Multimodality imaging predictors of sudden cardiac death

Bandera, F.; Baghdasaryan, Lilit; Mandoli, Giulia Elena; Cameli, Matteo

doi:10.1007/s10741-019-09893-z

Cited by 9 publications

(9 citation statements)

References 111 publications

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“…AR has been evaluated through a standard cardiac 1.5-T CMR scanner under steady-state submaximal exercise and at rest. The AR % decreased during exercise from 35 ( 9 – 26 , 39 , 56 – 92 ) % at rest to 16 ( 7 – 25 , 56 – 72 )% during exercise, p = 0.003. In addition, AR at rest correlates with an increase of cardiac index during submaximal exercise (R2 = 0.64; p = 0.001) ( 167 ).…”

Section: La Mechanics In Hf Stagesmentioning

confidence: 94%

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Cardiac Imaging for the Assessment of Left Atrial Mechanics Across Heart Failure Stages

Bandera

Mollo

Frigelli

et al. 2022

Front. Cardiovasc. Med.

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The left atrium (LA) is emerging as a key element in the pathophysiology of several cardiac diseases due to having an active role in contrasting heart failure (HF) progression. Its morphological and functional remodeling occurs progressively according to pressure or volume overload generated by the underlying disease, and its ability of adaptation contributes to avoid pulmonary circulation congestion and to postpone HF symptoms. Moreover, early signs of LA dysfunction can anticipate and predict the clinical course of HF diseases before the symptom onset which, particularly, also applies to patients with increased risk of HF with still normal cardiac structure (stage A HF). The study of LA mechanics (chamber morphology and function) is moving from a research interest to a clinical application thanks to a great clinical, prognostic, and pathophysiological significance. This process is promoted by the technological progress of cardiac imaging which increases the availability of easy-to-use tools for clinicians and HF specialists. Two-dimensional (2D) speckle tracking echocardiography and feature tracking cardiac magnetic resonance are becoming essential for daily practice. In this context, a deep understanding of LA mechanics, its prognostic significance, and the available approaches are essential to improve clinical practice. The present review will focus on LA mechanics, discussing atrial physiology and pathophysiology of main cardiac diseases across the HF stages with specific attention to the prognostic significance. Imaging techniques for LA mechanics assessment will be discussed with an overlook on the dynamic (under stress) evaluation of the chamber.

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Section: La Mechanics In Hf Stagesmentioning

confidence: 94%

“…Cardiovascular imaging is further evolving from a morphology-based tool to a unique in vivo opportunity to address heart function and structure ( 9 , 10 ). The understanding of LA mechanics changed with the introduction of myocardial deformation analysis and its multimodality use during stress conditions ( 5 ).…”

Section: Introductionmentioning

confidence: 99%

Cardiac Imaging for the Assessment of Left Atrial Mechanics Across Heart Failure Stages

Bandera

Mollo

Frigelli

et al. 2022

Front. Cardiovasc. Med.

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“…While each modality has its specific use case, fusing imaging modalities can increase understanding of how cardiac perfusion, structure, and function affect patient outcomes and theoretically enable better medical and surgical treatments. For example, Bandera et al provide an overview of how multiple imaging modalities can be combined to improve the prediction of sudden cardiac death (SCD) in individuals with dilated cardiomyopathies ( Figure 2 ) ( 24 ). While SCD accounts for over 200,000 deaths a year in the U.S. in those with cardiomyopathies, it can be difficult to predict who is at highest risk.…”

Section: Multimodal Data Fusion Across Different Use Casesmentioning

confidence: 99%

“… Framework for combining multiple imaging modalities to improve accuracy of predicting sudden cardiac death (SCD) in patients with dilated cardiomyopathies [from Bandera et al ( 24 )]. …”

Section: Multimodal Data Fusion Across Different Use Casesmentioning

confidence: 99%

Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care

Amal

Safarnejad

Omiye

et al. 2022

Front. Cardiovasc. Med.

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Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized and timely. Sources of data for digital health tools include multiple modalities such as electronic medical records (EMR), radiology images, and genetic repositories, to name a few. While historically, these data were utilized in silos, new machine learning (ML) and deep learning (DL) technologies enable the integration of these data sources to produce multi-modal insights. Data fusion, which integrates data from multiple modalities using ML and DL techniques, has been of growing interest in its application to medicine. In this paper, we review the state-of-the-art research that focuses on how the latest techniques in data fusion are providing scientific and clinical insights specific to the field of cardiovascular medicine. With these new data fusion capabilities, clinicians and researchers alike will advance the diagnosis and treatment of cardiovascular diseases (CVD) to deliver more timely, accurate, and precise patient care.

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“…Impaired sympathetic innervation and activity result in heterogeneous electrical conductance, act as arrhythmic substrate, and are linked to increased risk of SCD [87][88][89]. In this context, the norepinephrine analogue meta-iodbenzylguanidine radiolabeled with iodine-123 (123I-mIBG) has been used to measure neuronal integrity with reference to the heart/mediastinum (H/M) uptake ratio [90,91].…”

Section: Single-photon Emission Computed Tomography (Spect)mentioning

confidence: 99%

CMR-Based Risk Stratification of Sudden Cardiac Death and Use of Implantable Cardioverter–Defibrillator in Non-Ischemic Cardiomyopathy

Keil

Chevalier

Kirchhof

et al. 2021

IJMS

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Non-ischemic cardiomyopathy (NICM) is one of the most important entities for arrhythmias and sudden cardiac death (SCD). Previous studies suggest a lower benefit of implantable cardioverter–defibrillator (ICD) therapy in patients with NICM as compared to ischemic cardiomyopathy (ICM). Nevertheless, current guidelines do not differentiate between the two subgroups in recommending ICD implantation. Hence, risk stratification is required to determine the subgroup of patients with NICM who will likely benefit from ICD therapy. Various predictors have been proposed, among others genetic mutations, left-ventricular ejection fraction (LVEF), left-ventricular end-diastolic volume (LVEDD), and T-wave alternans (TWA). In addition to these parameters, cardiovascular magnetic resonance imaging (CMR) has the potential to further improve risk stratification. CMR allows the comprehensive analysis of cardiac function and myocardial tissue composition. A range of CMR parameters have been associated with SCD. Applicable examples include late gadolinium enhancement (LGE), T1 relaxation times, and myocardial strain. This review evaluates the epidemiological aspects of SCD in NICM, the role of CMR for risk stratification, and resulting indications for ICD implantation.

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Multimodality imaging predictors of sudden cardiac death

Cited by 9 publications

References 111 publications

Cardiac Imaging for the Assessment of Left Atrial Mechanics Across Heart Failure Stages

Cardiac Imaging for the Assessment of Left Atrial Mechanics Across Heart Failure Stages

Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care

CMR-Based Risk Stratification of Sudden Cardiac Death and Use of Implantable Cardioverter–Defibrillator in Non-Ischemic Cardiomyopathy

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