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CARDIOPROOF – a ‘proof-of-concept’ for model-based cardiovascular prediction

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  • CARDIOPROOF is a three-year project co-funded by the European Commission under its seventh framework program.
  • The project partners are working to assess the applicability and effectiveness of predictive modeling and simulation tools for cardiology.
  • CARDIOPROOF aims to enhance the role of the physician in the modeling process chain by markedly improving data management, enabling access to cloud-based central resources, and making use of user-friendly human interfaces.

One of the tools implemented within CARDIOPROOF is the virtual stenting software, which facilitates the treatment of the aortic coarctation. This simulator, developed at Fraunhofer MEVIS in Bremen, can be installed on Windows or Mac OS operating systems and allows clinicians to simulate treatment options prior to the intervention. The virtual stenting allows them to extract patient-individual anatomy, assess the pre-interventional hemodynamics, and interactively simulate the stent placement and deformation.

Previous research efforts have developed some powerful tools for computer-based modeling of various cardiovascular (CV) diseases. This has raised significant expectations of such tools being made available for early diagnosis and for predicting disease behavior and evolution, as well as treatment outcomes. The translation into a routine clinical environment has, however, remained challenging and substantially bounded. In fact, despite an increasing interest from the medical community to apply modeling methods to CV diseases, limited results have been achieved so far: currently, clinical guidelines are highly complex and rely mostly on imaging diagnostics and clinical parameters, without benefiting, as yet, from prediction based on patient-specific disease modeling.

CARDIOPROOF goes beyond the current state of the art by conducting validation trials aimed at covering and comparing the complete spectrum of cardiovascular treatment in aortic valve disease (AVD) and aortic coarctation (CoA) (narrowing of the aorta), predicting the evolution of both diseases and the immediate and midterm outcome of treatment. With more than 50,000 interventions per year within the EU, the diseases addressed by CARDIOPROOF have a significant socio-economic impact.

The team of Gernot Plank at the Medical University of Graz is working on the development, application, and validation of a modelling methodology for performing patient-specific in-silico simulations of ventricular electromechanics. Such simulations provide deformation data, which become usable as boundary conditions for fluid-flow simulation in other modelling activities within CARDIOPROOF. This model development targets both cardiac anatomy and physiology, with sufficient detail to facilitate a direct clinical interpretation of computed simulation results. Besides serving as input for CFD simulations, physiologically important parameters such as regional distribution of strains, stresses, myocardial work, and energy consumption are also computed.

In brief, using already developed modeling methods, the primary objectives of CARDIOPROOF are:

  1. Conduct validation trials in patients with AVD or CoA that reflect a real-world approach by covering and comparing the complete spectrum of cardiovascular treatment.
  2. Provide first data about comparative clinical and cost effectiveness of in-silico approaches compared to conventional diagnostic and treatment algorithms.
  3. Accelerate the deployment of CV model-based methods by improving their usability and interoperability in the clinical context.

CARDIOPROOF aggregates information from multiple biological levels with regard to the patient-specific disease state, and pays particular attention to user-friendliness as a key component of clinical usability. While improving usability and interoperability, CARDIOPROOF aims also to enhance the role of the physician in the modeling process chain by markedly improving data management, enabling access to cloud-based central resources, and making use of user-friendly human interfaces.

To prove the concreteness, effectiveness, and significance of in-silico modeling, CARDIOPROOF evaluates the impact of the cardiac simulation modeling tools, as compared to current standard practice on clinical decision making.

Knowledge of pressure and velocity of blood flow in the human cardiovascular system can be decisive for clinical evaluations (initial and post-procedural) and procedure planning. For example, the severity of the cardiovascular diseases targeted in this project (CoA and AVD) can be assessed by intraluminal pressure gradients. A team of scientists at SIEMENS has developed a method to noninvasively compute, from magnetic-resonance imaging (MRI), the relative pressure within the aorta. This technique has been applied to some first CARDIOPROOF patients, delivering sound results even with still limited data. The whole pipeline will now be validated using the coarctation cases, which include invasive pressure measurements. This way, it will become possible to directly compare the simulated pressures with the measured ones, and to investigate the relationship between treatment outcomes and simulation results.

A comparative-effectiveness study is underway, to evaluate whether the additional parameters that are made available through image-based simulation modeling would result in different clinical decision making. In doing so, the questions addressed through randomized controlled experiments are aimed at ascertaining whether taking into account image-based modeling results would lead clinicians to adopt different decisions (compared to using current clinical practice guidelines).

In collaboration with its three clinical partners, two separate imaging datasets are generated for each of the patients recruited in CARDIOPROOF. The first dataset includes the imaging parameters currently recommended by clinical practice guidelines (referred to as ‘limited dataset’). The second dataset includes an expanded list of parameters, including  information that is available from traditional imaging parameters (as recommended by the guidelines), and simulation modeling (referred to as ‘image-based modeling dataset’).

 This article is republished from GÉANT’s CONNECT magazine. For more articles like this one, read the latest issue in full.A computerized random-sample function is used to randomly allocate interventional cardiologists (not directly involved in CARDIOPROOF) into two separate groups. Each group is then provided with one set of imaging data, either the limited dataset or the image-based modeling dataset. Thus, it can be checked whether the two groups reach different treatment decisions on the basis of the information made available through the image-based modeling dataset.

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