel HPC model, simulating the thrombolysis in patient geometries and heterogeneous clots. This is the candidate model for the in-silico trials in WP6.  
 
 

Here is a video from our current simulations for the 3D mesoscopic thrombolysis 

 
(1) a full thrombosis-thrombolysis endogeneous in-vitro experiment giving the time evolution of the thrombus.
(2) A detailed analysis of the histology of the patients clots, showing the variation of composition and spatial heterogeneity (also discussed in WP2).
(3) An analysis of fibrin pure thrombis to determine the fiber diameters as a function of the fibrinogen concentration. (4) the flow of a contrast agent in a thrombus subject to a pressure gradient.
 
Validations of the numerical models have been conducted against literature data and patient data for porosity and permeability values. Lysis time between the 1D macroscopic model and the 3D mesoscopic mode have been compared. Compatibility with clinical lysis times have also been verified.  The 0-D model of thrombosis-thrombolysis has been validated and calibrated from in-vitro observations.
 
A sensitivity analysis was performed with the 1D model, identifying the most important quantities that affect lysis (concentrations of various molecules, presence of colaterals, and clot heterogeneity).
 
In conclusion, from a modeling point of view, the achievements are on track, but the validation remains a challenge. First, in-vitro and in-vivo data that can be compared with numerical simulations are not always available or easy to produced. Second, the lysis process is still insufficiently understood to describe all relevant situations and have a universal model.

In silico models for thrombectomy

 

 

WP4 Picture1 Video Completo

The simulations showed that our models can replicate the thrombus extraction.

The proposed workflow is amenable to model the thrombectomy procedure and it could be used to predict the procedure outcomes but also to optimize the procedure itself or the stent-retriever design.

 Intra-arterial thrombectomy is a minimally invasive procedure based on stent technology. After the stent-retriever deployment, the thrombus is dislodged by retrieval of the stent.
 Patient-specific details of the procedure were collected, and thrombus characteristics were included in the model. Geometry of the stent-retriever was created to replicate the real design of the device. WP4 Picture2

 

  In silico models of perfusion defects and tissue damage

In WP5, we have been developing new models of blood flow and oxygen transport in the brain. These have been built up from the very smallest vessels (capillaries) to the large supply vessels (arteries) to the brain. Linking all of these models together has been a big challenge, but we can now model blood flowing all the way through the brain. Since blood transports oxygen to the brain cells, we have also been able to model how oxygen is delivered to brain tissue for the first time in such a detailed way. This is important because it means that we can simulate not only the response of the brain to a stroke, i.e. when a large vessel is blocked, but also the response of the brain when a clot is removed, and some very small clots remain in the bloodstream, blocking smaller vessels downstream (which can be a problem if there are lots of these). In parallel with the modelling work, we have been performing animal studies, looking at how these small clots affect both the transport of oxygen to tissue and how it dies in response. In the last year of the project we will be tying all these models together and validating them using both the animal data and clinical data. This will mean that we will have a fully-validated simulator of stroke for the first time that can be used to help in other WP to simulate the response to stroke and its treatment.

 

WP5 2 Capillary bed 625   WP5 Trees12 box slices2
 A statistically accurate model of the capillary bed in human grey matter   Penetrating vessels descending into a voxel of grey matter. Red layers indicate slices over which permeability was calculated

           

Integration and validation

 

Current work in WP6 continues the implementation and improvement of the framework, and determining methodologies for the VVUQ procedures. Figure 1 shows an overview of the data flow between the different models (and WPs) for the trial.

WP6 figure 1

Figure 1. The trial models and the arrows show the data transfer between them.

The INSIST framework uses containers (Docker or Singularity) for each of the models in order to maximise portability between computer environments and assist with reproducibility. A command line interface has been developed for the easy creation and running of trials, with patient specifications detailed in a patient YAML file. Simple parallel support has also been added to enable running larger trial cohorts.

Preliminary uncertainty quantification (UQ) activities have begun for the VVUQ of the trial. In order to easily implement the UQ methodologies, the EasyVVUQ framework, developed within the VECMA project, was integrated into the INSIST trial framework. We then undertook preliminary UQ analysis of the blood flow model in both baseline and occluded state, using a Quasi-Monte Carlo approach. Four parameters were considered: blood viscosity, density, stroke volume, and heart rate. Give the uncertainties in the inputs shown in Figure 2 (the histograms show the sample points used for the analysis), we can determine the sensitivity of the output of interest, in this case flow rate in the occluded vessel, to each of these uncertainties. This is shown in Figure 3. Note however, these results are preliminary and shown to demonstrate the use of EasyVVUQ with the INSIST framework.

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