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BIOTEK2021-Bioteknologi for verdiskaping

ERA-NET: Personalized Atrial fibrillation Risk of Ischemic Stroke assessment (PARIS)

Alternative title: Personlig Risikovurdering av Hjerneslag ved Atrieflimmer

Awarded: NOK 4.2 mill.

Atrial Fibrillation (AF) is a complex cardiac disease characterized by chaotic electrical activation and loss of proper contraction, which may lead to blood clots and increase the risk of stroke. AF is gaining epidemic proportions and currently affects more than 6 million Europeans, with an annual cost exceeding 13.5 billion Euros, and the number of patients is expected to double by 2030. The majority of AF patients are prescribed anticoagulants that markedly reduce stroke incidence, but at the cost of increased risk of severe bleedings. Anticoagulation management needs to balance these risks, which remains a major challenge. Existing guidelines for medication management are based on population-level statistical correlations, do not account for the underlying mechanisms of clot formation, and routinely available clinical data are under-utilized. The overall goal for the PARIS project was to use patient-specific computational models of atrial blood flow, to give more precise diagnostic and medication management. Such computations are challenging to perform, because of the complex flow patterns and complex anatomy of the heart. The focus of the first phase of the project was to build an accurate and reliable simulator for patient-specific blood flow in the atria. The first version of this simulator has been completed, and has been tested and validated by comparing the results with blood flow measurements in the atria and the ventricles. We have also completed an automated and objective framework for high-throughput simulation of cardiac flows on high-performance computing clusters that will be used to pursue clinically relevant questions. The newly developed blood flow simulator has been applied to labeled clinical data, and used to assess the impact of common modeling assumptions on atrial flows. We are currently working closely with our collaborators to further integrate patient data with computational tools, to quantify the added value of patient-specific models for clinical thrombus risk assessment.

The overall ambition for the PARIS project was to holistically answer fundamental questions in vascular biology critical for our understanding of AF, and to develop new tools for individualized risk assessment. The majority of the work has been devoted to the development of novel and sophisticated numerical models, which are needed to test hypotheses in cardiovascular disease. We have developed a robust, automated and objective framework based on tools for medical image segmentation, landmarking, and atlasing, with minimal operator dependency. These tools and pipelines, which are provided to the research community as open source software, represent a major impact from the project. We have demonstrated the applicability of the tools in clinically relevant settings, with the aim of studying correlations between atrial form and function, and mechanisms of thrombus formation or cardiovascular events. with the aim of setting a new standard for simulations of cardiovascular flows. Ongoing research, in collaboration with clinical partners, aims to study the correlation between individual flow patterns and patient outcome. This study could potentially impact the clinical management of AF as well as our mechanistic understanding of flow blood clot formation.

Atrial Fibrillation (AF) is a complex cardiac disease characterized by chaotic electrical activation and loss of atrial contraction, which creates a hemodynamic environment that is prone to clot formation and a six-fold increase in risk of ischemic stroke. AF is gaining epidemic proportions and currently affects more than 6 million Europeans, with an annual cost exceeding €13.5 billion, and the number of patients expected to double by 2030. The majority of AF patients are prescribed anticoagulants that markedly reduce stroke incidence, but at the cost of increased risk of severe bleedings. Individualized anticoagulation management remains a major challenge, and current risk scores for stratifying stroke and bleed risk (e.g. CHA2DS2-VASc, HAS-BLED) show poor performance. The current risk scores are based on population-level statistical correlations only, do not account for the underlying mechanisms of clot formation, and routinely available patient-specific clinical data are under-utilized. Computational models of the atria have reached a high level of sophistication, and include advanced statistical representations of atrial morphology and motion, as well as biophysically detailed models of tissue- and fluid dynamics. Model-based tools for diagnosis and prediction are emerging, but remain insufficiently validated and tested to be used for individualized clinical predictions. PARIS will utilize existing medical records of AF patients with known clinical outcome, to tune and validate computer models and predictive machine learning methods in an iterative process. The resulting decision support system will be validated retrospectively by predicting individualized disease outcome in a matched case-control cohort. The ambition is to identify biomarkers that correlate with stroke, bleeding and other severe complications, and to prospectively outperform the current risk score to reduce individual bleeds by optimizing personalized treatment and clinical follow-up.

Funding scheme:

BIOTEK2021-Bioteknologi for verdiskaping