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

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 in 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, NCT02917213), 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 ability 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.

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.

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.

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.

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.

Transvalvular jet velocity, aortic valve area, mortality, and cardiovascular outcomes.

AIMS: The interplay between aortic stenosis (AS), cardiovascular events, and mortality is poorly understood. In addition, how echocardiographic indices compare for predicting outcomes remains unexplored for the full range of AS severity. METHODS AND RESULTS: We prospectively calculated peak jet velocity (Vmax) and aortic valve area (AVA) in 5994 adult subjects with and without AS. We linked ultrasound data to 5-year mortality and clinical events obtained from electronic medical records. Proportional-hazard and negative binomial regression models were adjusted for relevant covariables such as age, sex, comorbidities, stroke-volume, LV ejection fraction, left valve regurgitation, aortic valve sclerosis or calcification, and valve replacement. We observed a strong linear relationship between Vmax and all-cause mortality (hazard ratio: 1.26, 95% confidence interval: 1.19-1.33 per 100 cm/s), cardiovascular events, as well as incidental and recurrent heart failure (HF). Adjusted risks were highly significant even at Vmax values in the range of 150-200 cm/s, risk curves separating very early after the index exam. Vmax was not associated with coronary, arrhythmic, cerebrovascular, or non-cardiovascular events. Although risks were confirmed when AVA was entered in place of Vmax, the risks estimated for categories based on the two indices were mismatched, even in patients with normal flow. An external cohort comprising 112 690 patients confirmed augmented risks of all-cause and cardiovascular mortality starting at values of Vmax and AVA in the range of mild AS. CONCLUSIONS: Aortic stenosis is strongly associated to all-cause mortality, cardiovascular mortality, and cardiac events, specifically HF. Risks increase in parallel to the degree of outflow obstruction but are apparent very early in patients with mild disease. Criteria for grading AS based on Vmax and AVA are mismatched in terms of outcomes.

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.