Super-resolution left ventricular flow and pressure mapping by Navier-Stokes-informed neural networks.

Intraventricular vector flow mapping (VFM) is an increasingly adopted echocardiographic technique that derives time-resolved two-dimensional flow maps in the left ventricle (LV) from color-Doppler sequences. Current VFM models rely on kinematic constraints arising from planar flow incompressibility. However, these models are not informed by crucial information about flow physics; most notably the forces within the fluid and the resulting accelerations.

The long-term vascular and myocardial outcomes in selected Kawasaki disease patients with regression of giant coronary artery aneurysms.

Background: Giant coronary artery aneurysms and myocardial fibrosis after Kawasaki disease may lead to devastating cardiovascular outcomes. We characterised the vascular and myocardial outcomes in five selected Kawasaki disease patients with a history of giant coronary artery aneurysms that completely regressed.

Methods: Five patients were selected who had giant coronary artery aneurysm in early childhood that regressed when studied 12-33 years after Kawasaki disease onset.

Deriving phenotype-representative left ventricular flow patterns by reduced-order modeling and classification.

Background: Extracting phenotype-representative flow patterns and their associated numerical metrics is a bottleneck in the clinical translation of advanced cardiac flow imaging modalities. We hypothesized that reduced-order models (ROMs) are a suitable strategy for deriving simple and interpretable clinical metrics of intraventricular flow suitable for further assessments. Combined with machine learning (ML) flow-based ROMs could provide new insight to help diagnose and risk-stratify patients.

Super-resolution Left Ventricular Flow and Pressure Mapping by Navier-Stokes-Informed Neural Networks.

Intraventricular vector flow mapping (VFM) is a growingly adopted echocardiographic modality that derives time-resolved two-dimensional flow maps in the left ventricle (LV) from color-Doppler sequences. Current VFM models rely on kinematic constraints arising from planar flow incompressibility. However, these models are not informed by crucial information about flow physics; most notably the pressure and shear forces within the fluid and the resulting accelerations.

Efficient multi-fidelity computation of blood coagulation under flow.

Clot formation is a crucial process that prevents bleeding, but can lead to severe disorders when imbalanced. This process is regulated by the coagulation cascade, a biochemical network that controls the enzyme thrombin, which converts soluble fibrinogen into the fibrin fibers that constitute clots. Coagulation cascade models are typically complex and involve dozens of partial differential equations (PDEs) representing various chemical species' transport, reaction kinetics, and diffusion.

Deriving Explainable Metrics of Left Ventricular Flow by Reduced-Order Modeling and Classification.

Background: Extracting explainable flow metrics is a bottleneck to the clinical translation of advanced cardiac flow imaging modalities. We hypothesized that reduced-order models (ROMs) of intraventricular flow are a suitable strategy for deriving simple and interpretable clinical metrics suitable for further assessments. Combined with machine learning (ML) flow-based ROMs could provide new insight to help diagnose and risk-stratify patients.

Pulmonary vein flow split effects in patient-specific simulations of left atrial flow.

Disruptions to left atrial (LA) blood flow, such as those caused by atrial fibrillation (AF), can lead to thrombosis in the left atrial appendage (LAA) and an increased risk of systemic embolism. LA hemodynamics are influenced by various factors, including LA anatomy and function, and pulmonary vein (PV) inflow conditions. In particular, the PV flow split can vary significantly among and within patients depending on multiple factors.

Myocardial Regional Shortening from 4D Cardiac CT Angiography for the Detection of Left Ventricular Segmental Wall Motion Abnormality.

Purpose: To investigate whether endocardial regional shortening computed from four-dimensional (4D) CT angiography (RS) can be used as a decision classifier to detect the presence of left ventricular (LV) wall motion abnormalities (WMAs).

Materials And Methods: One hundred electrocardiographically gated cardiac 4D CT studies (mean age, 59 years ± 14 [SD]; 61 male patients) conducted between April 2018 and December 2020 were retrospectively evaluated. Three experts labeled LV wall motion in each of the 16 American Heart Association (AHA) segments as normal or abnormal; they also measured peak RS across one heartbeat in each segment.

Predictors of Myocardial Ischemia in Patients with Kawasaki Disease: Insights from Patient-Specific Simulations of Coronary Hemodynamics.

Current treatments for patients with coronary aneurysms caused by Kawasaki disease (KD) are based primarily on aneurysm size. This ignores hemodynamic factors influencing myocardial ischemic risk. We performed patient-specific computational hemodynamics simulations for 15 KD patients, with parameters tuned to patients' arterial pressure and cardiac function.

Deep learning automates detection of wall motion abnormalities measurement of longitudinal strain from ECG-gated CT images.

Introduction: 4D cardiac CT (cineCT) is increasingly used to evaluate cardiac dynamics. While echocardiography and CMR have demonstrated the utility of longitudinal strain (LS) measures, measuring LS from cineCT currently requires reformatting the 4D dataset into long-axis imaging planes and delineating the endocardial boundary across time. In this work, we demonstrate the ability of a recently published deep learning framework to automatically and accurately measure LS for detection of wall motion abnormalities (WMA).

Biodegradable external wrapping promotes favorable adaptation in an ovine vein graft model.

Vein grafts, the most commonly used conduits in multi-vessel coronary artery bypass grafting surgery, have high intermediate- and long-term failure rates. The abrupt and marked increase in hemodynamic loads on the vein graft is a known contributor to failure. Recent computational modeling suggests that veins can more successfully adapt to an increase in mechanical load if the rate of loading is gradual.

Detection of left ventricular wall motion abnormalities from volume rendering of 4DCT cardiac angiograms using deep learning.

Background: The presence of left ventricular (LV) wall motion abnormalities (WMA) is an independent indicator of adverse cardiovascular events in patients with cardiovascular diseases. We develop and evaluate the ability to detect cardiac wall motion abnormalities (WMA) from dynamic volume renderings (VR) of clinical 4D computed tomography (CT) angiograms using a deep learning (DL) framework.

Methods: Three hundred forty-three ECG-gated cardiac 4DCT studies (age: 61 ± 15, 60.

Non-Newtonian blood rheology impacts left atrial stasis in patient-specific simulations.

The lack of mechanically effective contraction of the left atrium (LA) during atrial fibrillation (AF) disturbs blood flow, increasing the risk of thrombosis and ischemic stroke. Thrombosis is most likely in the left atrial appendage (LAA), a small narrow sac where blood is prone to stagnate. Slow flow promotes the formation of erythrocyte aggregates in the LAA, also known as rouleaux, causing viscosity gradients that are usually disregarded in patient-specific simulations.

Trends in testing and prevalence of elevated Lp(a) among patients with aortic valve stenosis.

Background And Aims: Lipoprotein(a) [Lp(a)] is causally associated with aortic valve stenosis (AS) but Lp(a) testing among AS patients is not broadly incorporated into clinical practice. We evaluated trends in Lp(a) testing in an academic medical center.

Methods: Educational efforts and adding Lp(a) to the lipid panel on the electronic medical record (EMR) and pre-procedure order sets were used to increase awareness of Lp(a) as a risk factor in AS.

Systolic dysfunction in patients with methamphetamine use and heart failure with preserved ejection fraction.

Background: We aimed to evaluate for occult systolic dysfunction and the effect of methamphetamine cessation among patients with methamphetamine use (MU) and heart failure with preserved ejection fraction (HFpEF).

Methods: A retrospective cohort of patients with HFpEF with serial echocardiograms was stratified by MU and evaluated using myocardial strain analysis on echocardiograms at baseline and 1 year to measure global longitudinal strain (GLS). Contemporaneous controls with an ICD diagnosis of HF within 3 days of an MU case were chosen.

Myocardial Strain and Association With Clinical Outcomes in Danon Disease: A Model for Monitoring Progression of Genetic Cardiomyopathies.

Background Myocardial strain can identify subclinical left ventricular dysfunction in various cardiac diseases, but its association with clinical outcomes in genetic cardiomyopathies remains unknown. Herein, we assessed myocardial strain in patients with Danon disease (DD), a rare X-linked autophagic disorder that causes severe cardiac manifestations. Methods and Results Echocardiographic images were reviewed and used to calculate myocardial strain from a retrospective, international registry of patients with DD.

Biomarkers of inflammation and fibrosis in young adults with history of Kawasaki disease.

Background: Myocardial histology from autopsies of young adults with giant coronary artery aneurysms following Kawasaki disease (KD) shows bridging fibrosis beyond the territories supplied by the aneurysmal arteries. The etiology of this fibrosis is unknown, but persistent, low-level myocardial inflammation and microcirculatory ischemia are both possible contributing factors. To investigate the possibility of subclinical myocardial inflammation or fibrosis, we measured validated biomarkers in young adults with a remote history of KD.

Computational modeling of blood component transport related to coronary artery thrombosis in Kawasaki disease.

Coronary artery thrombosis is the major risk associated with Kawasaki disease (KD). Long-term management of KD patients with persistent aneurysms requires a thrombotic risk assessment and clinical decisions regarding the administration of anticoagulation therapy. Computational fluid dynamics has demonstrated that abnormal KD coronary artery hemodynamics can be associated with thrombosis.

Non-invasive characterization of complex coronary lesions.

Conventional invasive diagnostic imaging techniques do not adequately resolve complex Type B and C coronary lesions, which present unique challenges, require personalized treatment and result in worsened patient outcomes. These lesions are often excluded from large-scale non-invasive clinical trials and there does not exist a validated approach to characterize hemodynamic quantities and guide percutaneous intervention for such lesions. This work identifies key biomarkers that differentiate complex Type B and C lesions from simple Type A lesions by introducing and validating a coronary angiography-based computational fluid dynamic (CFD-CA) framework for intracoronary assessment in complex lesions at ultrahigh resolution.

Demonstration of Patient-Specific Simulations to Assess Left Atrial Appendage Thrombogenesis Risk.

Atrial fibrillation (AF) alters left atrial (LA) hemodynamics, which can lead to thrombosis in the left atrial appendage (LAA), systemic embolism and stroke. A personalized risk-stratification of AF patients for stroke would permit improved balancing of preventive anticoagulation therapies against bleeding risk. We investigated how LA anatomy and function impact LA and LAA hemodynamics, and explored whether patient-specific analysis by computational fluid dynamics (CFD) can predict the risk of LAA thrombosis.

Computational Evaluation of Venous Graft Geometries in Coronary Artery Bypass Surgery.

Cardiothoracic surgeons are faced with a choice of different revascularization techniques and diameters for saphenous vein grafts (SVG) in coronary artery bypass graft surgery . Using computational simulations, we virtually investigate the effect of SVG geometry on hemodynamics of both venous grafts and the target coronary arteries. We generated patient-specific 3-dimensional anatomic models of coronary artery bypass graft surgery patients and quantified mechanical stimuli.

High Risk α-HPV E6 Impairs Translesion Synthesis by Blocking POLη Induction.

High risk genus α human papillomaviruses (α-HPVs) express two versatile oncogenes (α-HPV E6 and E7) that cause cervical cancer (CaCx) by degrading tumor suppressor proteins (p53 and RB). α-HPV E7 also promotes replication stress and alters DNA damage responses (DDR). The translesion synthesis pathway (TLS) mitigates DNA damage by preventing replication stress from causing replication fork collapse.

Intraventricular Flow Patterns in Patients Treated with Left Ventricular Assist Devices.

The success of left ventricular assist device (LVAD) therapy is hampered by complications such as thrombosis and bleeding. Understanding blood flow interactions between the heart and the LVAD might help optimize treatment and decrease complication rates. We hypothesized that LVADs modify shear stresses and blood transit in the left ventricle (LV) by changing flow patterns and that these changes can be characterized using 2D echo color Doppler velocimetry (echo-CDV).