Abstract
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.
LINKED CONTENT
This article is linked to Vasudevan et al papers. To view these articles, visit https://doi.org/10.1111/apt.16039 and https://doi.org/10.1111/apt.16095
Abstract
Making a firm diagnosis of chronic heart failure with preserved ejection fraction (HFpEF) remains a challenge. We recommend a new stepwise diagnostic process, the ‘HFA–PEFF diagnostic ...algorithm’. Step 1 (P=Pre-test assessment) is typically performed in the ambulatory setting and includes assessment for HF symptoms and signs, typical clinical demographics (obesity, hypertension, diabetes mellitus, elderly, atrial fibrillation), and diagnostic laboratory tests, electrocardiogram, and echocardiography. In the absence of overt non-cardiac causes of breathlessness, HFpEF can be suspected if there is a normal left ventricular ejection fraction, no significant heart valve disease or cardiac ischaemia, and at least one typical risk factor. Elevated natriuretic peptides support, but normal levels do not exclude a diagnosis of HFpEF. The second step (E: Echocardiography and Natriuretic Peptide Score) requires comprehensive echocardiography and is typically performed by a cardiologist. Measures include mitral annular early diastolic velocity (e′), left ventricular (LV) filling pressure estimated using E/e′, left atrial volume index, LV mass index, LV relative wall thickness, tricuspid regurgitation velocity, LV global longitudinal systolic strain, and serum natriuretic peptide levels. Major (2 points) and Minor (1 point) criteria were defined from these measures. A score ≥5 points implies definite HFpEF; ≤1 point makes HFpEF unlikely. An intermediate score (2–4 points) implies diagnostic uncertainty, in which case Step 3 (F1: Functional testing) is recommended with echocardiographic or invasive haemodynamic exercise stress tests. Step 4 (F2: Final aetiology) is recommended to establish a possible specific cause of HFpEF or alternative explanations. Further research is needed for a better classification of HFpEF.
Since the E/e’ ratio was first described in 1997 as a noninvasive surrogate marker of mean pulmonary capillary wedge pressure, it has gained a central role in diagnostic recommendations and a ...supremacy in clinical use that require critical reappraisal. We review technical factors, physiological influences, and pathophysiological processes that can complicate the interpretation of E/e’. The index has been validated in certain circumstances, but its use cannot be extrapolated to other situations—such as critically ill patients or children—in which it has either been shown not to work or it has not been well validated. Meta‐analyses demonstrated that E/e’ is not useful for the diagnosis of HFpEF and that changes in E/e’ are uninformative during diastolic stress echocardiography. A similar ratio has been applied to estimate right heart filling pressure despite insufficient evidence. As a composite index, changes in E/e’ should only be interpreted with knowledge of changes in its components. Sometimes, e’ alone may be as informative. Using a scoring system for diastolic function that relies on E/e’, as recommended in consensus documents, leaves some patients unclassified and others in an intermediate category. Alternative methods for estimating left heart filling pressures may be more accurate, including the duration of retrograde pulmonary venous flow, or contractile deformation during atrial pump function. Using all measurements as continuous variables may demonstrate abnormal diastolic function that is missed by using the reductive index E/e’ alone. With developments in diagnostic methods and clinical decision support tools, this may become easier to implement.
Provocative comments can entertain and instruct as long as they are used to stimulate a civilized discussion, and it is fun to embrace an opportunity to change one’s mind (and learn). I am therefore ...delighted to respond to Adrian Ionescu’s comments, although I think he has got it wrong—as I will aim to demonstrate. In the spirit of this debate, please indulge me while I too let off some steam!
I have always disliked the fact that one of the subspecialties within cardiology, which did not exist when I qualified in the 1970s, has come to be known as “cardiac imaging.” Cardiac diagnosis is not about pictures, although some conditions are indeed instantly recognizable. Usually, what we need to know to understand disease is how the heart is functioning, much more than what it looks like. That is true for coronary arteriography as much as for non-invasive imaging. If I am forced to adopt a subspeciality label, then I would much prefer to be considered a clinical pathophysiologist.
Accurate diagnosis is the sine
of logical evidence-based clinical practice, yet we often get it wrong. And there remain many patients with disease that we cannot diagnose precisely because we do not understand it sufficiently. Why does this patient with heart failure with reduced ejection fraction have impaired left ventricular function? Why does that patient with normal blood pressure have left ventricular hypertrophy? In this patient in sinus rhythm, which particular aspects of cardiovascular function will influence the development of dementia? Cardiologists who are expert in performing, analyzing, and interpreting detailed echocardiographic and cardiovascular investigations are needed to give us the best chance of answering such questions. They cannot be replaced by an uninterpretable computer algorithm when no-one yet knows the answer—but by staying in control, researchers can use artificial intelligence (AI) to help their thinking.
Current diagnosis of heart failure with preserved ejection fraction (HFpEF) is suboptimal. We tested the hypothesis that comprehensive machine learning (ML) of left ventricular function at rest and ...exercise objectively captures differences between HFpEF and healthy subjects.
One hundred fifty-six subjects aged >60 years (72 HFpEF+33 healthy for the initial analyses; 24 hypertensive+27 breathless for independent evaluation) underwent stress echocardiography, in the MEDIA study (Metabolic Road to Diastolic Heart Failure). Left ventricular long-axis myocardial velocity patterns were analyzed using an unsupervised ML algorithm that orders subjects according to their similarity, allowing exploration of the main trends in velocity patterns. ML identified a continuum from health to disease, including a transition zone associated to an uncertain diagnosis. Clinical validation was performed (1) to characterize the main trends in the patterns for each zone, which corresponded to known characteristics and new features of HFpEF; the ML-diagnostic zones differed for age, body mass index, 6-minute walk distance, B-type natriuretic peptide, and left ventricular mass index (
<0.05) and (2) to evaluate the consistency of the proposed groupings against diagnosis by current clinical criteria; correlation with diagnosis was good (κ, 72.6%; 95% confidence interval, 58.1-87.0); ML identified 6% of healthy controls as HFpEF. Blinded reinterpretation of imaging from subjects with discordant clinical and ML diagnoses revealed abnormalities not included in diagnostic criteria. The algorithm was applied independently to another 51 subjects, classifying 33% of hypertensive and 67% of breathless controls as mild-HFpEF.
The analysis of left ventricular long-axis function on exercise by interpretable ML may improve the diagnosis and understanding of HFpEF.
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