Partho P. Sengupta

Current and evolving echocardiographic techniques for the quantitative evaluation of cardiac mechanics: ASE/EAE consensus statement on methodology and indications: Endorsed by the Japanese Society of Echocardiography

Echocardiographic imaging is ideally suited for the evaluation of cardiac mechanics because of its intrinsically dynamic nature. Because for decades, echocardiography has been the only imaging modality that allows dynamic imaging of the heart, it is only natural that new, increasingly automated techniques for sophisticated analysis of cardiac mechanics have been driven by researchers and manufacturers of ultrasound imaging equipment.Several such technique shave emerged over the past decades to address the issue of readers experience and inter measurement variability in interpretation.Some were widely embraced by echocardiographers around the world and became part of the clinical routine,whereas others remained limited to research and exploration of new clinical applications.Two such techniques have dominated the research arena of echocardiography: (1) Doppler based tissue velocity measurements,frequently referred to as tissue Doppler or myocardial Doppler, and (2) speckle tracking on the basis of displacement measurements.Both types of measurements lend themselves to the derivation of multiple parameters of myocardial function. The goal of this document is to focus on the currently available techniques that allow quantitative assessment of myocardial function via image-based analysis of local myocardial dynamics, including Doppler tissue imaging and speckle-tracking echocardiography, as well as integrated backscatter analysis. This document describes the current and potential clinical applications of these techniques and their strengths and weaknesses,briefly surveys a selection of the relevant published literature while highlighting normal and abnormal findings in the context of different cardiovascular pathologies, and summarizes the unresolved issues, future research priorities, and recommended indications for clinical use.

Current and Evolving Echocardiographic Techniques for the Quantitative Evaluation of Cardiac Mechanics: ASE/EAE Consensus Statement on Methodology and Indications Endorsed by the Japanese Society of Echocardiography

Echocardiographic imaging is ideally suited for the evaluation of cardiac mechanics because of its intrinsically dynamic nature. Because for decades, echocardiography has been the only imaging modality that allows dynamic imaging of the heart, it is only natural that new, increasingly automated techniques for sophisticated analysis of cardiac mechanics have been driven by researchers and manufacturers of ultrasound imaging equipment. Several such techniques have emerged over the past decades to address the issue of readers experience and inter-measurement variability in interpretation. Some were widely embraced by echocardiographers around the world and became part of the clinical routine, whereas others remained limited to research and exploration of new clinical applications. Two such techniques have dominated the research arena of echocardiography: (1) Doppler-based tissue velocity measurements, frequently referred to as tissue Doppler or myocardial Doppler, and (2) speckle tracking on the basis of displacement measurements. Both types of measurements lend themselves to the derivation of multiple parameters of myocardial function. The goal of this document is to focus on the currently available techniques that allow quantitative assessment of myocardial function via image-based analysis of local myocardial dynamics, including Doppler tissue imaging and speckle-tracking echocardiography, as well as integrated back- scatter analysis. This document describes the current and potential clinical applications of these techniques and their strengths and weaknesses, briefly surveys a selection of the relevant published literature while highlighting normal and abnormal findings in the context of different cardiovascular pathologies, and summarizes the unresolved issues, future research priorities, and recommended indications for clinical use.

Global Left Atrial Strain Correlates with CHADS2 Risk Score in Patients with Atrial Fibrillation

BACKGROUND The aim of this cross-sectional study was to explore the association between echocardiographic parameters and CHADS2 score in patients with nonvalvular atrial fibrillation (AF). METHODS Seventy-seven subjects (36 patients with AF, 41 control subjects) underwent standard two-dimensional, Doppler, and speckle-tracking echocardiography to compute regional and global left atrial (LA) strain. RESULTS Global longitudinal LA strain was reduced in patients with AF compared with controls (P < .001) and was a predictor of high risk for thromboembolism (CHADS2 score ≥ 2; odds ratio, 0.86; P = .02). LA strain indexes showed good interobserver and intraobserver variability. In sequential Cox models, the prediction of hospitalization and/or death was improved by addition of global LA strain and indexed LA volume to CHADS2 score (P = .003). CONCLUSIONS LA strain is a reproducible marker of dynamic LA function and a predictor of stroke risk and cardiovascular outcomes in patients with AF.

Immediate and delayed effects of successful percutaneous transvenous mitral commissurotomy on global right ventricular function in patients with isolated mitral stenosis

Global right ventricular function of the pressure-overloaded right ventricle in patients with mitral stenosis and pulmonary hypertension after successful percutaneous transvenous mitral commissurotomy (PTMC) has not been well-defined. With the use of a recently developed Doppler method for estimating right ventricular function in human beings, we studied 25 consecutive patients with isolated rheumatic mitral stenosis before, immediately after (mean, 40+/-12 h) and at a mean follow-up of 11.5 months after PTMC. Immediately after percutaneous mitral commissurotomy, there was a significant increase in mitral valve area (P = 0.000017) along with a decrease in mean pulmonary pressure (P = 0.001). The index was not affected immediately after successful PTMC (0.70+/-0.25 vs., 0.58+/-0.18; P = 0.06); however, at follow-up of about one year, the index showed a significant decrease (0.697+/-0.28 vs. 0.380+/-0.13; P = 0.0008, n = 24). The change in the index was characterised by a significant prolongation of the right ventricular ejection time, with a decrease in the isovolumic intervals. The Doppler index of combined right ventricular function was significantly correlated to the mean pulmonary artery pressure (r = 0.695, P<0.001) and systolic pulmonary artery pressure (r = 0.60, P = 0.007) before PTMC and also immediately after the procedure; however, at follow-up, the index had no correlation with the Doppler estimated pulmonary artery systolic pressure (r = 0.07). Despite a larger mitral valve area following PTMC, right ventricular isovolumic indices remain abnormal on mid-term follow-up, although global function tends to normalise in two-thirds of the patients.

Machine learning in cardiovascular medicine: are we there yet?

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.

Standardization of left atrial, right ventricular, and right atrial deformation imaging using two-dimensional speckle tracking echocardiography: a consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging

The EACVI/ASE/Industry Task Force to standardize deformation imaging prepared this consensus document to standardize definitions and techniques for using two-dimensional (2D) speckle tracking echocardiography (STE) to assess left atrial, right ventricular, and right atrial myocardial deformation. This document is intended for both the technical engineering community and the clinical community at large to provide guidance on selecting the functional parameters to measure and how to measure them using 2D STE.This document aims to represent a significant step forward in the collaboration between the scientific societies and the industry since technical specifications of the software packages designed to post-process echocardiographic datasets have been agreed and shared before their actual development. Hopefully, this will lead to more clinically oriented software packages which will be better tailored to clinical needs and will allow industry to save time and resources in their development.

3-Dimensional–Printed Models for TAVR Planning: Why Guess When You Can See?

P aravalvular leak (PVL) has been the Achilles’ heel of transcatheter aortic valve replacement (TAVR), especially with self-expandable valves. In the pivotal CoreValve and PARTNER (Placement of AoRtic TraNscathetER Valves) I trials, moderate to severe PVL occurred in 7.8% and 11.8% of patients, respectively, and was associated with excess shortand long-term mortality (2–4). In recent years, novel designs of the newer transcatheter heart valves (THVs), along with our enhanced understanding of the optimal sizing and implantation of these valves, led to a significant reduction in the rates of PVL. Moderate to severe PVL was only reported in 5.4% and 3.7% of patients in the recently published SURTAVI (SUrgical Replacement and Transcatheter Aortic Valve Implantation) and PARTNER II trials, respectively (5,6). However, these rates of significant PVL are higher than what is observed with surgical bioprosthetic valves, calling for further efforts to mitigate the risk of PVL following TAVR (7).

New Cardiac Imaging Algorithms to Diagnose Constrictive Pericarditis Versus Restrictive Cardiomyopathy

Purpose of ReviewEchocardiography is the mainstay in the diagnostic evaluation of constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM), but no single echocardiographic parameter is sufficiently robust to accurately distinguish between the two conditions. The present review summarizes the recent advances in echocardiography that promise to improve its diagnostic performance for this purpose. The role of other imaging modalities such as cardiac computed tomography, magnetic resonance imaging, and invasive hemodynamic assessment in the overall diagnostic approach is also discussed briefly.Recent FindingsA recent study has demonstrated improved diagnostic accuracy of echocardiography with integration of multiple conventional echocardiographic parameters in to a step-wise algorithm. Concurrently, the studies using speckle-tracking echocardiography have revealed distinct and disparate patterns of myocardial mechanical abnormalities in CP and RCM with their ability to distinguish between the two conditions. The incorporation of machine-learning algorithms into echocardiography workflow permits easy integration of the wealth of the diagnostic data available and promises to further enhance the diagnostic accuracy of echocardiography.SummaryNew imaging algorithms are continuously being evolved to permit accurate distinction between CP and RCM. Further research is needed to validate the accuracy of these newer algorithms and to define their place in the overall diagnostic approach for this purpose.

Handheld Echocardiography: Current State and Future Perspectives

Echocardiography is the primary imaging modality for diagnosing cardiac conditions. Over the past 2 decades, technological advancements have resulted in the emergence of miniaturized handheld ultrasound equipment that is compact and battery operated, and handheld echocardiography can be readily performed at the point of care with reasonable image quality. The simplicity of use, availability at the patients bedside, easy transportability, and relatively low cost have encouraged physicians to use these devices for prompt medical decision making. As a consequence, the use of handheld echocardiography is on the rise even among nonechocardiographers (intensivists, emergency care physicians, internists, and medical students). One of the real utilities of ultrasound-augmented clinical diagnosis is in evaluating patients efficiently and selecting patients for appropriate downstream diagnostic testing including comprehensive echocardiography. Although clinical evidence supports the use of handheld devices in various clinical settings and by different users, proficiency in point-of-care ultrasound requires dedicated training in both performance and interpretation. This review summarizes the existing literature on the use of handheld echocardiography in conducting focused cardiac examinations: its training requirements, challenges, opportunities, and future perspectives in the care of the cardiovascular patient.

The Many Dimensions of Diastolic Function: A Curse or a Blessing?

L eft ventricular filling involves a number of physiological processes, including untwist (which generates suction), myocardial relaxation, and left ventricular compliance. Each of these processes may be disturbed by a variety of causes, including aging, degenerative changes, myocardial energetics, and fibrosis. Moreover, each process is also manifested by numerous signals that can be measured using imaging techniques varying from untwist to isovolumic relaxation, the balance between passive and active left ventricular filling, relaxation velocity, and so on. Perhaps because of the heterogeneous contributors to these signals, the development of multiple echocardiographic parameters has challenged the clinician’s ability to interpret diastolic function. Repeated updates in algorithms have sought to bring together multiple parameters in an empirical approach, but the concordance of observers with previous iterations of the recommendations for assessing diastolic dysfunction has been limited (1), and the validity of the approach presented in the current recommendations (2) is a matter of ongoing investigation. In this context, modern statistical approaches for separating, classifying, and visualizing patient-centric models could greatly facilitate the meaningful integration of routinely acquired echocardiographic parameters. To this end, Selmeryd et al. (3), in this issue of iJACC, describe the development of a multivariable statistical model using novel computational approaches that combine age-related changes in diastolic variables for differentiating normal from abnormal filling patterns.