Surgical Modulation of Pulmonary Artery Shear Stress: A Patient-Specific CFD Analysis of the Norwood Procedure.

PURPOSR: This study created 3D CFD models of the Norwood procedure for hypoplastic left heart syndrome (HLHS) using standard angiography and echocardiogram data to investigate the impact of shunt characteristics on pulmonary artery (PA) hemodynamics. Leveraging routine clinical data offers advantages such as availability and cost-effectiveness without subjecting patients to additional invasive procedures. METHODS: Patient-specific geometries of the intrathoracic arteries of two Norwood patients were generated from biplane cineangiograms. "Virtual surgery" was then performed to simulate the hemodynamics of alternative PA shunt configurations, including shunt type (modified Blalock-Thomas-Taussig shunt (mBTTS) vs. right ventricle-to-pulmonary artery shunt (RVPAS)), shunt diameter, and pulmonary artery anastomosis angle. Left-right pulmonary flow differential, Qp/Qs, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) were evaluated. RESULTS: There was strong agreement between clinically measured data and CFD model output throughout the patient-specific models. Geometries with a RVPAS tended toward more balanced left-right pulmonary flow, lower Qp/Qs, and greater TAWSS and OSI than models with a mBTTS. For both shunt types, larger shunts resulted in a higher Qp/Qs and higher TAWSS, with minimal effect on OSI. Low TAWSS areas correlated with regions of low flow and changing the PA-shunt anastomosis angle to face toward low TAWSS regions increased TAWSS. CONCLUSION: Excellent correlation between clinically measured and CFD model data shows that 3D CFD models of HLHS Norwood can be developed using standard angiography and echocardiographic data. The CFD analysis also revealed consistent changes in PA TAWSS, flow differential, and OSI as a function of shunt characteristics.

Characterization of hemodynamics in anomalous aortic origin of coronary arteries using patient-specific modeling.

The anomalous aortic origin of coronary arteries (AAOCA) is a congenital disease that can lead to sudden cardiac death (SCD) during strenuous physical activity. Despite AAOCA being the second leading cause of SCD among young athletes, the mechanism behind sudden cardiac death remains mostly unknown. Computational fluid dynamics provides a powerful tool for studying how pathologic anatomy can affect different hemodynamic states. The present study investigates the effect of AAOCA on patient hemodynamics. We performed patient-specific hemodynamic simulations of interarterial AAOCA at baseline and in the exercise state using our massively parallel flow solver. Additionally, we investigate how surgical correction via coronary unroofing impacts patient blood flow. Results show that patient-specific AAOCA models exhibited higher interarterial time-averaged wall shear stress (TAWSS) values compared to the control patients. The oscillatory shear index had no impact on AAOCA. Finally, the coronary unroofing procedure normalized the elevated TAWSS by decreasing TAWSS in the postoperative patient. The present study provides a proof of concept for the potential hemodynamic factors underlying coronary ischemia in AAOCA during exercise state.

High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory.

The ability to track simulated cancer cells through the circulatory system, important for developing a mechanistic understanding of metastatic spread, pushes the limits of today's supercomputers by requiring the simulation of large fluid volumes at cellular-scale resolution. To overcome this challenge, we introduce a new adaptive physics refinement (APR) method that captures cellular-scale interaction across large domains and leverages a hybrid CPU-GPU approach to maximize performance. Through algorithmic advances that integrate multi-physics and multi-resolution models, we establish a finely resolved window with explicitly modeled cells coupled to a coarsely resolved bulk fluid domain. In this work we present multiple validations of the APR framework by comparing against fully resolved fluid-structure interaction methods and employ techniques, such as latency hiding and maximizing memory bandwidth, to effectively utilize heterogeneous node architectures. Collectively, these computational developments and performance optimizations provide a robust and scalable framework to enable system-level simulations of cancer cell transport.

Rapid Ventilator Splitting During COVID-19 Pandemic Using 3D Printed Devices and Numerical Modeling of 200 Million Patient Specific Air Flow Scenarios.

There has been a pressing need for an expansion of the ventilator capacity in response to the recent COVID19 pandemic. To address this need, we present a system to enable rapid and efficacious splitting between two or more patients with varying lung compliances and tidal volume requirements. Reserved for dire situations, ventilator splitting is complex, and has been limited to patients with similar pulmonary compliances and tidal volume requirements. Here, we report a 3D printed ventilator splitter and resistor system (VSRS) that uses interchangeable airflow resistors to deliver optimal tidal volumes to patients with differing respiratory physiologies, thereby expanding the applicability of ventilator splitting to a larger patient pool. We demonstrate the capability of the VSRS using benchtop test lungs and standard-of-care ventilators, which produced data used to validate a complementary, patient-specific airflow computational model. The computational model allows clinicians to rapidly select optimal resistor sizes and predict delivered pressures and tidal volumes on-demand from different patient characteristics and ventilator settings. Due to the inherent need for rapid deployment, all simulations for the wide range of clinically-relevant patient characteristics and ventilator settings were pre-computed and compiled into an easy to use mobile app. As a result, over 200 million individual computational simulations were performed to maximize the number of scenarios for which the VSRS can provide assistance. The VSRS will help address the pressing need for increased ventilator capacity by allowing ventilator splitting to be used with patients with differing pulmonary physiologies and respiratory requirements, which will be particularly useful for developing countries and rural communities with a limited ventilator supply.

Cloud Computing for COVID-19: Lessons Learned From Massively Parallel Models of Ventilator Splitting.

A patient-specific airflow simulation was developed to help address the pressing need for an expansion of the ventilator capacity in response to the COVID-19 pandemic. The computational model provides guidance regarding how to split a ventilator between two or more patients with differing respiratory physiologies. To address the need for fast deployment and identification of optimal patient-specific tuning, there was a need to simulate hundreds of millions of different clinically relevant parameter combinations in a short time. This task, driven by the dire circumstances, presented unique computational and research challenges. We present here the guiding principles and lessons learned as to how a large-scale and robust cloud instance was designed and deployed within 24 hours and 800 000 compute hours were utilized in a 72-hour period. We discuss the design choices to enable a quick turnaround of the model, execute the simulation, and create an intuitive and interactive interface.