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).

Patient-specific multi-physics simulations of fibrotic changes in left atrial tissue mechanics impact on hemodynamics.

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 a patient-specific manner, shedding light onto the link 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 improvedfib 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.

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.

Cardiac Stasis Imaging, Stroke and Silent Brain Infarcts in Patients with Non-Ischemic Dilated Cardiomyopathy.

Cardioembolic stroke is one of the most devastating complications of non-ischemic 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 endpoint 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 (RT) maps and its derived stasis indices. Of the 89 recruited patients, 18 showed a positive endpoint: 9 had a history stroke or TIA and 9 were diagnosed with SBIs in the brain imaging. Averaged RT, performed good to identify the primary endpoint (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 was 0.92 (0.85-1.00) with and 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.

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).

Atorvastatin Effect on Aortic Dilatation and Valvular Calcification Progression in Bicuspid Aortic Valve (BICATOR): A Randomized Clinical Trial.

BACKGROUND: Ascending aorta dilation and aortic valve degeneration are common complications in patients with bicuspid aortic valve. Several retrospective studies have suggested the benefit of statins in reducing these complications. This study aimed to determine whether atorvastatin treatment is effective in reducing the growth of aortic diameters in bicuspid aortic valve and if it slows the progression of valve calcification. METHODS: In a randomized clinical trial, 220 patients with bicuspid aortic valve (43 women; 46±13 years of age) were included and treated with either 20 mg of atorvastatin per day or placebo for 3 years. Inclusion criteria were ≥18 years of age, nonsevere valvular dysfunction, nonsevere valve calcification, and ascending aorta diameter ≤50 mm. Computed tomography and echocardiography studies were performed at baseline and after 3 years of treatment. RESULTS: During follow-up, 28 patients (12.7%) discontinued medical treatment (15 on atorvastatin and 13 taking placebo). Thus, 192 patients completed the 36 months of treatment. Low-density lipoprotein cholesterol levels decreased significantly in the atorvastatin group (median [interquartile range], -30 mg/dL [-51.65 to -1.75 mg/dL] versus 6 mg/dL [-4, 22.5 mg/dL]; P<0.001). The maximum ascending aorta diameter increased with no differences between groups: 0.65 mm (95% CI, 0.45-0.85) in the atorvastatin group and 0.74 mm (95% CI, 0.45-1.04) in the placebo group (P=0.613). Similarly, no significant differences were found for the progression of the aortic valve calcium score (P=0.167) or valvular dysfunction. CONCLUSIONS: Among patients with bicuspid aortic valve without severe valvular dysfunction, atorvastatin treatment was not effective in reducing the progression of ascending aorta dilation and aortic valve calcification during 3 years of treatment despite a significant reduction in low-density lipoprotein cholesterol levels. REGISTRATION: URL: https://www.clinicaltrialsregister.eu; Unique identifier: 2015-001808-57. URL: https://www.clinicaltrials.gov; Unique identifier: NCT02679261.

Sudden cardiac death triggered by minimal alcohol consumption in the context of novel PPA2 mutations in 2 unrelated families.

Biallelic variants in PPA2 gene cause a rare but lethal mitochondrial disorder. We describe the first four cases reported in Spain of PPA2 disease in two unrelated families. We have conducted a revision of the clinical history, necropsies, and postmortem genetic testing from probands, and clinical evaluation, genetic testing and blood transcript analysis in family members. All the cases harbored biallelic PPA2 variants in compound heterozygous status. Two brothers from family 1 suffered sudden death after a small first intake of alcohol in 2013 and 2022. The sister remains alive but affected with cardiomyopathy, extensive scar on cardiac imaging, and high sensitivity to alcohol intake. The three siblings carried PPA2 c.290A > G (p.Glu97Gly) novel missense variant and PPA2 c.513C > T (p.Cys171 = ) altering splicing site variant, both probably leading to mRNA degradation based on in-silico and transcript analyses. A teenager from family 2 suffered sudden death after a small intake of alcohol in 2018 and carried PPA2 c.683C > T (p.Pro228Leu) missense and PPA2 c.980_983del (p.Gln327fs) novel frameshift variant, both probably leading to abnormal protein structure. All cases were asymptomatic until adolescence. Furthermore, the sister in family 1 has survived as an asymptomatic adult. PPA2 disease can manifest as cardiac arrest in the young, especially after alcohol exposure. Our results show that PPA2 deficiency can be related to different pathogenicity mechanisms such as abnormal protein structure but also mRNA decay caused by synonymous or missense variants. Strict avoidance of alcohol consumption and early defibrillator implantation might prevent lethal arrhythmias in patients at risk.

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.

Impact of the Mediterranean Diet on the Gut Microbiome of a Well-Defined Cohort of Healthy Individuals.

A comprehensive understanding of gut microbiota in a clearly defined group of healthy individuals is essential when making meaningful comparisons with various diseases. The Mediterranean diet (MD), renowned for its potential health benefits, and the influence of adherence thereto on gut microbiota have become a focus of research. Our aim was to elucidate the impact of adherence to the MD on gut microbiota composition in a well-defined cohort. In this prospective study, healthy volunteers completed a questionnaire to provide demographic data, medical history, and dietary intake. Adherence was evaluated using the Med-DQI. The V4 region of the 16S rRNA gene was sequenced. Analysis of sequencing data and statistical analysis were performed using MOTHUR software and R. The study included 60 patients (51.7% females). Adherence correlated with alpha diversity, and higher values were recorded in good adherers. Good adherers had a higher abundance of Paraprevotella and Bacteroides (p < 0.001). Alpha diversity correlated inversely with fat intake and positively with non-starch polysaccharides (NSPs). Evenness correlated inversely with red meat intake and positively with NSPs. Predicted functional analysis highlighted metabolic pathway differences based on adherence to the MD. In conclusion, our study adds useful information on the relationship between the MD and the gut microbiome.

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.

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.

Microbiome profile and calprotectin levels as markers of risk of recurrent Clostridioides difficile infection.

Introduction: Clostridioides difficile infection (CDI) is the main cause of nosocomial diarrhoea in developed countries. Recurrent CDI (R-CDI), which affects 20%-30% of patients and significantly increases hospital stay and associated costs, is a key challenge. The main objective of this study was to explore the role of the microbiome and calprotectin levels as predictive biomarkers of R-CDI. Methods: We prospectively (2019-2021) included patients with a primary episode of CDI. Clinical data and faecal samples were collected. The microbiome was analysed by sequencing the hypervariable V4 region of the 16S rRNA gene on an Illumina Miseq platform. Results: We enrolled 200 patients with primary CDI, of whom 54 developed R-CDI and 146 did not. We analysed 200 primary samples and found that Fusobacterium increased in abundance, while Collinsella, Senegalimassilia, Prevotella and Ruminococcus decreased in patients with recurrent versus non-recurrent disease. Elevated calprotectin levels correlated significantly with R-CDI (p=0.01). We built a risk index for R-CDI, including as prognostic factors age, sex, immunosuppression, toxin B amplification cycle, creatinine levels and faecal calprotectin levels (overall accuracy of 79%). Discussion: Calprotectin levels and abundance of microbial genera such as Fusobacterium and Prevotella in primary episodes could be useful as early markers of R-CDI. We propose a readily available model for prediction of R-CDI that can be applied at the initial CDI episode. The use of this tool could help to better tailor treatments according to the risk of R-CDI.

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.

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.

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.

Combined atrial fibrillation ablation and balloon mitral commissurotomy in patients with rheumatic mitral stenosis.

INTRODUCTION: Ablation of atrial fibrillation (AF) is usually not considered in patients with rheumatic mitral stenosis (RMS). We analyzed the results of a combined procedure of AF ablation and percutaneous balloon mitral commissurotomy (PBMC). METHODS: We prospectively included 22 patients with severe RMS to undergo a combined PBMC + AF ablation procedure. Noninvasive mapping of the atria was also performed. A historical sample of propensity-scored matched patients who underwent PBMC alone was used as controls. The primary endpoint was freedom from AF/AT at 1-year. Multivariate analysis evaluated sinus rhythm (SR) predictors. RESULTS: Successful pulmonary vein isolation and electrocardiographic imaging-based drivers ablation was performed in 20 patients following PBMC. At 1-year, 75% of the patients in the combined group were in SR compared to 40% in the propensity-score matched group (p = 0.004). The composite of AF recurrence, need for mitral surgery and all-cause mortality was also more frequent in the control group (65% vs. 30%; p = 0.005). Catheter ablation (odds ratio [OR] 1.58; 95% confidence interval [CI] [1.17-17.37]; p = 0.04) and AF type (OR 1.46; 95% CI [1.05-82.64]; p < 0.001) were the only independent predictors of SR at 1-year. Noninvasive mapping in the combined group showed that the number of simultaneous rotors (OR 2.10; 95% CI [1.41-10.2]; p = 0.04) was the only independent predictor of AF. CONCLUSION: A combined procedure of AF ablation and PBMC significantly increased the proportion of patients in sinus rhythm at 1-year. Noninvasive mapping may help to improve AF characterization and guide personalized AF treatment.

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.