All Publications

  • Patient-Specific Computational Fluid Dynamics Reveal Localized Flow Patterns Predictive of Post-Left Ventricular Assist Device Aortic Incompetence. Circulation. Heart failure Shad, R., Kaiser, A. D., Kong, S., Fong, R., Quach, N., Bowles, C., Kasinpila, P., Shudo, Y., Teuteberg, J., Woo, Y. J., Marsden, A. L., Hiesinger, W. 2021: CIRCHEARTFAILURE120008034


    BACKGROUND: Progressive aortic valve disease has remained a persistent cause of concern in patients with left ventricular assist devices. Aortic incompetence (AI) is a known predictor of both mortality and readmissions in this patient population and remains a challenging clinical problem.METHODS: Ten left ventricular assist device patients with de novo aortic regurgitation and 19 control left ventricular assist device patients were identified. Three-dimensional models of patients' aortas were created from their computed tomography scans, following which large-scale patient-specific computational fluid dynamics simulations were performed with physiologically accurate boundary conditions using the SimVascular flow solver.RESULTS: The spatial distributions of time-averaged wall shear stress and oscillatory shear index show no significant differences in the aortic root in patients with and without AI (mean difference, 0.67 dyne/cm2 [95% CI, -0.51 to 1.85]; P=0.23). Oscillatory shear index was also not significantly different between both groups of patients (mean difference, 0.03 [95% CI, -0.07 to 0.019]; P=0.22). The localized wall shear stress on the leaflet tips was significantly higher in the AI group than the non-AI group (1.62 versus 1.35 dyne/cm2; mean difference [95% CI, 0.15-0.39]; P<0.001), whereas oscillatory shear index was not significantly different between both groups (95% CI, -0.009 to 0.001; P=0.17).CONCLUSIONS: Computational fluid dynamics serves a unique role in studying the hemodynamic features in left ventricular assist device patients where 4-dimensional magnetic resonance imaging remains unfeasible. Contrary to the widely accepted notions of highly disturbed flow, in this study, we demonstrate that the aortic root is a region of relatively stagnant flow. We further identified localized hemodynamic features in the aortic root that challenge our understanding of how AI develops in this patient population.

    View details for DOI 10.1161/CIRCHEARTFAILURE.120.008034

    View details for PubMedID 34139862

  • Computational fluid dynamics simulations to predict false lumen enlargement after surgical repair of Type-A aortic dissection. Seminars in thoracic and cardiovascular surgery Shad, R., Kong, S., Fong, R., Quach, N., Kasinpila, P., Bowles, C., Lee, A., Hiesinger, W. 2021


    We aim to use computational fluid dynamics to investigate the hemodynamic conditions that may predispose to false lumen enlargement in this patient population. Nine patients who received surgical repairs of their type-A aortic dissections between 2017-2018 were retrospectively identified. Multiple contrast-enhanced post-operative CT scans were used to construct 3D models of aortic geometries. Computational fluid dynamics simulations of the models were run on a high-performance computing cluster using SimVascular - an open source simulation package. Physiological pulsatile flow conditions (4.9 L/min) were used at the aortic true lumen inlet, and physiological vascular resistances were applied at the distal vascular ends. Exploratory analyses showed no correlation between rate of false lumen growth and blood pressure, immediate post-op aortic diameter, or the number of fenestrations (p = 0.2). 1-year post-operative CT scans showed a median (IQR) false lumen growth rate of 4.31 (3.66, 14.67) mm/year Median (Interquartile range) peak systolic, mid-diastolic, and late diastolic velocity magnitudes were 0.90 (1.40); 0.10 (0.16); and 0.06 (0.06) cm/s respectively. Spearman's ranked correlations between fenestration velocity and 1-year false lumen growth rates were found to be statistically significant: Velocity magnitude at peak systolic (p = 0.025; rho = 0.75), mid diastolic (p = 0.025; rho = 0.75) and late diastolic phases of the cardiac cycle (p = 0.006; rho = 0.85). We have shown that false lumen growth is strongly correlated to fenestration flow velocity, which has potential implications for post-operative surveillance and risk stratification.

    View details for DOI 10.1053/j.semtcvs.2021.05.012

    View details for PubMedID 34091015

  • Long-term survival in patients with post-LVAD right ventricular failure: multi-state modelling with competing outcomes of heart transplant. The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation Shad, R., Fong, R., Quach, N., Bowles, C., Kasinpila, P., Li, M., Callon, K., Castro, M., Guha, A., Suarez, E. E., Lee, S., Jovinge, S., Boeve, T., Shudo, Y., Langlotz, C. P., Teuteberg, J., Hiesinger, W. 2021


    BACKGROUND: Multicenter data on long term survival following LVAD implantation that make use of contemporary definitions of RV failure are limited. Furthermore, traditional survival analyses censor patients who receive a bridge to heart transplant. Here we compare the outcomes of LVAD patients who develop post-operative RV failure accounting for the transitional probability of receiving an interim heart transplantation.METHODS: We use a retrospective cohort of LVAD patients sourced from multiple high-volume centers based in the United States. Five- and ten-year survival accounting for transition probabilities of receiving a heart transplant were calculated using a multi-state Aalen Johansen survival model.RESULTS: Of the 897 patients included in the study, 238 (26.5%) developed post-operative RV failure at index hospitalization. At 10 years the probability of death with post-op RV failure was 79.28% vs 61.70% in patients without (HR 2.10; 95% CI 1.72 - 2.57; p=< .001). Though not significant, patients with RV failure were less likely to be bridged to a heart transplant (HR 0.87, p=.4). Once transplanted the risk of death between both patient groups remained equivalent; the probability of death after a heart transplant was 3.97% in those with post-operative RV failure shortly after index LVAD implant, as compared to 14.71% in those without.CONCLUSIONS AND RELEVANCE: Long-term durable mechanical circulatory support is associated with significantly higher mortality in patients who develop post-operative RV failure. Improving outcomes may necessitate expeditious bridge to heart transplant wherever appropriate, along with critical reassessment of organ allocation policies.

    View details for DOI 10.1016/j.healun.2021.05.002

    View details for PubMedID 34167863

  • Predicting post-operative right ventricular failure using video-based deep learning. Nature communications Shad, R., Quach, N., Fong, R., Kasinpila, P., Bowles, C., Castro, M., Guha, A., Suarez, E. E., Jovinge, S., Lee, S., Boeve, T., Amsallem, M., Tang, X., Haddad, F., Shudo, Y., Woo, Y. J., Teuteberg, J., Cunningham, J. P., Langlotz, C. P., Hiesinger, W. 2021; 12 (1): 5192


    Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. All modern echocardiography artificial intelligence (AI) systems are similarly limited by design - automating measurements of the same reductionist metrics rather than utilizing the embedded wealth of data. This underutilization is most evident where clinical decision making is guided by subjective assessments of disease acuity. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such example. Here we describe a video AI system trained to predict post-operative RV failure using the full spatiotemporal density of information in pre-operative echocardiography. We achieve an AUC of 0.729, and show that this ML system significantly outperforms a team of human experts at the same task on independent evaluation.

    View details for DOI 10.1038/s41467-021-25503-9

    View details for PubMedID 34465780

  • Isomerization of N-Allyl Amides To Form Geometrically Defined Di-, Tri-, and Tetrasubstituted Enamides JOURNAL OF THE AMERICAN CHEMICAL SOCIETY Trost, B. M., Cregg, J. J., Quach, N. 2017; 139 (14): 5133-5139


    Enamides represent bioactive pharmacophores in various natural products, and have become increasingly common reagents for asymmetric incorporation of nitrogen functionality. Yet the synthesis of the requisite geometrically defined enamides remains problematic, especially for highly substituted and Z-enamides. Herein we wish to report a general atom economic method for the isomerization of a broad range of N-allyl amides to form Z-di-, tri-, and tetrasubstituted enamides with exceptional geometric selectivity. This report represents the first examples of a catalytic isomerization of N-allyl amides to form nonpropenyl disubstituted, tri- and tetrasubstituted enamides with excellent geometric control. Applications of these geometrically defined enamides toward the synthesis of cis vicinal amino alcohols and tetrasubstituted α-borylamido complexes are discussed.

    View details for DOI 10.1021/jacs.7b00564

    View details for Web of Science ID 000399353800028

    View details for PubMedID 28252296

  • Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments. PLoS computational biology Van Valen, D. A., Kudo, T., Lane, K. M., Macklin, D. N., Quach, N. T., DeFelice, M. M., Maayan, I., Tanouchi, Y., Ashley, E. A., Covert, M. W. 2016; 12 (11)


    Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

    View details for DOI 10.1371/journal.pcbi.1005177

    View details for PubMedID 27814364

    View details for PubMedCentralID PMC5096676