Faculty Bibliography

Infective endocarditis (IE) is a life-threatening disease associated with in-hospital mortality of nearly one in five cases. IE can destroy valvular tissue, which may rarely progress to aneurysm formation, most commonly at the anterior leaflet in instances of mitral valve involvement. We present a remarkable case of a patient with IE and a rare complication of a ruptured aneurysm of the posterior leaflet of the mitral valve. Two- and Three-dimensional transesophageal echocardiography, intra-operative videography, and histopathologic analysis revealed disruption at this unusual location-at the junction of the P2 and P3 scallops, surrounded by an annular abscess.

OBJECTIVES/OBJECTIVE:The aim of this study was to clarify the dynamics of the mitral annulus throughout the cardiac cycle and its relevance to transcatheter mitral valve replacement (TMVR) sizing and case selection. BACKGROUND:Limited data are available regarding the relevance of mitral annular (MA) and neo-left ventricular outflow tract (LVOT) dynamics in the overall population presenting with significant mitral valve disease. METHODS:Patients attending a combined surgical-transcatheter heart valve clinic for severe symptomatic mitral valve disease were assessed using multiphase computed tomography. The relative influence of MA and neo-LVOT dynamics to TMVR case selection was studied. RESULTS:A total of 476 patients with significant mitral valve disease were evaluated. In 99 consecutive patients with severe mitral regurgitation, a 10-phase assessment showed that the mitral annulus was on average largest in late systole. On comparing maximal MA dimension with late systolic dimension, TMVR size assignment changed in 24.2% of patients. If the average MA perimeter was used to determine sizing, 48.5% were excluded because of MA dimension being too large; in a multiphase assessment of the neo-LVOT, an additional 16.2% were excluded on the basis of neo-LVOT dimension. In an expanded series of 312 consecutive patients, selection protocol influenced anatomical exclusion: a manufacturer-proposed early systolic approach excluded 69.2% of patients, whereas a late systolic approach excluded 82.7% of patients, the vast majority because of large mitral annuli. CONCLUSIONS:Contemporary TMVR can treat only a minority of patients with severe mitral regurgitation, principally because of limitations of large MA dimension.

Lung ultrasound (LUS) has gained considerable acceptance in emergency and critical care medicine but is yet to be fully implemented in cardiology. Standard imaging protocols for LUS in acute care settings have allowed the rapid and accurate diagnosis of dyspnea, respiratory failure, and shock. LUS is greatly additive to echocardiography and is superior to auscultation and chest radiography, particularly when the diagnosis of acute decompensated heart failure is in question. In this review, the authors describe LUS techniques, interpretation, and clinical applications, with the goal of informing cardiologists on the imaging modality. Additionally, the authors review LUS findings associated with various disease states most relevant to cardiac care. Although there is extensive literature on LUS in the acute care setting, there is a dearth of reviews directly focused for practicing cardiologists. Current evidence demonstrates that this modality is an important adjunct to echocardiography, providing valuable clinical information at the bedside.

BACKGROUND:We have recently tested an automated machine-learning algorithm that quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical views. However, in the point-of-care (POC) setting, apical 2-chamber views are often difficult to obtain, limiting the usefulness of this approach. Since most POC physicians often rely on visual assessment of apical 4-chamber and parasternal long-axis views, our algorithm was adapted to use either one of these 3 views or any combination. This study aimed to (1) test the accuracy of these automated estimates; (2) determine whether they could be used to accurately classify LV function. METHODS:Reference EF was obtained using conventional biplane measurements by experienced echocardiographers. In protocol 1, we used echocardiographic images from 166 clinical examinations. Both automated and reference EF values were used to categorize LV function as hyperdynamic (EF>73%), normal (53%-73%), mildly-to-moderately (30%-52%), or severely reduced (<30%). Additionally, LV function was visually estimated for each view by 10 experienced physicians. Accuracy of the detection of reduced LV function (EF<53%) by the automated classification and physicians' interpretation was assessed against the reference classification. In protocol 2, we tested the new machine-learning algorithm in the POC setting on images acquired by nurses using a portable imaging system. RESULTS:Protocol 1: the agreement with the reference EF values was good (intraclass correlation, 0.86-0.95), with biases <2%. Machine-learning classification of LV function showed similar accuracy to that by physicians in most views, with only 10% to 15% cases where it was less accurate. Protocol 2: the agreement with the reference values was excellent (intraclass correlation=0.84) with a minimal bias of 2.5±6.4%. CONCLUSIONS:The new machine-learning algorithm allows accurate automated evaluation of LV function from echocardiographic views commonly used in the POC setting. This approach will enable more POC personnel to accurately assess LV function.