Intraventricular vortex properties in nonischemic dilated cardiomyopathy

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

Non-selective beta-blockers impair global circulatory homeostasis and renal function in cirrhotic patients with refractory ascites

BACKGROUND & AIMS

The safety of non-selective β-blockers (NSBBs) has been questioned in refractory ascites (RA). We studied the effects of NSBBs on cardiac systolic function, systemic hemodynamics, and renal perfusion pressure (RPP) and function in patients with diuretic-responsive ascites (DRA) and RA.

METHODS

We performed a prospective pre-post repeated-measures study in cirrhotic patients, 18 with DRA and 20 with RA on NSBBs for variceal bleeding prophylaxis. Systolic function (by ejection intraventricular pressure difference [EIVPD]), hepatic venous pressure gradient (HVPG), cardiopulmonary pressures, RPP, and sympathetic activation were measured at baseline and after 4 weeks of propranolol.

RESULTS

EIVPD was elevated at baseline (RA 4.5 [2.8-5.7] and DRA 4.2 [3.1-5.7] mmHg; normal 2.4-3.6 mmHg) and directly related to the severity of vasodilation and sympathetic activation. NSBBs led to similar reductions in heart rate and HVPG in both groups. NSBBs reduced EIPVD in RA but not in DRA (-20% vs. -2%, p <0.01). In RA, the NSBB-induced reduction in EIPVD correlated with the severity of vasodilation and with higher plasma nitric oxide, norepinephrine and IL-6 (r >0.40, all p <0.05). NSBBs reduced RPP in both groups, but impaired renal function only in patients with RA. Reduced EIPVD correlated with decreases in RPP and estimated glomerular filtration rate (r >0.40, all p <0.01). After NSBB treatment, RPP dropped below the threshold of renal flow autoregulation in 11 of the 20 (55%) patients with RA, including the 4 fulfilling the criteria for HRS-AKI.

CONCLUSION

Renal perfusion and function depend critically on systolic function and sympathetic hyperactivation in RA. NSBBs blunt the sympathetic overdrive, hamper cardiac output, lower RPP below the critical threshold and impair renal function. β-blockade should be used cautiously or even avoided in patients with RA.

LAY SUMMARY

We have identified the mechanisms by which non-selective beta-blockers could impair survival in patients with refractory ascites. We show that peripheral vasodilation and sympathetic activation lead to increased left ventricle systolic function in patients with cirrhosis and ascites, which acts as an adaptive mechanism to maintain renal perfusion. When ascites becomes refractory, this compensatory cardiac response to vasodilation is critically dependent on sympathetic hyperactivation and is hardly able to maintain renal perfusion. In this setting, β-blockade blunts the sympathetic overdrive of cardiac function, hampers cardiac output, lowers renal perfusion pressure below the critical threshold and impairs renal function.

A clinical method for mapping and quantifying blood stasis in the left ventricle

In patients at risk of intraventrcular thrombosis, the benefits of chronic anticoagulation therapy need to be balanced with the pro-hemorrhagic effects of therapy. Blood stasis in the cardiac chambers is a recognized risk factor for intracardiac thrombosis and potential cardiogenic embolic events. In this work, we present a novel flow image-based method to assess the location and extent of intraventricular stasis regions inside the left ventricle (LV) by digital processing flow-velocity images obtained either by phase-contrast magnetic resonance (PCMR) or 2D color-Doppler velocimetry (echo-CDV). This approach is based on quantifying the distribution of the blood Residence Time (TR) from time-resolved blood velocity fields in the LV. We tested the new method in illustrative examples of normal hearts, patients with dilated cardiomyopathy and one patient before and after the implantation of a left ventricular assist device (LVAD). The method allowed us to assess in-vivo the location and extent of the stasis regions in the LV. Original metrics were developed to integrate flow properties into simple scalars suitable for a robust and personalized assessment of the risk of thrombosis. From a clinical perspective, this work introduces the new paradigm that quantitative flow dynamics can provide the basis to obtain subclinical markers of intraventricular thrombosis risk. The early prediction of LV blood stasis may result in decrease strokes by appropriate use of anticoagulant therapy for the purpose of primary and secondary prevention. It may also have a significant impact on LVAD device design and operation set-up.

The role of elastic restoring forces in right-ventricular filling

AIMS

The physiological determinants of RV diastolic function remain poorly understood. We aimed to quantify the contribution of elastic recoil to RV filling and determine its sensitivity to interventricular interaction.

METHODS AND RESULTS

High-fidelity pressure-volume loops and simultaneous 3-dimensional ultrasound sequences were obtained in 13 pigs undergoing inotropic modulation, volume overload, and acute pressure overload induced by endotoxin infusion. Using a validated method, we isolated elastic restoring forces from ongoing relaxation using conventional pressure-volume data. The RV contracted below the equilibrium volume in >75% of the data sets. Consequently, elastic recoil generated strong sub-atmospheric passive pressure at the onset of diastole [-3 (-4 to -2) mmHg at baseline]. Stronger restoring suction pressure was related to a shorter isovolumic relaxation period, a higher rapid filling fraction, and lower atrial pressures (all P < 0.05). Restoring forces were mostly determined by the position of operating volumes around the equilibrium volume. By this mechanism, the negative inotropic effect of beta-blockade reduced and sometimes abolished restoring forces. During acute pressure overload, restoring forces initially decreased, but recovered at advanced stages. This biphasic response was related to alterations of septal curvature induced by changes in the diastolic LV-RV pressure balance. The constant of elastic recoil was closely related to the constant of passive stiffness (R = 0.69).

CONCLUSION

The RV works as a suction pump, exploiting contraction energy to facilitate filling by means of strong elastic recoil. Restoring forces are influenced by the inotropic state and RV conformational changes mediated by direct ventricular interdependence.

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.

Clinical assessment of intraventricular blood transport in patients undergoing cardiac resynchronization therapy

In the healthy heart, left ventricular (LV) filling generates different flow patterns which have been proposed to optimize blood transport by coupling diastole and systole. This work presents a novel image-based method to assess how different flow patterns influence LV blood transport in patients undergoing cardiac resynchronization therapy (CRT). Our approach is based on solving the advection equation for a passive scalar field from time-resolved blood velocity fields. Imposing time-varying inflow boundary conditions for the scalar field provides a straightforward method to distinctly track the transport of blood entering the LV in the different filling waves of a given cardiac cycle, as well as the transport barriers which couple filling and ejection. We applied this method to analyze flow transport in a group of patients with implanted CRT devices and a group of healthy volunteers. Velocity fields were obtained using echocardiographic color Doppler velocimetry, which provides two-dimensional time-resolved flow maps in the apical long axis three-chamber view of the LV. In the patients under CRT, the device programming was varied to analyze flow transport under different values of the atrioventricular conduction delay, and to model tachycardia (100 bpm). Using this method, we show how CRT influences the transit of blood inside the left ventricle, contributes to conserving kinetic energy, and favors the generation of hemodynamic forces that accelerate blood in the direction of the LV outflow tract. These novel aspects of ventricular function are clinically accessible by quantitative analysis of color-Doppler echocardiograms.

Data from acellular human heart matrix

Perfusion decellularization of cadaveric hearts removes cells and generates a cell-free extracellular matrix scaffold containing acellular vascular conduits, which are theoretically sufficient to perfuse and support tissue-engineered heart constructs. This article contains additional data of our experience decellularizing and testing structural integrity and composition of a large series of human hearts, “Acellular human heart matrix: a critical step toward whole heat grafts” (Sanchez et al., 2015) [1]. Here we provide the information about the heart decellularization technique, the valve competence evaluation of the decellularized scaffolds, the integrity evaluation of epicardial and myocardial coronary circulation, the pressure volume measurements, the primers used to assess cardiac muscle gene expression and, the characteristics of donors, donor hearts, scaffolds and perfusion decellularization process.

Valve area and the risk of overestimating aortic stenosis

OBJECTIVE

To obtain reference values of aortic valve area (AVA) in a large population and to infer the risk of overestimating aortic stenosis (AS) when focusing on flow-corrected indices of severity.

METHODS

We prospectively measured indices of AS in all consecutive echocardiograms performed in a large referral cardiac imaging laboratory for 1 year. We specifically analysed the distribution of AVA, indexed AVA and velocity ratio (Vratio) in patients with and without AS, the latter defined as the coexistence of valvular outflow obstruction (Vmax ≥2.5 m/s) and morphological findings of valve degeneration.

RESULTS

16 156 echocardiograms were analysed, 14 669 of which did not show valvular obstruction (peak jet velocity <2.5 m/s). In the latter group, AVA was 2.6±0.7 cm² in 8190 studies with normal valves and 2.3±0.7 cm² in 6479 studies with aortic sclerosis (AScl). There was a relatively wide overlap between values of AVA, indexed AVA and velocity ratio between studies of patients with AScl and AS. Values of AVA ≤1.0 cm² were found in 0.5% of studies with normal valves and 1.8% of studies with AScl. These proportions were 3.1% and 9.3% for AVA ≤1.5 cm², respectively. Vratio ≤0.25 were found in 0.1% of patients without obstruction. Risk factors for a small AVA in patients without obstruction were AScl, female sex, small body surface area, low ejection fraction and mitral regurgitation.

CONCLUSIONS

Normal values of continuity-equation derived AVA are smaller than previously considered. AVA values below cutoffs of moderate or severe AS can be found in patients without the disease. Flow-corrected indices may overestimate AS in patients with low gradients, particularly in the presence of well-identified risk factors.

Prediction of left ventricular thrombus after myocardial infarction: a cardiac magnetic resonance-based prospective registry

BACKGROUND

Left ventricular thrombus (LVTh) is a severe complication after ST-segment elevation myocardial infarction (STEMI).

OBJECTIVES

We aim to predict LVTh occurrence by cardiac magnetic resonance (CMR) using clinical, echocardiographic, and electrocardiographic (ECG) variables readily available at admission.

METHODS

We included 590 reperfused STEMI patients who underwent early (1-week) and/or late (6-month) CMR in our institution. Baseline clinical, echocardiographic (left ventricular ejection fraction -LVEF-) and ECG data (summatory of ST-segment elevation -sum-STE- and Q-wave and residual ST-elevation >1 mm -Q-STE-) during admission were registered. Multivariate binary logistic regression models and receiver operating characteristic curves were computed for LVTh prediction.

RESULTS

LVTh was detected by CMR in 43 (7.3 %) patients and was predicted by previous chronic coronary syndrome (CCS, HR 4.74 [1.82-12.35], p = 0.001), anterior STEMI (HR 10.93 [2.47-48.31], p = 0.002), LVEF (HR 0.96 [0.93-0.99] per %, p = 0.008), maximum sum-STE (HR 1.04 [1.01-1.07] per mm, p = 0.04), and Q-STE (HR 1.31 [1.08-1.6] per lead, p = 0.008). High-risk patients with both major (anterior STEMI and Q-STE in ≥1 leads) and 1-3 minor (CCS, maximum sum-STE >10 mm, LVEF <50%) factors showed the highest LVTh risk (19.6 % within 6 months). The model showed excellent discrimination ability (area under the curve=0.85 [0.81-0.9], p < 0.001). Simplified 4-variable (excluding sum-STE) and 3-variable (also excluding CCS) risk scores showed similar discrimination ability and were externally validated.

CONCLUSIONS

LVTh within 6 months post-STEMI can be predicted using pre-discharge clinical (anterior infarction and CCS), echocardiographic (LVEF), and ECG (sum-STE and Q-STE) data. Our results can help select patients who should undergo CMR after STEMI for LVTh detection.