Cardiac stasis imaging, stroke, and silent brain infarcts in patients with nonischemic dilated cardiomyopathy.

Cardioembolic stroke is one of the most devastating complications of nonischemic dilated cardiomyopathy (NIDCM). However, in clinical trials of primary prevention, the benefits of anticoagulation are hampered by the risk of bleeding. Indices of cardiac blood stasis may account for the risk of stroke and be useful to individualize primary prevention treatments. We performed a cross-sectional study in patients with NIDCM and no history of atrial fibrillation (AF) from two sources: 1) a prospective enrollment of unselected patients with left ventricular (LV) ejection fraction <45% and 2) a retrospective identification of patients with a history of previous cardioembolic neurological event. The primary end point integrated a history of ischemic stroke or the presence intraventricular thrombus, or a silent brain infarction (SBI) by imaging. From echocardiography, we calculated blood flow inside the LV, its residence time (TR) maps, and its derived stasis indices. Of the 89 recruited patients, 18 showed a positive end point, 9 had a history of stroke or transient ischemic attack (TIA) and 9 were diagnosed with SBIs in the brain imaging. Averaged TR, [Formula: see text] performed well to identify the primary end point [AUC (95% CI) = 0.75 (0.61-0.89), P = 0.001]. When accounting only for identifying a history of stroke or TIA, AUC for [Formula: see text] was 0.92 (0.85-1.00) with odds ratio = 7.2 (2.3-22.3) per cycle, P < 0.001. These results suggest that in patients with NIDCM in sinus rhythm, stasis imaging derived from echocardiography may account for the burden of stroke.NEW & NOTEWORTHY Patients with nonischemic dilated cardiomyopathy (NIDCM) are at higher risk of stroke than their age-matched population. However, the risk of bleeding neutralizes the benefit of preventive oral anticoagulation. In this work, we show that in patients in sinus rhythm, the burden of stroke is related to intraventricular stasis metrics derived from echocardiography. Therefore, stasis metrics may be useful to personalize primary prevention anticoagulation in these patients.

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. Solving these PDE systems computationally is challenging, due to their large size and multi-scale nature. We propose a multi-fidelity strategy to increase the efficiency of coagulation cascade simulations. Leveraging the slower dynamics of molecular diffusion, we transform the governing PDEs into ordinary differential equations (ODEs) representing the evolution of species concentrations versus blood residence time. We then Taylor-expand the ODE solution around the zero-diffusivity limit to obtain spatiotemporal maps of species concentrations in terms of the statistical moments of residence time, [Formula: see text], and provide the governing PDEs for [Formula: see text]. This strategy replaces a high-fidelity system of N PDEs representing the coagulation cascade of N chemical species by N ODEs and p PDEs governing the residence time statistical moments. The multi-fidelity order (p) allows balancing accuracy and computational cost providing a speedup of over N/p compared to high-fidelity models. Moreover, this cost becomes independent of the number of chemical species in the large computational meshes typical of the arterial and cardiac chamber simulations. Using a coagulation network with N = 9 and an idealized aneurysm geometry with a pulsatile flow as a benchmark, we demonstrate favorable accuracy for low-order models of p = 1 and p = 2. The thrombin concentration in these models departs from the high-fidelity solution by under 20% (p = 1) and 2% (p = 2) after 20 cardiac cycles. These multi-fidelity models could enable new coagulation analyses in complex flow scenarios and extensive reaction networks. Furthermore, it could be generalized to advance our understanding of other reacting systems affected by flow.

Prognostic implications of systolic function in patients with cirrhosis.

INTRODUCTION: LV intrinsic systolic cardiac function in cirrhotic patients is conditioned by the degree of sympathetic activation and the use of non-selective beta-blockers (NSBBs). Systolic function can be non-invasively measured by ultrasound using Ejection Intraventricular Pressure Differences in the LV (EIVPD). We aimed to address the relationship between systolic function and long-term clinical outcomes using EIVPD. METHODS: We studied 45 Child-Pugh B or C patients (13 female, 24 on NSBBs) using echocardiography. The primary endpoint was the combination of any-cause mortality or liver transplantation. After a follow-up of 7 years (796 person-months) and a median period of 17 (10-42) months, 41 patients (91%) reached the primary endpoint: 13 (29%) died and 28 (62%) underwent transplantation. RESULTS: By univariable analysis the primary endpoint was related exclusively to MELD score. However, in a multivariable proportional-hazards analysis, adjusted for age, sex and MELD score, EIVPD was inversely related to the primary endpoint, showing interaction with NSBBs. In patients without NSBBs, EIVPD inversely predicted the primary endpoint, whereas in patients with NSBBs, EIVPD was unrelated to outcomes. These relationships were undetected by myocardial strain or conventional cardiac indices. CONCLUSIONS: LV intrinsic systolic function, as noninvasively measured by EIVPD is a predictor of long-term outcomes in patients with cirrhosis. The prognostic value of EIVPD is present along any degree of liver dysfunction but blunted by NSBBs. Because NSBBs have a deep effect on myocardial contractility, these drugs need to be considered when assessing the prognostic implications of cardiac function in these patients.

The natural matching of harmonic responses in the pulmonary circulation.

KEY POINTS: The right ventricle of the mammal heart is highly sensitive to the afterload imposed by a combination of the pulmonary circulation and the retrograde contribution of the left heart. Right ventricular afterload can be analysed in terms of pulmonary artery input impedance, which we were able to decompose as the result of the harmonic frequency responses of the pulmonary vessels and the left heart attached in series. Using spectral methods, we found a natural matching between the pulmonary vasculature and the left chambers of the heart. This coupling implies that the upstream transmission of the left heart frequency-response has favourable effects on the pulmonary tree. This physiological mechanism protects the right ventricle against acute changes in preload, and its impairment may be a relevant contribution to right ventricle dysfunction in pulmonary hypertension. ABSTRACT: The right ventricle (RV) of the mammal heart is highly sensitive to the afterload imposed by the pulmonary circulation, and the left heart (LH) retrogradely contributes significantly to this vascular load. Transmission-line theory anticipates that the degree of matching between the frequency responses of the pulmonary vasculature and the LH should modulate the global right haemodynamic burden. We measured simultaneous high-fidelity flow (pulmonary artery) and pressure (pulmonary artery and left atrium) in 18 healthy minipigs under acute haemodynamic interventions. From these data, we decomposed the impedance spectra of the total right-circulation system into the impedance of the pulmonary vessels and the harmonic response of the LH. For frequencies above the first harmonic, total impedance was below the pulmonary impedance during all phases (P < 0.001; pooled phases), demonstrating a favourable effect of the LH harmonic response on RV pulsatile load: the LH harmonic response was responsible for a 20% reduction of pulse pulmonary artery pressure (P < 0.001 vs. a theoretical purely-resistive response) and a 15% increase of pulmonary compliance (P = 0.009). This effect on compliance was highest during acute volume overload. In the normal right circulation, the longitudinal impedance of the pulmonary vasculature is matched to the harmonic response of the LH in a way that efficiently reduces the pulmonary pulsatile vascular load. This source of interaction between the right and left circulations of mammals protects the RV against excessive afterload during acute volume transients and its disruption may be an important contributor to pulmonary hypertension.

The Biological Bases of Group 2 Pulmonary Hypertension.

Pulmonary hypertension (PH) is a potentially fatal condition with a prevalence of around 1% in the world population and most commonly caused by left heart disease (PH-LHD). Usually, in PH-LHD, the increase of pulmonary pressure is only conditioned by the retrograde transmission of the left atrial pressure. However, in some cases, the long-term retrograde pressure overload may trigger complex and irreversible biomechanical and biological changes in the pulmonary vasculature. This latter clinical entity, designated as combined pre- and post-capillary PH, is associated with very poor outcomes. The underlying mechanisms of this progression are poorly understood, and most of the current knowledge comes from the field of Group 1-PAH. Treatment is also an unsolved issue in patients with PH-LHD. Targeting the molecular pathways that regulate pulmonary hemodynamics and vascular remodeling has provided excellent results in other forms of PH but has a neutral or detrimental result in patients with PH-LHD. Therefore, a deep and comprehensive biological characterization of PH-LHD is essential to improve the diagnostic and prognostic evaluation of patients and, eventually, identify new therapeutic targets. Ongoing research is aimed at identify candidate genes, variants, non-coding RNAs, and other biomarkers with potential diagnostic and therapeutic implications. In this review, we discuss the state-of-the-art cellular, molecular, genetic, and epigenetic mechanisms potentially involved in PH-LHD. Signaling and effective pathways are particularly emphasized, as well as the current knowledge on -omic biomarkers. Our final aim is to provide readers with the biological foundations on which to ground both clinical and pre-clinical research in the field of PH-LHD.

Intraventricular vortex properties in nonischemic dilated cardiomyopathy.

Vortices may have a role in optimizing the mechanical efficiency and blood mixing of the left ventricle (LV). We aimed to characterize the size, position, circulation, and kinetic energy (KE) of LV main vortex cores in patients with nonischemic dilated cardiomyopathy (NIDCM) and analyze their physiological correlates. We used digital processing of color-Doppler images to study flow evolution in 61 patients with NIDCM and 61 age-matched control subjects. Vortex features showed a characteristic biphasic temporal course during diastole. Because late filling contributed significantly to flow entrainment, vortex KE reached its maximum at the time of the peak A wave, storing 26 ± 20% of total KE delivered by inflow (range: 1-74%). Patients with NIDCM showed larger and stronger vortices than control subjects (circulation: 0.008 ± 0.007 vs. 0.006 ± 0.005 m(2)/s, respectively, P = 0.02; KE: 7 ± 8 vs. 5 ± 5 mJ/m, P = 0.04), even when corrected for LV size. This helped confining the filling jet in the dilated ventricle. The vortex Reynolds number was also higher in the NIDCM group. By multivariate analysis, vortex KE was related to the KE generated by inflow and to chamber short-axis diameter. In 21 patients studied head to head, Doppler measurements of circulation and KE closely correlated with phase-contract magnetic resonance values (intraclass correlation coefficient = 0.82 and 0.76, respectively). Thus, the biphasic nature of filling determines normal vortex physiology. Vortex formation is exaggerated in patients with NIDCM due to chamber remodeling, and enlarged vortices are helpful for ameliorating convective pressure losses and facilitating transport. These findings can be accurately studied using ultrasound.

Machine Learning Prediction for Prognosis of Patients With Aortic Stenosis.

Background: Aortic valve stenosis of any degree is associated with poor outcomes. Objectives: The authors aimed to develop a risk prediction model for aortic stenosis (AS) prognosis using machine learning techniques. Methods: A prognostic algorithm was developed using an AS registry of 10,407 patients undergoing echocardiography between 2008 and 2020. Clinical, echocardiographic, laboratory, and medication data were used to train and test a time-to-event model, the random survival forest (RSF), for AS patient's prognosis. The composite outcome included aortic valve replacement or mortality. The SHapley Additive exPlanations method attributed the importance of variables and provided personalized risk assessment. The algorithm was validated in 2 external cohorts of 11,738 and 954 patients with AS. Results: The median follow-up of the primary cohort was 48 (21-87) months. In this period, 1,116 patients underwent aortic valve replacement, and 5,069 patients died. RSF had an area under the curve (AUC) of 0.83 (95% CI: 0.80-0.86) and 0.83 (95% CI: 0.81-0.84) for outcomes prediction at 1 and 5 years, respectively. Using a cut-off of 50%, the RSF sensitivity and specificity for the composite outcome, were 0.80 and 0.73, respectively. Validation performance in the 2 external cohorts was similar, with AUCs of 0.73 (95% CI: 0.72-0.74) and 0.74 (95% CI: 0.72-0.76), respectively. AS severity, age, serum albumin, pulmonary artery pressure, and chronic kidney disease emerged as the top significant variables in the model. Conclusions: In patients with AS, a machine learning algorithm predicts outcomes with good accuracy, and prognostic characteristics were identified. The model can potentially guide risk factor modification and clinical decisions to improve patient prognosis.

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. This limitation has rendered VFM unable to combine information from different time frames in an acquisition sequence or derive fluctuating pressure maps. In this study, we leveraged recent advances in artificial intelligence (AI) to develop AI-VFM, a vector flow mapping modality that uses physics-informed neural networks (PINNs) encoding mass conservation and momentum balance inside the LV, and no-slip boundary conditions at the LV endocardium. AI-VFM recovers the flow and pressure fields in the LV from standard echocardiographic scans. It performs phase unwrapping and recovers flow data in areas without input color-Doppler data. AI-VFM also recovers complete flow maps at time points without color-Doppler input data, producing super-resolution flow maps. We show that informing the PINNs with momentum balance is essential to achieving temporal super-resolution and significantly increases the accuracy of AI-VFM compared to informing the PINNs only with mass conservation. AI-VFM is solely informed by each patient's flow physics; it does not utilize explicit smoothness constraints or incorporate data from other patients or flow models. AI-VFM takes 15 minutes to run in off-the-shelf graphics processing units and its underlying PINN framework could be extended to map other flow-associated metrics like blood residence time or the concentration of coagulation species.

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. METHODS: We analyzed 2D color-Doppler echocardiograms of 81 non-ischemic dilated cardiomyopathy (DCM) patients, 51 hypertrophic cardiomyopathy (HCM) patients, and 77 normal volunteers (Control). We applied proper orthogonal decomposition (POD) to build patient-specific and cohort-specific ROMs of LV flow. Each ROM aggregates a low number of components representing a spatially dependent velocity map modulated along the cardiac cycle by a time-dependent coefficient. We tested three classifiers using deliberately simple ML analyses of these ROMs with varying supervision levels. In supervised models, hyperparameter grid search was used to derive the ROMs that maximize classification power. The classifiers were blinded to LV chamber geometry and function. We ran vector flow mapping on the color-Doppler sequences to help visualize flow patterns and interpret the ML results. RESULTS: POD-based ROMs stably represented each cohort through 10-fold cross-validation. The principal POD mode captured >80 % of the flow kinetic energy (KE) in all cohorts and represented the LV filling/emptying jets. Mode 2 represented the diastolic vortex and its KE contribution ranged from <1 % (HCM) to 13 % (DCM). Semi-unsupervised classification using patient-specific ROMs revealed that the KE ratio of these two principal modes, the vortex-to-jet (V2J) energy ratio, is a simple, interpretable metric that discriminates DCM, HCM, and Control patients. Receiver operating characteristic curves using V2J as classifier had areas under the curve of 0.81, 0.91, and 0.95 for distinguishing HCM vs. Control, DCM vs. Control, and DCM vs. HCM, respectively. CONCLUSIONS: Modal decomposition of cardiac flow can be used to create ROMs of normal and pathological flow patterns, uncovering simple interpretable flow metrics with power to discriminate disease states, and particularly suitable for further processing using ML.

Multi-physics simulations reveal hemodynamic impacts of patient-derived fibrosis-related changes in left atrial tissue mechanics.

Stroke is a leading cause of death and disability worldwide. Atrial myopathy, including fibrosis, is associated with an increased risk of ischemic stroke, but the mechanisms underlying this association are poorly understood. Fibrosis modifies myocardial structure, impairing electrical propagation and tissue biomechanics, and creating stagnant flow regions where clots could form. Fibrosis can be mapped non-invasively using late gadolinium enhancement magnetic resonance imaging (LGE-MRI). However, fibrosis maps are not currently incorporated into stroke risk calculations or computational electro-mechano-fluidic models. We present multi-physics simulations of left atrial (LA) myocardial motion and hemodynamics using patient-specific anatomies and fibrotic maps from LGE-MRI. We modify tissue stiffness and active tension generation in fibrotic regions and investigate how these changes affect LA flow for different fibrotic burdens. We find that fibrotic regions and, to a lesser extent, non-fibrotic regions experience reduced myocardial strain, resulting in decreased LA emptying fraction consistent with clinical observations. Both fibrotic tissue stiffening and hypocontractility independently reduce LA function, but together, these two alterations cause more pronounced effects than either one alone. Fibrosis significantly alters flow patterns throughout the atrial chamber, and particularly, the filling and emptying jets of the left atrial appendage (LAA). The effects of fibrosis in LA flow are largely captured by the concomitant changes in LA emptying fraction except inside the LAA, where a multi-factorial behavior is observed. This work illustrates how high-fidelity, multi-physics models can be used to study thrombogenesis mechanisms in patient-specific anatomies, shedding light onto the links between atrial fibrosis and ischemic stroke. Key points: Left atrial (LA) fibrosis is associated with arrhythmogenesis and increased risk of ischemic stroke; its extent and pattern can be quantified on a patient-specific basis using late gadolinium enhancement magnetic resonance imaging.Current stroke risk prediction tools have limited personalization, and their accuracy could be improved by incorporating patient-specific information like fibrotic maps and hemodynamic patterns.We present the first electro-mechano-fluidic multi-physics computational simulations of LA flow, including fibrosis and anatomies from medical imaging. Mechanical changes in fibrotic tissue impair global LA motion, decreasing LA and left atrial appendage (LAA) emptying fractions, especially in subjects with higher fibrosis burdens. Fibrotic-mediated LA motion impairment alters LA and LAA flow near the endocardium and the whole cavity, ultimately leading to more stagnant blood regions in the LAA.

One-year longitudinal changes of peripheral CD4+ T-lymphocyte counts, gut microbiome, and plaque vulnerability after an acute coronary syndrome.

Background: Longitudinal changes in gut microbiome and inflammation may be involved in the evolution of atherosclerosis after an acute coronary syndrome (ACS). We aimed to characterize repeated profiles of gut microbiota and peripheral CD4+ T lymphocytes during the first year after an ACS, and to address their relationship with atherosclerotic plaque changes. Methods: Over one year we measured the microbiome, peripheral counts of CD4+ T populations and cytokines in 67 patients shortly after a first ACS. We compared baseline measurements to those of a matched population of 40 chronic patients. A subgroup of 20 ACS patients underwent repeated assessment of fibrous cap thickness (FCT) of a non-culprit lesion. Results: At admission, ACS patients showed gut dysbiosis compared with the chronic group, which was rapidly reduced and remained low at 1-year. Also, their Th1 and Th2 CD4+ T counts were increased but decreased over time. The CD4+ T counts were related to ongoing changes in gut microbiome. Unsupervised clustering of repeated CD4+ Th0, Th1, Th2, Th17 and Treg counts in ACS patients identified two different cell trajectory patterns, related to cytokines. The group of patients following a high-CD4+ T cell trajectory showed a one-year reduction in their FCT [net effect = -24.2 µm; p = 0.016]. Conclusions: Patients suffering an ACS show altered profiles of microbiome and systemic inflammation that tend to mimic values of chronic patients after 1-year. However, in one-third of patients, this inflammatory state remains particularly dysregulated. This persistent inflammation is likely related to plaque vulnerability as evident by fibrous cap thinning (Clinical Trial NCT03434483).

Stasis imaging predicts the risk of cardioembolic events related to acute myocardial infarction: the ISBITAMI study.

INTRODUCTION AND OBJECTIVES: In the setting of ST-segment elevation myocardial infarction (STEMI), imaging-based biomarkers could be useful for guiding oral anticoagulation to prevent cardioembolism. Our objective was to test the efficacy of intraventricular blood stasis imaging for predicting a composite primary endpoint of cardioembolic risk during the first 6 months after STEMI. METHODS: We designed a prospective clinical study, Imaging Silent Brain Infarct in Acute Myocardial Infarction (ISBITAMI), including patients with a first STEMI, an ejection fraction ≤ 45% and without atrial fibrillation to assess the performance of stasis metrics to predict cardioembolism. Patients underwent ultrasound-based stasis imaging at enrollment followed by heart and brain magnetic resonance at 1-week and 6-month visits. From the stasis maps, we calculated the average residence time, RT, of blood inside the left ventricle and assessed its performance to predict the primary endpoint. The longitudinal strain of the 4 apical segments was quantified by speckle tracking. RESULTS: A total of 66 patients were assigned to the primary endpoint. Of them, 17 patients had 1 or more events: 3 strokes, 5 silent brain infarctions, and 13 mural thromboses. No systemic embolisms were observed. RT (OR, 3.73; 95%CI, 1.75-7.9; P<.001) and apical strain (OR, 1.47; 95%CI, 1.13-1.92; P=.004) showed complementary prognostic value. The bivariate model showed a c-index=0.86 (95%CI, 0.73-0.95), a negative predictive value of 1.00 (95%CI, 0.94-1.00), and positive predictive value of 0.45 (95%CI, 0.37-0.77). The results were confirmed in a multiple imputation sensitivity analysis. Conventional ultrasound-based metrics were of limited predictive value. CONCLUSIONS: In patients with STEMI and left ventricular systolic dysfunction in sinus rhythm, the risk of cardioembolism may be assessed by echocardiography by combining stasis and strain imaging. Registered at ClinicalTrials.gov (NCT02917213).

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. Methods: We analyzed 2D color-Doppler echocardiograms of 81 non-ischemic dilated cardiomyopathy (DCM) patients, 51 hypertrophic cardiomyopathy (HCM) patients, and 77 normal volunteers (Control). We applied proper orthogonal decomposition (POD) to build patient-specific and cohort-specific ROMs of LV flow. Each ROM aggregates a low number of components representing a spatially dependent velocity map modulated along the cardiac cycle by a time-dependent coefficient. We tested three classifiers using deliberately simple ML analyses of these ROMs with varying supervision levels. In supervised models, hyperparameter gridsearch was used to derive the ROMs that maximize classification power. The classifiers were blinded to LV chamber geometry and function. We ran vector flow mapping on the color-Doppler sequences to help visualize flow patterns and interpret the ML results. Results: POD-based ROMs stably represented each cohort through 10-fold cross-validation. The principal POD mode captured >80% of the flow kinetic energy (KE) in all cohorts and represented the LV filling/emptying jets. Mode 2 represented the diastolic vortex and its KE contribution ranged from <1% (HCM) to 13% (DCM). Semi-unsupervised classification using patient-specific ROMs revealed that the KE ratio of these two principal modes, the vortex-to-jet (V2J) energy ratio, is a simple, interpretable metric that discriminates DCM, HCM, and Control patients. Receiver operating characteristic curves using V2J as classifier had areas under the curve of 0.81, 0.91, and 0.95 for distinguishing HCM vs. Control, DCM vs. Control, and DCM vs. HCM, respectively. Conclusions: Modal decomposition of cardiac flow can be used to create ROMs of normal and pathological flow patterns, uncovering simple interpretable flow metrics with power to discriminate disease states, and particularly suitable for further processing using ML.

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. Solving these PDE systems computationally is challenging, due to their large size and multi-scale nature. We propose a multi-fidelity strategy to increase the efficiency of coagulation cascade simulations. Leveraging the slower dynamics of molecular diffusion, we transform the governing PDEs into ordinary differential equations (ODEs) representing the evolution of species concentrations versus blood residence time. We then Taylor-expand the ODE solution around the zero-diffusivity limit to obtain spatiotemporal maps of species concentrations in terms of the statistical moments of residence time, , and provide the governing PDEs for . This strategy replaces a high-fidelity system of N PDEs representing the coagulation cascade of N chemical species by N ODEs and p PDEs governing the residence time statistical moments. The multi-fidelity order( p ) allows balancing accuracy and computational cost, providing a speedup of over N/p compared to high-fidelity models. Using a simplified coagulation network and an idealized aneurysm geometry with a pulsatile flow as a benchmark, we demonstrate favorable accuracy for low-order models of p = 1 and p = 2. These models depart from the high-fidelity solution by under 16% ( p = 1) and 5% ( p = 2) after 20 cardiac cycles. The favorable accuracy and low computational cost of multi-fidelity models could enable unprecedented coagulation analyses in complex flow scenarios and extensive reaction networks. Furthermore, it can be generalized to advance our understanding of other systems biology networks affected by blood flow.

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. In this study, we investigated how changes in PV flow split affect LA flow transport, focusing for the first time on blood stasis in the LAA, using a high-fidelity patient-specific computational fluid dynamics (CFD) model. We use an Immersed Boundary Method, simulating the flow in a fixed, uniform Cartesian mesh and imposing the movement of the LA walls with a moving Lagrangian mesh generated from 4D Computerized Tomography images. We analyzed LA anatomies from eight patients with varying atrial function, including three with AF and either a LAA thrombus or a history of Transient Ischemic Attacks (TIAs). Using four different flow splits (60/40% and 55/45% through right and left PVs, even flow rate, and same velocity through each PV), we found that flow patterns are sensitive to PV flow split variations, particularly in planes parallel to the mitral valve. Changes in PV flow split also had a significant impact on blood stasis and could contribute to increased risk for thrombosis inside the LAA, particularly in patients with AF and previous LAA thrombus or a history of TIAs. Our study highlights the importance of considering patient-specific PV flow split variations when assessing LA hemodynamics and identifying patients at increased risk for thrombosis and stroke. This knowledge is relevant to planning clinical procedures such as AF ablation or the implementation of LAA occluders.

Machine Learning to Optimize the Echocardiographic Follow-Up of Aortic Stenosis.

BACKGROUND: Disease progression in patients with mild-to-moderate aortic stenosis is heterogenous and requires periodic echocardiographic examinations to evaluate severity. OBJECTIVES: This study sought to explore the use of machine learning to optimize aortic stenosis echocardiographic surveillance automatically. METHODS: The study investigators trained, validated, and externally applied a machine learning model to predict whether a patient with mild-to-moderate aortic stenosis will develop severe valvular disease at 1, 2, or 3 years. Demographic and echocardiographic patient data to develop the model were obtained from a tertiary hospital consisting of 4,633 echocardiograms from 1,638 consecutive patients. The external cohort was obtained from an independent tertiary hospital, consisting of 4,531 echocardiograms from 1,533 patients. Echocardiographic surveillance timing results were compared with the European and American guidelines echocardiographic follow-up recommendations. RESULTS: In internal validation, the model discriminated severe from nonsevere aortic stenosis development with an area under the receiver-operating characteristic curve (AUC-ROC) of 0.90, 0.92, and 0.92 for the 1-, 2-, or 3-year interval, respectively. In external application, the model showed an AUC-ROC of 0.85, 0.85, and 0.85, for the 1-, 2-, or 3-year interval. A simulated application of the model in the external validation cohort resulted in savings of 49% and 13% of unnecessary echocardiographic examinations per year compared with European and American guideline recommendations, respectively. CONCLUSIONS: Machine learning provides real-time, automated, personalized timing of next echocardiographic follow-up examination for patients with mild-to-moderate aortic stenosis. Compared with European and American guidelines, the model reduces the number of patient examinations.

Increased Chamber Resting Tone Is a Key Determinant of Left Ventricular Diastolic Dysfunction.

BACKGROUND: Twitch-independent tension has been demonstrated in cardiomyocytes, but its role in heart failure (HF) is unclear. We aimed to address twitch-independent tension as a source of diastolic dysfunction by isolating the effects of chamber resting tone (RT) from impaired relaxation and stiffness. METHODS: We invasively monitored pressure-volume data during cardiopulmonary exercise in 20 patients with hypertrophic cardiomyopathy, 17 control subjects, and 35 patients with HF with preserved ejection fraction. To measure RT, we developed a new method to fit continuous pressure-volume measurements, and first validated it in a computational model of loss of cMyBP-C (myosin binding protein-C). RESULTS: In hypertrophic cardiomyopathy, RT (estimated marginal mean [95% CI]) was 3.4 (0.4-6.4) mm Hg, increasing to 18.5 (15.5-21.5) mm Hg with exercise (P<0.001). At peak exercise, RT was responsible for 64% (53%-76%) of end-diastolic pressure, whereas incomplete relaxation and stiffness accounted for the rest. RT correlated with the levels of NT-proBNP (N-terminal pro-B-type natriuretic peptide; R=0.57; P=0.02) and with pulmonary wedge pressure but following different slopes at rest and during exercise (R2=0.49; P<0.001). In controls, RT was 0.0 mm Hg and 1.2 (0.3-2.8) mm Hg in HF with preserved ejection fraction patients and was also exacerbated by exercise. In silico, RT increased in parallel to the loss of cMyBP-C function and correlated with twitch-independent myofilament tension (R=0.997). CONCLUSIONS: Augmented RT is the major cause of LV diastolic chamber dysfunction in hypertrophic cardiomyopathy and HF with preserved ejection fraction. RT transients determine diastolic pressures, pulmonary pressures, and functional capacity to a greater extent than relaxation and stiffness abnormalities. These findings support antimyosin agents for treating HF.

Assessment of Blood Flow Transport in the Left Ventricle Using Ultrasound. Validation Against 4-D Flow Cardiac Magnetic Resonance.

Four-dimensional flow cardiac magnetic resonance (CMR) is the reference technique for analyzing blood transport in the left ventricle (LV), but similar information can be obtained from ultrasound. We aimed to validate ultrasound-derived transport in a head-to-head comparison against 4D flow CMR. In five patients and two healthy volunteers, we obtained 2D + t and 3D + t (4D) flow fields in the LV using transthoracic echocardiography and CMR, respectively. We compartmentalized intraventricular blood flow into four fractions of end-diastolic volume: direct flow (DF), retained inflow (RI), delayed ejection flow (DEF) and residual volume (RV). Using ultrasound we also computed the properties of LV filling waves (percentage of LV penetration and percentage of LV volume carried by E/A waves) to determine their relationships with CMR transport. Agreement between both techniques for quantifying transport fractions was good for DF and RV (Ric [95% confidence interval]: 0.82 [0.33, 0.97] and 0.85 [0.41, 0.97], respectively) and moderate for RI and DEF (Ric= 0.47 [-0.29, 0.88] and 0.55 [-0.20, 0.90], respectively). Agreement between techniques to measure kinetic energy was variable. The amount of blood carried by the E-wave correlated with DF and RV (R = 0.75 and R = 0.63, respectively). Therefore, ultrasound is a suitable method for expanding the analysis of intraventricular flow transport in the clinical setting.

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. To evaluate these non-Newtonian effects, we built atrial models derived from 4D computed tomography scans of patients and carried out computational fluid dynamics simulations using the Carreau-Yasuda constitutive relation. We examined six patients, three of whom had AF and LAA thrombosis or a history of transient ischemic attacks (TIAs). We modeled the effects of hematocrit and rouleaux formation kinetics by varying the parameterization of the Carreau-Yasuda relation and modulating non-Newtonian viscosity changes based on residence time. Comparing non-Newtonian and Newtonian simulations indicates that slow flow in the LAA increases blood viscosity, altering secondary swirling flows and intensifying blood stasis. While some of these effects are subtle when examined using instantaneous metrics like shear rate or kinetic energy, they are manifested in the blood residence time, which accumulates over multiple heartbeats. Our data also reveal that LAA blood stasis worsens when hematocrit increases, offering a potential new mechanism for the clinically reported correlation between hematocrit and stroke incidence. In summary, we submit that hematocrit-dependent non-Newtonian blood rheology should be considered when calculating patient-specific blood stasis indices by computational fluid dynamics.

A comparison of the clinical efficacy of echocardiography and magnetic resonance for chronic aortic regurgitation.

AIMS: Timing surgery in chronic aortic regurgitation (AR) relies mostly on echocardiography. However, cardiac magnetic resonance (CMR) may be more accurate for quantifying regurgitation and left ventricular (LV) remodelling. We aimed to compare the technical and clinical efficacies of echocardiography and CMR to account for the severity of the disease, the degree of LV remodelling, and predict AR-related outcomes. METHODS AND RESULTS: We studied 263 consecutive patients with isolated AR undergoing echocardiography and CMR. After a median follow-up of 33 months, 76 out of 197 initially asymptomatic patients reached the primary endpoint of AR-related events: 6 patients (3%) were admitted for heart failure, and 70 (36%) underwent surgery. Adjusted survival models based on CMR improved the predictions of the primary endpoint based on echocardiography: R2 = 0.37 vs. 0.22, χ2 = 97 vs. 49 (P < 0.0001), and C-index = 0.80 vs. 0.70 (P < 0.001). This resulted in a net classification index of 0.23 (0.00-0.46, P = 0.046) and an integrated discrimination improvement of 0.12 (95% confidence interval 0.08-0.58, P = 0.02). CMR-derived regurgitant fraction (<28, 28-37, or >37%) and LV end-diastolic volume (<83, 183-236, or >236 mL) adequately stratified patients with normal EF. The agreement between techniques for grading AR severity and assessing LV dilatation was poor, and CMR showed better reproducibility. CONCLUSIONS: CMR improves the clinical efficacy of ultrasound for predicting outcomes of patients with AR. This is due to its better reproducibility and accuracy for grading the severity of the disease and its impact on the LV. Regurgitant fraction, LV ejection fraction, and end-diastolic volume obtained by CMR most adequately predict AR-related events.