Retrospective evaluation of echocardiographic variables for prediction of heart failure hospitalization in heart failure with preserved versus reduced ejection fraction: A single center experience

The Concept of "Heart Failure with Preserved Ejection Fraction": Time for a Critical Reappraisal

Heart failure with preserved ejection fraction (HFpEF) is frequently observed in elderly physically deconditioned subjects, mainly women with hypertension, obesity, glucose intolerance/diabetes, atrial fibrillation, anaemia, coronary artery disease, chronic pulmonary disease, and chronic renal insufficiency. In practice, these conditions represent the majority of cardiac diseases we deal with in our daily clinical practice. For this reason, the HFpEF disease does not exist as a single entity and, as such, no specific unifying therapy could be found. New classification attempts still do not consider the multifaceted aspect of the HF syndrome and appear rather as an artefactual attempt to categorize a condition which is indeed not categorizable. The aim of the present article is to critically review the construction of the concept of the HFpEF syndrome and propose the return of a pathophysiological approach in the evaluation and treatment of patients. Considering the huge economic efforts employed up to date to run awfully expensive trials and research in this field, it is time to call action and redirect such resources towards more specific pathophysiological classifications and potential specific therapeutic targets.

Machine Learning Augmented Echocardiography for Diastolic Function Assessment

Cardiac diastolic dysfunction is prevalent and is a diagnostic criterion for heart failure with preserved ejection fraction—a burgeoning global health issue. As gold-standard invasive haemodynamic assessment of diastolic function is not routinely performed, clinical guidelines advise using echocardiography measures to determine the grade of diastolic function. However, the current process has suboptimal accuracy, regular indeterminate classifications and is susceptible to confounding from comorbidities. Advances in artificial intelligence in recent years have created revolutionary ways to evaluate and integrate large quantities of cardiology data. Imaging is an area of particular strength for the sub-field of machine-learning, with evidence that trained algorithms can accurately discern cardiac structures, reliably estimate chamber volumes, and output systolic function metrics from echocardiographic images. In this review, we present the emerging field of machine-learning based echocardiographic diastolic function assessment. We summarise how machine-learning has made use of diastolic parameters to accurately differentiate pathology, to identify novel phenotypes within diastolic disease, and to grade diastolic function. Perspectives are given about how these innovations could be used to augment clinical practice, whilst areas for future investigation are identified.