A computational study of aortic insufficiency in patients supported with continuous flow left ventricular assist devices: Is it time for a paradigm shift in management?

Background: De novo aortic insufficiency (AI) following continuous flow left ventricular assist device (CF-LVAD) implantation is a common complication. Traditional early management utilizes speed augmentation to overcome the regurgitant flow in an attempt to augment net forward flow, but this strategy increases the aortic transvalvular gradient which predisposes the patient to progressive aortic valve pathology and may have deleterious effects on aortic shear stress and right ventricular (RV) function. Materials and methods: We employed a closed-loop lumped-parameter mathematical model of the cardiovascular system including the four cardiac chambers with corresponding valves, pulmonary and systemic circulations, and the LVAD. The model is used to generate boundary conditions which are prescribed in blood flow simulations performed in a three-dimensional (3D) model of the ascending aorta, aortic arch, and thoracic descending aorta. Using the models, impact of various patient management strategies, including speed augmentation and pharmacological treatment on systemic and pulmonary (PA) vasculature, were investigated for four typical phenotypes of LVAD patients with varying degrees of RV to PA coupling and AI severity. Results: The introduction of mild/moderate or severe AI to the coupled RV and pulmonary artery at a speed of 5,500 RPM led to a reduction in net flow from 5.4 L/min (no AI) to 4.5 L/min (mild/moderate) to 2.1 L/min (severe). RV coupling ratio (Ees/Ea) decreased from 1.01 (no AI) to 0.96 (mild/moderate) to 0.76 (severe). Increasing LVAD speed to 6,400 RPM in the severe AI and coupled scenario, led to a 42% increase in net flow and a 16% increase in regurgitant flow (RF) with a nominal decrease of 1.6% in RV myocardial oxygen consumption (MVO2). Blood pressure control with the coupled RV with severe AI at 5,500 RPM led to an 81% increase in net flow with a 15% reduction of RF and an 8% reduction in RV MVO2. With an uncoupled RV, the introduction of mild/moderate or severe AI at a speed of 5,500 RPM led to a reduction in net flow from 5.0 L/min (no AI) to 4.0 L/min (mild/moderate) to 1.8 L/min (severe). Increasing the speed to 6,400 RPM with severe AI and an uncoupled RV increased net flow by 45%, RF by 15% and reduced RV MVO2 by 1.1%. For the uncoupled RV with severe AI, blood pressure control alone led to a 22% increase in net flow, 4.2% reduction in RF, and 3.9% reduction in RV MVO2; pulmonary vasodilation alone led to a 18% increase in net flow, 7% reduction in RF, and 26% reduction in RV MVO2; whereas, combined BP control and pulmonary vasodilation led to a 113% increase in net flow, 20% reduction in RF and 31% reduction in RV MVO2. Compared to speed augmentation, blood pressure control consistently resulted in a reduction in WSS throughout the proximal regions of the arterial system. Conclusion: Speed augmentation to overcome AI in patients supported by CF-LVAD appears to augment flow but also increases RF and WSS in the aorta, and reduces RV MVO2. Aggressive blood pressure control and pulmonary vasodilation, particularly in those patients with an uncoupled RV can improve net flow with more advantageous effects on the RV and AI RF

Localization of the thyrotropin-releasing hormone gene, Trh , on mouse Chromosome 6

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47026/1/335_2004_Article_BF00355651.pd

Automated lumen segmentation using multi-frame convolutional neural networks in Intravascular Ultrasound datasets

Aims: Assessment of minimum lumen areas in intravascular ultrasound (IVUS) pullbacks is time-consuming and demands adequately trained personnel. In this work, we introduce a novel and fully automated pipeline to segment the lumen boundary in IVUS datasets. Methods and results First, an automated gating is applied to select end-diastolic frames and bypass saw-tooth artefacts. Second, within a machine learning (ML) environment, we automatically segment the lumen boundary using a multi-frame (MF) convolutional neural network (MFCNN). Finally, we use the theory of Gaussian processes (GPs) to regress the final lumen boundary. The dataset consisted of 85 IVUS pullbacks (52 patients). The dataset was partitioned at the pullback-level using 73 pullbacks for training (20 586 frames), 6 pullbacks for validation (1692 frames), and 6 for testing (1692 frames). The degree of overlapping, between the ground truth and ML contours, median (interquartile range, IQR) systematically increased from 0.896 (0.874–0.933) for MF1 to 0.925 (0.911–0.948) for MF11. The median (IQR) of the distance error was also reduced from 3.83 (2.94–4.98)% for MF1 to 3.02 (2.25–3.95)% for MF11-GP. The corresponding median (IQR) in the lumen area error remained between 5.49 (2.50–10.50)% for MF1 and 5.12 (2.15–9.00)% for MF11-GP. The dispersion in the relative distance and area errors consistently decreased as we increased the number of frames, and also when the GP regressor was coupled to the MFCNN output. Conclusion: These results demonstrate that the proposed ML approach is suitable to effectively segment the lumen boundary in IVUS scans, reducing the burden of costly and time-consuming manual delineation.Fil: Ziemer, Paulo G. P.. Laboratorio Nacional de Computacao Cientifica; BrasilFil: Bulant, Carlos Alberto. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; ArgentinaFil: Orlando, José Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; ArgentinaFil: Maso Talou, Gonzalo D.. University of Auckland; Nueva ZelandaFil: Mansilla Álvarez, Luis A.. Laboratorio Nacional de Computacao Cientifica; BrasilFil: Guedes Bezerra, Cristiano. Universidade de Sao Paulo; BrasilFil: Lemos, Pedro A.. Universidade de Sao Paulo; BrasilFil: García García, Héctor M.. Georgetown University School of Medicine; Estados UnidosFil: Blanco, Pablo J.. Laboratorio Nacional de Computacao Cientifica; Brasi

500.05 Comparison Between Fractional Flow Reserve (FFR) vs. Computational Fractional Flow Reserve Derived from Three-dimensional Intravascular Ultrasound (IVUSFR) and Quantitative Flow Ratio (QFR)

BACKGROUND The determination of the ischemic status of a coronary artery by wireless physiologic assessment derived from angiography has been validated and approved in the US. However, the use ofplain angiography quantitative variables does not add much to thephysiology data since it has low correlation with fractional flowreserve (FFR) and predicts clinical outcomes poorly. Recently, a grayscale intravascular ultrasound (IVUS) derived physiology method(IVUSFR) was developed and showed a good correlation with invasiveFFR by combining the geometric advantages of IVUS with physiology.The aim of this study is to assess the coefficient of correlation (R) ofinvasive FFR compared to IVUSFR and quantitative flow ratio (QFR).METHODS Stable coronary artery disease (CAD) patients with intermediate lesions (i.e. 40?80% of diameter stenosis) were assessed by angiography and IVUS. QFR was derived from the angiography images, andIVUSFR was derived from quantitative IVUS data using computationalfluid dynamics. Coefficient of correlation (R) was used in this report.RESULTS Twenty-four patients with 34 lesions were included in theanalysis. The IVUSFR, invasive FFR, Vessel QFR fixed flow (vQFRf),and Vessel QFR contrast flow (vQFRc) values varied from 0.52 to 1.00,0.71 to 0.99, 0.55 to 1.00, and 0.34 to 1.00, respectively. The coefficient of correlation (R) of FFR vs. IVUSFR was 0.79; FFR vs. vQFRf was0.72; FFR vs. vQFRc was 0.65 (Figure).CONCLUSION Compared to invasive FFR, IVUSFR and vQFRf showed asimilar coefficient of correlation and were better than vQFR contrast flowFil: Kajita, Alexandre. Medstart; Estados UnidosFil: Bezerra, Cristiano Guedes. Universidade Federal da Bahia; BrasilFil: Ozaki, Yuichi. Medstart; Estados UnidosFil: Dan, Kazuhiro. Medstart; Estados UnidosFil: Melaku, Gebremedhin D.. Medstart; Estados UnidosFil: Pinton, Fabio A.. Universidade de Sao Paulo; BrasilFil: Falcão, Breno A. A.. Hospital of Messejana; BrasilFil: Mariani, José. Universidade de Sao Paulo; BrasilFil: Bulant, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. National Laboratory For Scientific Computing; BrasilFil: Maso Talou, Gonzalo Daniel. National Laboratory For Scientific Computing; BrasilFil: Esteves, Antonio. Universidade de Sao Paulo; BrasilFil: Blanco, Pablo Javier. National Laboratory For Scientific Computing; BrasilFil: Waksman, Ron. Medstart; Estados UnidosFil: Garcia Garcia, Hector M.. Medstart; Estados UnidosFil: Lemons, Pedro Alves. Universidade de Sao Paulo; Brasi

Common Genetic Variation And Age at Onset Of Anorexia Nervosa

Background Genetics and biology may influence the age at onset of anorexia nervosa (AN). The aims of this study were to determine whether common genetic variation contributes to AN age at onset and to investigate the genetic associations between age at onset of AN and age at menarche. Methods A secondary analysis of the Psychiatric Genomics Consortium genome-wide association study (GWAS) of AN was performed which included 9,335 cases and 31,981 screened controls, all from European ancestries. We conducted GWASs of age at onset, early-onset AN (< 13 years), and typical-onset AN, and genetic correlation, genetic risk score, and Mendelian randomization analyses. Results Two loci were genome-wide significant in the typical-onset AN GWAS. Heritability estimates (SNP-h2) were 0.01-0.04 for age at onset, 0.16-0.25 for early-onset AN, and 0.17-0.25 for typical-onset AN. Early- and typical-onset AN showed distinct genetic correlation patterns with putative risk factors for AN. Specifically, early-onset AN was significantly genetically correlated with younger age at menarche, and typical-onset AN was significantly negatively genetically correlated with anthropometric traits. Genetic risk scores for age at onset and early-onset AN estimated from independent GWASs significantly predicted age at onset. Mendelian randomization analysis suggested a causal link between younger age at menarche and early-onset AN. Conclusions Our results provide evidence consistent with a common variant genetic basis for age at onset and implicate biological pathways regulating menarche and reproduction.Peer reviewe