Cardiac Mast Cells: Underappreciated Immune Cells in Cardiovascular Homeostasis and Disease.

Background Aortic valve replacement (AVR) is a life‐saving treatment for patients with symptomatic severe aortic valve stenosis. We sought to determine whether transcatheter AVR has resulted in a m...

Racial Differences in the Use of Aortic Valve Replacement for Treatment of Symptomatic Severe Aortic Valve Stenosis in the Transcatheter Aortic Valve Replacement Era

Cardiovascular multidisciplinary heart teams (MDHTs) have evolved significantly over the past decade. These teams play a central role in the treatment of a wide array of cardiovascular diseases affecting interventional cardiology, cardiac surgery, interventional imaging, advanced heart failure, adult congenital heart disease, cardio-oncology, and cardio-obstetrics. To meet the specific needs of both patients and heart programs, the composition and function of cardiovascular MDHTs have had to adapt and evolve. Although lessons have been learned from multidisciplinary cancer care, best practices for the operation of cardiovascular MDHTs have yet to be defined, and the evidence base supporting their effectiveness is limited. This expert panel review discusses the history and evolution of cardiovascular MDHTs, their composition and role in treating patients across a broad spectrum of disciplines, basic tenets for successful operation, and the future challenges facing them. 

The Multidisciplinary Heart Team in Cardiovascular Medicine

Valvular heart disease (VHD) is the next epidemic in the cardiovascular field, affecting millions of people worldwide and having a major impact on health care systems. With aging of the population, the incidence and prevalence of VHD will continue to increase. However, VHD has not received the attention it deserves from both the public and policymakers. Despite important advances in the pathophysiology, natural history, management, and treatment of VHD including the development of transcatheter therapies, VHD remains underdiagnosed, identified late, and often undertreated with inequality in access to care and treatment options, and there is no medication that can prevent disease progression. The present review article discusses these gaps in the management of VHD and potential actions to undertake to improve the outcome of patients with VHD.

Unmet needs in valvular heart disease.

•Automated detection of AS is a novel approach to diagnosis. •ML methods were trained to detect AS from limited 2D echocardiographic images. •Fully automated screening for AS using limited data sets is achievable. •Release of a TTE database will encourage collaboration. Background Aortic stenosis (AS) is a degenerative valve condition that is underdiagnosed and undertreated. Detection of AS using limited two-dimensional echocardiography could enable screening and improve appropriate referral and treatment of this condition. The aim of this study was to develop methods for automated detection of AS from limited imaging data sets. Methods Convolutional neural networks were trained, validated, and tested using limited two-dimensional transthoracic echocardiographic data sets. Networks were developed to accomplish two sequential tasks: (1) view identification and (2) study-level grade of AS. Balanced accuracy and area under the receiver operator curve (AUROC) were the performance metrics used. Results Annotated images from 577 patients were included. Neural networks were trained on data from 338 patients (average n = 10,253 labeled images), validated on 119 patients (average n = 3,505 labeled images), and performance was assessed on a test set of 120 patients (average n = 3,511 labeled images). Fully automated screening for AS was achieved with an AUROC of 0.96. Networks can distinguish no significant (no, mild, mild to moderate) AS from significant (moderate or severe) AS with an AUROC of 0.86 and between early (mild or mild to moderate AS) and significant (moderate or severe) AS with an AUROC of 0.75. External validation of these networks in a cohort of 8,502 outpatient transthoracic echocardiograms showed that screening for AS can be achieved using parasternal long-axis imaging only with an AUROC of 0.91. Conclusion Fully automated detection of AS using limited two-dimensional data sets is achievable using modern neural networks. These methods lay the groundwork for a novel method for screening for AS. Aortic stenosis (AS) is a degenerative valve condition that is underdiagnosed and undertreated. Detection of AS using limited two-dimensional echocardiography could enable screening and improve appropriate referral and treatment of this condition. The aim of this study was to develop methods for automated detection of AS from limited imaging data sets. Convolutional neural networks were trained, validated, and tested using limited two-dimensional transthoracic echocardiographic data sets. Networks were developed to accomplish two sequential tasks: (1) view identification and (2) study-level grade of AS. Balanced accuracy and area under the receiver operator curve (AUROC) were the performance metrics used. Annotated images from 577 patients were included. Neural networks were trained on data from 338 patients (average n = 10,253 labeled images), validated on 119 patients (average n = 3,505 labeled images), and performance was assessed on a test set of 120 patients (average n = 3,511 labeled images). Fully automated screening for AS was achieved with an AUROC of 0.96. Networks can distinguish no significant (no, mild, mild to moderate) AS from significant (moderate or severe) AS with an AUROC of 0.86 and between early (mild or mild to moderate AS) and significant (moderate or severe) AS with an AUROC of 0.75. External validation of these networks in a cohort of 8,502 outpatient transthoracic echocardiograms showed that screening for AS can be achieved using parasternal long-axis imaging only with an AUROC of 0.91. Fully automated detection of AS using limited two-dimensional data sets is achievable using modern neural networks. These methods lay the groundwork for a novel method for screening for AS. 

Automated Detection of Aortic Stenosis using Machine Learning.

Aortic Valve Stenosis Treatment Disparities in the Underserved: JACC Council Perspectives.

2020 ACC Clinical Competencies for Nurse Practitioners and Physician Assistants in Adult Cardiovascular Medicine: A Report of the ACC Competency Management Committee.

The heartbeet clinic: a plant-forward cardiology registry

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Aortic Valve Stenosis Treatment Disparities in the Underserved: JACC Council Perspectives.

The Multidisciplinary Heart Team in Cardiovascular Medicine

2020 ACC Clinical Competencies for Nurse Practitioners and Physician Assistants in Adult Cardiovascular Medicine: A Report of the ACC Competency Management Committee.

The heartbeet clinic: a plant-forward cardiology registry