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.

Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and advanced imaging.

Genome sequencing has established clinical utility for rare disease diagnosis. While increasing numbers of individuals have undergone elective genome sequencing, a comprehensive study surveying genome-wide disease-associated genes in adults with deep phenotyping has not been reported. Here we report the results of a 3-y precision medicine study with a goal to integrate whole-genome sequencing with deep phenotyping. A cohort of 1,190 adult participants (402 female [33.8%]; mean age, 54 y [range 20 to 89+]; 70.6% European) had whole-genome sequencing, and were deeply phenotyped using metabolomics, advanced imaging, and clinical laboratory tests in addition to family/medical history. Of 1,190 adults, 206 (17.3%) had at least 1 genetic variant with pathogenic (P) or likely pathogenic (LP) assessment that suggests a predisposition of genetic risk. A multidisciplinary clinical team reviewed all reportable findings for the assessment of genotype and phenotype associations, and 137 (11.5%) had genotype and phenotype associations. A high percentage of genotype and phenotype associations (>75%) was observed for dyslipidemia (n = 24), cardiomyopathy, arrhythmia, and other cardiac diseases (n = 42), and diabetes and endocrine diseases (n = 17). A lack of genotype and phenotype associations, a potential burden for patient care, was observed in 69 (5.8%) individuals with P/LP variants. Genomics and metabolomics associations identified 61 (5.1%) heterozygotes with phenotype manifestations affecting serum metabolite levels in amino acid, lipid and cofactor, and vitamin pathways. Our descriptive analysis provides results on the integration of whole-genome sequencing and deep phenotyping for clinical assessments in adults.

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. However, the underlying mechanisms of clot formation are not yet fully understood. Here we present a new model incorporating data from patient-specific simulated velocity fields to track platelet activation and accumulation. We use a system of Reaction-Advection-Diffusion equations solved with a stabilized finite element method to describe the evolution of non-activated platelets and activated platelet concentrations [AP], local concentrations of adenosine diphosphate (ADP) and poly-phosphate (PolyP). The activation of platelets is modeled as a function of shear-rate exposure and local concentration of agonists. We compared the distribution of activated platelets in a healthy coronary case and six cases with coronary artery aneurysms caused by KD, including three with confirmed thrombosis. Results show spatial correlation between regions of higher concentration of activated platelets and the reported location of the clot, suggesting predictive capabilities of this model towards identifying regions at high risk for thrombosis. Also, the concentration levels of ADP and PolyP in cases with confirmed thrombosis are higher than the reported critical values associated with platelet aggregation (ADP) and activation of the intrinsic coagulation pathway (PolyP). These findings suggest the potential initiation of a coagulation pathway even in the absence of an extrinsic factor. Finally, computational simulations show that in regions of flow stagnation, biochemical activation, as a result of local agonist concentration, is dominant. Identifying the leading factors to a pro-coagulant environment in each case-mechanical or biochemical-could help define improved strategies for thrombosis prevention tailored for each patient.

Precision medicine screening using whole-genome sequencing and advanced imaging to identify disease risk in adults.

Reducing premature mortality associated with age-related chronic diseases, such as cancer and cardiovascular disease, is an urgent priority. We report early results using genomics in combination with advanced imaging and other clinical testing to proactively screen for age-related chronic disease risk among adults. We enrolled active, symptom-free adults in a study of screening for age-related chronic diseases associated with premature mortality. In addition to personal and family medical history and other clinical testing, we obtained whole-genome sequencing (WGS), noncontrast whole-body MRI, dual-energy X-ray absorptiometry (DXA), global metabolomics, a new blood test for prediabetes (Quantose IR), echocardiography (ECHO), ECG, and cardiac rhythm monitoring to identify age-related chronic disease risks. Precision medicine screening using WGS and advanced imaging along with other testing among active, symptom-free adults identified a broad set of complementary age-related chronic disease risks associated with premature mortality and strengthened WGS variant interpretation. This and other similarly designed screening approaches anchored by WGS and advanced imaging may have the potential to extend healthy life among active adults through improved prevention and early detection of age-related chronic diseases (and their risk factors) associated with premature mortality.

Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics.

Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. We propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo (MLMF) estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models (zero- and one-dimensional) to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these two low-fidelity model alternatives and our high-fidelity three-dimensional models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. Our goal is to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to single fidelity Monte Carlo estimators and to a multilevel Monte Carlo approach based only on three-dimensional simulations, but leveraging multiple spatial resolutions. We demonstrate significant, on the order of 10 to 100 times, reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities such as outlet pressure and flow show larger reductions than local quantities, such as those relating to wall shear stress, as the latter rely more heavily on the highest fidelity model evaluations. Similarly, healthy models show larger reductions than diseased models. In all cases, our workflow coupling Dakota's MLMF estimators with the SimVascular cardiovascular modeling framework makes uncertainty quantification feasible for constrained computational budgets.

Uncertainty quantification of simulated biomechanical stimuli in coronary artery bypass grafts.

Coronary artery bypass graft surgery (CABG) is performed on more than 400,000 patients annually in the U.S. However, saphenous vein grafts (SVGs) implanted during CABG exhibit poor patency compared to arterial grafts, with failure rates up to 40% within 10 years after surgery. Differences in mechanical stimuli are known to play a role in driving maladaptation and have been correlated with endothelial damage and thrombus formation. As these quantities are difficult to measure in vivo, multi-scale coronary models offer a way to quantify them, while accounting for complex coronary physiology. However, prior studies have primarily focused on deterministic evaluations, without reporting variability in the model parameters due to uncertainty. This study aims to assess confidence in multi-scale predictions of wall shear stress and wall strain while accounting for uncertainty in peripheral hemodynamics and material properties. Boundary condition distributions are computed by assimilating uncertain clinical data, while spatial variations of vessel wall stiffness are obtained through approximation by a random field. We developed a stochastic submodeling approach to mitigate the computational burden of repeated multi-scale model evaluations to focus exclusively on the bypass grafts. This produces a two-level decomposition of quantities of interest into submodel contributions and full model/submodel discrepancies. We leverage these two levels in the context of forward uncertainty propagation using a previously proposed multi-resolution approach. The time- and space-averaged wall shear stress is well estimated with a coefficient of variation of <35%, but ignorance about the spatial distribution on the wall elastic modulus and thickness lead to large variations in an objective measure of wall strain, with coefficients of variation up to 100%. Sensitivity analysis reveals how the interactions between the flow and material parameters contribute to output variability.

Automated Tuning for Parameter Identification and Uncertainty Quantification in Multi-scale Coronary Simulations.

Atherosclerotic coronary artery disease, which can result in coronary artery stenosis, acute coronary artery occlusion, and eventually myocardial infarction, is a major cause of morbidity and mortality worldwide. Non-invasive characterization of coronary blood flow is important to improve understanding, prevention, and treatment of this disease. Computational simulations can now produce clinically relevant hemodynamic quantities using only non-invasive measurements, combining detailed three dimensional fluid mechanics with physiological models in a multiscale framework. These models, however, require specification of numerous input parameters and are typically tuned manually without accounting for uncertainty in the clinical data, hindering their application to large clinical studies. We propose an automatic, Bayesian, approach to parameter estimation based on adaptive Markov chain Monte Carlo sampling that assimilates non-invasive quantities commonly acquired in routine clinical care, quantifies the uncertainty in the estimated parameters and computes the confidence in local predicted hemodynamic indicators.

Validation of stroke volume and cardiac output by electrical interrogation of the brachial artery in normals: assessment of strengths, limitations, and sources of error.

The goal of this study is to validate a new, continuous, noninvasive stroke volume (SV) method, known as transbrachial electrical bioimpedance velocimetry (TBEV). TBEV SV was compared to SV obtained by cardiac magnetic resonance imaging (cMRI) in normal humans devoid of clinically apparent heart disease. Thirty-two (32) volunteers were enrolled in the study. Each subject was evaluated by echocardiography to assure that no aortic or mitral valve disease was present. Subsequently, each subject underwent electrical interrogation of the brachial artery by means of a high frequency, low amplitude alternating current. A first TBEV SV estimate was obtained. Immediately after the initial TBEV study, subjects underwent cMRI, using steady-state precession imaging to obtain a volumetric estimate of SV. Following cMRI, the TBEV SV study was repeated. Comparing the cMRI-derived SV to that of TBEV, the two TBEV estimates were averaged and compared to the cMRI standard. CO was computed as the product of SV and heart rate. Statistical methods consisted of Bland-Altman and linear regression analysis. TBEV SV and CO estimates were obtained in 30 of the 32 subjects enrolled. Bland-Altman analysis of pre- and post-cMRI TBEV SV showed a mean bias of 2.87 % (2.05 mL), precision of 13.59% (11.99 mL) and 95% limits of agreement (LOA) of +29.51% (25.55 mL) and -23.77% (-21.45 mL). Regression analysis for pre- and post-cMRI TBEV SV values yielded y = 0.76x + 25.1 and r(2) = 0.71 (r = 0.84). Bland-Altman analysis comparing cMRI SV with averaged TBEV SV showed a mean bias of -1.56% (-1.53 mL), precision of 13.47% (12.84 mL), 95% LOA of +24.85% (+23.64 mL) and -27.97% (-26.7 mL) and percent error = 26.2 %. For correlation analysis, the regression equation was y = 0.82x + 19.1 and correlation coefficient r(2) = 0.61 (r = 0.78). Bland-Altman analysis of averaged pre- and post-cMRI TBEV CO versus cMRI CO yielded a mean bias of 5.01% (0.32 L min(-1)), precision of 12.85% (0.77 L min(-1)), 95% LOA of +30.20 % (+0.1.83 L min(-1)) and -20.7% (-1.19 L min(-1)) and percent error = 24.8%. Regression analysis yielded y = 0.92x + 0.78, correlation coefficient r(2) = 0.74 (r = 0.86). TBEV is a novel, noninvasive method, which provides satisfactory estimates of SV and CO in normal humans.

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.

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.

Diagnostic Performance of Coronary Angiography Derived Computational Fractional Flow Reserve.

BACKGROUND: Computational fluid dynamics can compute fractional flow reserve (FFR) accurately. However, existing models are limited by either the intravascular hemodynamic phenomarkers that can be captured or the fidelity of geometries that can be modeled. METHODS AND RESULTS: This study aimed to validate a new coronary angiography-based FFR framework, FFRHARVEY, and examine intravascular hemodynamics to identify new biomarkers that could augment FFR in discerning unrevascularized patients requiring intervention. A 2-center cohort was used to examine diagnostic performance of FFRHARVEY compared with reference wire-based FFR (FFRINVASIVE). Additional biomarkers, longitudinal vorticity, velocity, and wall shear stress, were evaluated for their ability to augment FFR and indicate major adverse cardiac events. A total of 160 patients with 166 lesions were investigated. FFRHARVEY was compared with FFRINVASIVE by investigators blinded to the invasive FFR results with a per-stenosis area under the curve of 0.91, positive predictive value of 90.2%, negative predictive value of 89.6%, sensitivity of 79.3%, and specificity of 95.4%. The percentage ofdiscrepancy for continuous values of FFR was 6.63%. We identified a hemodynamic phenomarker, longitudinal vorticity, as a metric indicative of major adverse cardiac events in unrevascularized gray-zone cases. CONCLUSIONS: FFRHARVEY had high performance (area under the curve: 0.91, positive predictive value: 90.2%, negative predictive value: 89.6%) compared with FFRINVASIVE. The proposed framework provides a robust and accurate way to compute a complete set of intravascular phenomarkers, in which longitudinal vorticity was specifically shown to differentiate vessels predisposed to major adverse cardiac events.

Prevalence of Kawasaki disease in young adults with suspected myocardial ischemia.

BACKGROUND: Up to 25% of patients with untreated Kawasaki disease (KD) and 5% of those treated with intravenous immunoglobulin will develop coronary artery aneurysms. Persistent aneurysms may remain silent until later in life when myocardial ischemia can occur. We sought to determine the prevalence of coronary artery aneurysms suggesting a history of KD among young adults undergoing coronary angiography for evaluation of possible myocardial ischemia. METHODS AND RESULTS: We reviewed the medical histories and coronary angiograms of all adults <40 years of age who underwent coronary angiography for evaluation of suspected myocardial ischemia at 4 San Diego hospitals from 2005 to 2009 (n=261). History of KD-compatible illness and cardiac risk factors were obtained by medical record review. Angiograms were independently reviewed for the presence, size, and location of aneurysms and coronary artery disease by 2 cardiologists blinded to the history. Patients were evaluated for number of risk factors, angiographic appearance of their coronary arteries, and known history of KD. Of the 261 young adults who underwent angiography, 16 had coronary aneurysms. After all clinical criteria were assessed, 5.0% had aneurysms definitely (n=4) or presumed (n=9) secondary to KD as the cause of their coronary disease. CONCLUSIONS: Coronary sequelae of KD are present in 5% of young adults evaluated by angiography for myocardial ischemia. Cardiologists should be aware of this special subset of patients who may benefit from medical and invasive management strategies that differ from the strategies used to treat atherosclerotic coronary artery disease.

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 (RSCT) 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 RSCT across one heartbeat in each segment. The data set was split evenly into training and validation groups. During training, interchangeability of RSCT thresholding with experts to detect WMA was assessed using the individual equivalence index (γ), and an optimal threshold of the peak RSCT (RSCT*) that achieved maximum agreement was identified. RSCT* was then validated using the validation group, and the effect of AHA segment-specific thresholds was evaluated. Agreement was assessed using κ statistics. Results: The optimal threshold, RSCT* of -0.19, when applied to all AHA segments, led to high agreement (agreement rate = 92.17%, κ = 0.82) and interchangeability with experts (γ = -2.58%). The same RSCT* also achieved high agreement in the validation group (agreement rate = 90.29%, κ = 0.76, γ = -0.38%). The use of AHA segment-specific thresholds (range: 0.16 to -0.23 across AHA segments) slightly improved agreement (1.79% increase). Conclusion: RSCT thresholding was interchangeable with expert visual analysis in detecting segmental WMA from 4D CT and may be used as an objective decision classifier.Keywords: CT, Left Ventricle, Regional Endocardial Shortening, Wall Motion Abnormality Supplemental material is available for this article. © RSNA, 2023.

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.

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.

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. Ischemic risk was evaluated in 153 coronary arteries from simulated fractional flow reserve (FFR), wall shear stress, and residence time. FFR correlated weakly with aneurysm [Formula: see text]-scores (correlation coefficient, [Formula: see text]) but correlated better with the ratio of maximum-to-minimum aneurysmal lumen diameter ([Formula: see text]). FFR dropped more rapidly distal to aneurysms, and this correlated more with the lumen diameter ratio ([Formula: see text]) than [Formula: see text]-score ([Formula: see text]). Wall shear stress correlated better with the diameter ratio ([Formula: see text]), while residence time correlated more with [Formula: see text]-score ([Formula: see text]). Overall, the maximum-to-minimum diameter ratio predicted ischemic risk better than [Formula: see text]-score. Although FFR immediately distal to aneurysms was nonsignificant, its rapid rate of decrease suggests elevated risk.

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. We performed virtual surgery on 3 patient-specific models by modifying the geometry vein grafts to reflect single, Y, and sequential surgical configurations with SVG diameters ranging from 2 mm to 5 mm. Our study demonstrates that the coronary artery runoffs are relatively insensitive to the choice of SVG revascularization geometry. We observe a 10% increase in runoff when the SVG diameter is changed from 2 mm to 5 mm. The wall shear stress of SVG increases dramatically when the diameter drops, following an inverse power scaling with diameter. For a fixed diameter, the average wall shear stress on the vein graft varies in ascending order as single, Y, and sequential graft in the patient cohort. The runoff to the target coronary arteries changes marginally due to the choice of graft configuration or diameter. The shear stress on the vein graft depends on both flow rate and diameter and follows an inverse power scaling consistent with a Poiseuille flow assumption. Given the similarity in runoff with different surgical configurations, choices of SVG geometries can be informed by propensity for graft failure using shear stress evaluations.

Deep learning automates detection of wall motion abnormalities via 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). Methods: One hundred clinical cineCT studies were evaluated by three experienced cardiac CT readers to identify whether each AHA segment had a WMA. Fifty cases were used for method development and an independent group of 50 were used for testing. A previously developed convolutional neural network was used to automatically segment the LV bloodpool and to define the 2, 3, and 4 CH long-axis imaging planes. LS was measured as the perimeter of the bloodpool for each long-axis plane. Two smoothing approaches were developed to avoid artifacts due to papillary muscle insertion and texture of the endocardial surface. The impact of the smoothing was evaluated by comparison of LS estimates to LV ejection fraction and the fractional area change of the corresponding view. Results: The automated, DL approach successfully analyzed 48/50 patients in the training cohort and 47/50 in the testing cohort. The optimal LS cutoff for identification of WMA was -21.8, -15.4, and -16.6% for the 2-, 3-, and 4-CH views in the training cohort. This led to correct labeling of 85, 85, and 83% of 2-, 3-, and 4-CH views, respectively, in the testing cohort. Per-study accuracy was 83% (84% sensitivity and 82% specificity). Smoothing significantly improved agreement between LS and fractional area change (R 2: 2 CH = 0.38 vs. 0.89 vs. 0.92). Conclusion: Automated LV blood pool segmentation and long-axis plane delineation via deep learning enables automatic LS assessment. LS values accurately identify regional wall motion abnormalities and may be used to complement standard visual assessments.

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. RESULTS: Patients with MU (n = 31) were younger (49 ± 10 vs 59 ± 16 years, p < 0.01) and more frequently male (55% vs 26%, p = 0.04) than controls (n = 23). There was no baseline difference in ejection fraction (EF) (median 66% [IQR 58,71%] vs 62% [56,69%], p = 0.33) or GLS (-13.0% [-16.3,-10.9%] vs -14.8% [-16.0,-11.3%], p = 0.40). At one-year follow-up, MU cessation (n = 15) was associated with improvement in GLS (absolute change -4.4% [-6.5,-1.7%], p < 0.01), while no absolute change was observed with continued MU (n = 16) (0.74% [-1.2,2.8%], p = 0.22) or controls without MU (-0.6% [-2.1,2.8%], p = 0.78). Of those with abnormal baseline GLS, normalization was observed in 46% with MU cessation, none with continued MU, and 5% of controls (p < 0.001). Among MU patients, improvement in GLS was associated with decreased HF admissions per year [HR 0.74 per 1% change in GLS, 95% CI 0.55,0.98, p = 0.04]. CONCLUSIONS: Patients with MU and HFpEF may have occult systolic dysfunction as demonstrated by abnormal GLS, and MU cessation at 1 year is associated with improvement in GLS and a reduction in risk of HF admissions.

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, 60.1% male) were retrospectively evaluated. Volume-rendering videos of the LV blood pool were generated from 6 different perspectives (i.e., six views corresponding to every 60-degree rotation around the LV long axis); resulting in 2058 unique videos. Ground-truth WMA classification for each video was performed by evaluating the extent of impaired regional shortening visible (measured in the original 4DCT data). DL classification of each video for the presence of WMA was performed by first extracting image features frame-by-frame using a pre-trained Inception network and then evaluating the set of features using a long short-term memory network. Data were split into 60% for 5-fold cross-validation and 40% for testing. Results: Volume rendering videos represent ~800-fold data compression of the 4DCT volumes. Per-video DL classification performance was high for both cross-validation (accuracy = 93.1%, sensitivity = 90.0% and specificity = 95.1%, κ: 0.86) and testing (90.9, 90.2, and 91.4% respectively, κ: 0.81). Per-study performance was also high (cross-validation: 93.7, 93.5, 93.8%, κ: 0.87; testing: 93.5, 91.9, 94.7%, κ: 0.87). By re-binning per-video results into the 6 regional views of the LV we showed DL was accurate (mean accuracy = 93.1 and 90.9% for cross-validation and testing cohort, respectively) for every region. DL classification strongly agreed (accuracy = 91.0%, κ: 0.81) with expert visual assessment. Conclusions: Dynamic volume rendering of the LV blood pool combined with DL classification can accurately detect regional WMA from cardiac CT.