Atrial Fibrillation (AF) is a complex cardiac disease characterized by chaotic electrical activation and loss of proper contraction, which could 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 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 therefore remains a major challenge. The current medication management is based on population-level statistical correlations only, does not account for the underlying mechanisms of clot formation, and routinely available patient-specific clinical data are under-utilized. The goal for the PARIS project is to use sophisticated computational models of blood flow in the heart of AF patients, 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 has been to build an accurate and reliable simulator for the 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. With our newly developed sophisticated blood flow simulator we are working with labelled clinical data and assessing modelling assumptions and the impact on atrial flows. We have also completed an automated and objective framework for mass simulation of cardiac flows on high-performance computing clusters that will be used to pursue clinically relevant questions.
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.
