To investigate improvements in HFpEF hemodynamics numerical simulations using different device based therapy approaches (IASD, Co-Pulse, rotodynamic blood pump for ventricle or atrium) were performed. HFpEF patients present with various co-morbidities, risk factors and heterogenous hemodynamics. Machine learning methods were used to analyze real-world patient data from an observational HFpEF registry to identify hemodynamic HFpEF phenogroups.
Research Group Leader
Marcus Granegger, PhD