Regardless of AF history, there is broad variation in LAA morphology, anatomical relationships, dimensions, angulation, and motility. These observations may have importance for the development of technologies for therapy delivery in this region.
Auto EF can automatically calculate EF similarly to results by manual biplane Simpson's rule and MRI, with less variability than visual EF, and has clinical potential.
We report on a 22-year-old male carrying a presumptive clinical diagnosis of Dubowitz-like phenotype who has been followed-up by cardiology for bicuspid aortic valve with ascending aorta and aortic root dilatation. Cardiac magnetic resonance imaging (CMRI) confirmed these findings, along with an incidental finding of left ventricular non-compaction (LVNC). Genetic workup revealed the diagnosis of 22q11.2 distal deletion encompassing the BCR gene. This is the first time LVNC has been reported in a patient with 22q11.2 distal deletion.
Residual pulmonary insufficiency in post-repair Tetralogy of Fallot (rToF) patients often mediates biventricular dysfunction which is associated with long-term adverse clinical outcomes. The objective of this study was to demonstrate the presence of impaired left ventricle (LV) circumferential strain (CS) in pediatric rToF patients as compared to controls using cardiac magnetic resonance imaging (CMRI). Additionally, bivariate analysis between right ventricle (RV) and LV functional measures in rToF patients was performed to further characterize the interventricular interactions thought to mediate LV dysfunction secondary to RV volume overload. The medical records of 12 rToF patients (mean age 13.3 years) and 9 controls (mean age 10.9 years) were analyzed. LV global CS was significantly decreased in rToF patients versus controls (p = 0.04). This impairment was differentially distributed within the LV, with only the LV anterior and anterior lateral walls significantly decreased versus controls (p = 0.04, p = 0.03). Bivariate analysis revealed a significant correlation between RV mean CS and LV EF (r = 0.71, p = 0.01), RV infundibulum CS and LV EF (r = 0.70, p = 0.01), RV infundibulum CS and LV anterolateral wall CS (r = 0.59, p = 0.04), and RV infundibulum CS and pulmonary regurgitation fraction (r = -0.63, p = 0.03). These findings support existing research implicating interventricular interactions in the development of LV dysfunction. Furthermore, the segment specific CS impairment in the LV suggests a possible spatial component to these interactions. The success of this study in identifying regional myocardial strain impairment indicates CMRI based techniques may be useful in localizing otherwise undetectable myocardial dysfunction.
BackgroundPediatric cardiomyopathies are a rare, yet heterogeneous group of pathologies of the myocardium that are routinely examined clinically using Cardiovascular Magnetic Resonance Imaging (cMRI). This gold standard powerful non-invasive tool yields high resolution temporal images that characterize myocardial tissue. The complexities associated with the annotation of images and extraction of markers, necessitate the development of efficient workflows to acquire, manage and transform this data into actionable knowledge for patient care to reduce mortality and morbidity.MethodsWe develop and test a novel informatics framework called cMRI-BED for biomarker extraction and discovery from such complex pediatric cMRI data that includes the use of a suite of tools for image processing, marker extraction and predictive modeling. We applied our workflow to obtain and analyze a dataset of 83 de-identified cases and controls containing cMRI-derived biomarkers for classifying positive versus negative findings of cardiomyopathy in children. Bayesian rule learning (BRL) methods were applied to derive understandable models in the form of propositional rules with posterior probabilities pertaining to their validity. Popular machine learning methods in the WEKA data mining toolkit were applied using default parameters to assess cross-validation performance of this dataset using accuracy and percentage area under ROC curve (AUC) measures.ResultsThe best 10-fold cross validation predictive performance obtained on this cMRI-derived biomarker dataset was 80.72% accuracy and 79.6% AUC by a BRL decision tree model, which is promising from this type of rare data. Moreover, we were able to verify that mycocardial delayed enhancement (MDE) status, which is known to be an important qualitative factor in the classification of cardiomyopathies, is picked up by our rule models as an important variable for prediction.ConclusionsPreliminary results show the feasibility of our framework for processing such data while also yielding actionable predictive classification rules that can augment knowledge conveyed in cardiac radiology outcome reports. Interactions between MDE status and other cMRI parameters that are depicted in our rules warrant further investigation and validation. Predictive rules learned from cMRI data to classify positive and negative findings of cardiomyopathy can enhance scientific understanding of the underlying interactions among imaging-derived parameters.
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