Circulation. Cardiovascular imaging
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Circ Cardiovasc Imaging · Jun 2020
Meta AnalysisDiagnosis of Infective Endocarditis by Subtype Using 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography: A Contemporary Meta-Analysis.
Background Infective endocarditis (IE) remains a difficult to diagnose condition associated with high mortality. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) has recently emerged as another IE imaging modality, although diagnostic accuracy varies across observational studies and types of IE. This meta-analysis assessed the diagnostic performance of 18F-FDG PET/CT for IE and its subtypes. Methods We searched Pubmed, Cochrane, and Embase from January 1980 to September 2019 for studies reporting both sensitivity and specificity of 18F-FDG PET/CT for IE. ⋯ Pooled sensitivities and specificities were higher for the 17 studies since 2015 than the 9 studies published before 2015. Conclusions 18F-FDG PET/CT had high specificity for all IE subtypes; however, sensitivity was markedly lower for native valve IE than prosthetic valve IE and cardiac implantable electronic devices IE. It is, therefore, a useful adjunct modality for assessing endocarditis, especially in the challenging scenarios of prosthetic valve IE and cardiac implantable electronic devices IE, with improving performance over time, related to advances in 18F-FDG PET/CT techniques.
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Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. ⋯ Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging.
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Circ Cardiovasc Imaging · Dec 2015
ReviewImproving Appropriateness and Quality in Cardiovascular Imaging: A Review of the Evidence.
High-quality cardiovascular imaging requires a structured process to ensure appropriate patient selection, accurate and reproducible data acquisition, and timely reporting which answers clinical questions and improves patient outcomes. Several guidelines provide frameworks to assess quality. This article reviews interventions to improve quality in cardiovascular imaging, including methods to reduce inappropriate testing, improve accuracy, reduce interobserver variability, and reduce diagnostic and reporting errors.
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Circ Cardiovasc Imaging · Mar 2014
Review Meta AnalysisLate gadolinium enhancement on cardiac magnetic resonance predicts adverse cardiovascular outcomes in nonischemic cardiomyopathy: a systematic review and meta-analysis.
Late gadolinium enhancement (LGE) by cardiac MR (CMR) is a predictor of adverse cardiovascular outcomes in patients with nonischemic cardiomyopathy (NICM). However, these findings are limited by single-center studies, small sample sizes, and low event rates. We performed a meta-analysis to evaluate the prognostic role of LGE by CMR (LGE-CMR) imaging in patients with NICM. ⋯ LGE in patients with NICM is associated with increased risk of all-cause mortality, heart failure hospitalization, and SCD. Detection of LGE by CMR has excellent prognostic characteristics and may help guide risk stratification and management in patients with NICM.