Publications by authors named "J Flewitt"
Article Synopsis
- The study explores using multi-phase Computed Tomography Angiography (mpCTA) for better assessing cardiac health before transcatheter aortic valve replacement (TAVR) and predicting patient outcomes.
- Researchers analyzed mpCTAs from 205 patients and found that 96% could be assessed, with specific deformation measurements indicating higher risks of complications like heart failure or death.
- The findings suggest that advanced 4D modeling techniques can predict outcomes post-TAVR effectively, and further validation in multiple centers is planned.
Article Synopsis
- The study aimed to compare cardiovascular characteristics and outcomes between male and female patients with idiopathic non-ischaemic cardiomyopathy (NICM).
- Researchers analyzed data from the Cardiovascular Imaging Registry of Calgary, finding that females exhibited higher heart function measures and less fibrosis than males but had similar rates of adverse outcomes over time.
- Despite sex differences in heart structure and function, both sexes had comparable long-term prognoses for NICM, indicating that underlying mechanisms might differ, but outcomes do not.
Background The prognostic utility of cardiovascular magnetic resonance imaging, including strain analysis and tissue characterization, has not been comprehensively investigated in adult patients with muscular dystrophy. Methods and Results We prospectively enrolled 148 patients with dystrophinopathies (including heterozygotes), limb-girdle muscular dystrophy, and type 1 myotonic dystrophy (median age, 36.0 [interquartile range, 23.
View Article and Find Full Text PDF3-Dimensional (3D) myocardial deformation analysis (3D-MDA) enables novel descriptions of geometry-independent principal strain (PS). Applied to routine 2D cine cardiovascular magnetic resonance (CMR), this provides unique measures of myocardial biomechanics for disease diagnosis and prognostication. However, healthy reference values remain undefined.
View Article and Find Full Text PDFBackground: Atrial fibrillation (AF) is a commonly encountered cardiac arrhythmia associated with morbidity and substantial healthcare costs. While patients with cardiovascular disease experience the greatest risk of new-onset AF, no risk model has been developed to predict AF occurrence in this population. We hypothesized that a patient-specific model could be delivered using cardiovascular magnetic resonance (CMR) disease phenotyping, contextual patient health information, and machine learning.
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