Zachary Aaron Sexton

All Publications

• Personalized coronary and myocardial blood flow models incorporating CT perfusion imaging and synthetic vascular trees. Npj imaging Menon, K., Khan, M. O., Sexton, Z. A., Richter, J., Nguyen, P. K., Malik, S. B., Boyd, J., Nieman, K., Marsden, A. L. 2024; 2 (1): 9

  Abstract

  Computational simulations of coronary artery blood flow, using anatomical models based on clinical imaging, are an emerging non-invasive tool for personalized treatment planning. However, current simulations contend with two related challenges - incomplete anatomies in image-based models due to the exclusion of arteries smaller than the imaging resolution, and the lack of personalized flow distributions informed by patient-specific imaging. We introduce a data-enabled, personalized and multi-scale flow simulation framework spanning large coronary arteries to myocardial microvasculature. It includes image-based coronary anatomies combined with synthetic vasculature for arteries below the imaging resolution, myocardial blood flow simulated using Darcy models, and systemic circulation represented as lumped-parameter networks. We propose an optimization-based method to personalize multiscale coronary flow simulations by assimilating clinical CT myocardial perfusion imaging and cardiac function measurements to yield patient-specific flow distributions and model parameters. Using this proof-of-concept study on a cohort of six patients, we reveal substantial differences in flow distributions and clinical diagnosis metrics between the proposed personalized framework and empirical methods based purely on anatomy; these errors cannot be predicted a priori. This suggests virtual treatment planning tools would benefit from increased personalization informed by emerging imaging methods.

  View details for DOI 10.1038/s44303-024-00014-6

  View details for PubMedID 38706558

  View details for PubMedCentralID PMC11062925

• Computational approaches for mechanobiology in cardiovascular development and diseases. Current topics in developmental biology Brown, A. L., Sexton, Z. A., Hu, Z., Yang, W., Marsden, A. L. 2024; 156: 19-50

  Abstract

  The cardiovascular development in vertebrates evolves in response to genetic and mechanical cues. The dynamic interplay among mechanics, cell biology, and anatomy continually shapes the hydraulic networks, characterized by complex, non-linear changes in anatomical structure and blood flow dynamics. To better understand this interplay, a diverse set of molecular and computational tools has been used to comprehensively study cardiovascular mechanobiology. With the continual advancement of computational capacity and numerical techniques, cardiovascular simulation is increasingly vital in both basic science research for understanding developmental mechanisms and disease etiologies, as well as in clinical studies aimed at enhancing treatment outcomes. This review provides an overview of computational cardiovascular modeling. Beginning with the fundamental concepts of computational cardiovascular modeling, it navigates through the applications of computational modeling in investigating mechanobiology during cardiac development. Second, the article illustrates the utility of computational hemodynamic modeling in the context of treatment planning for congenital heart diseases. It then delves into the predictive potential of computational models for elucidating tissue growth and remodeling processes. In closing, we outline prevailing challenges and future prospects, underscoring the transformative impact of computational cardiovascular modeling in reshaping cardiovascular science and clinical practice.

  View details for DOI 10.1016/bs.ctdb.2024.01.006

  View details for PubMedID 38556423

• Personalized coronary and myocardial blood flow models incorporating CT perfusion imaging and synthetic vascular trees. medRxiv : the preprint server for health sciences Menon, K., Khan, M. O., Sexton, Z. A., Richter, J., Nieman, K., Marsden, A. L. 2023

  Abstract

  Computational simulations of coronary artery blood flow, using anatomical models based on clinical imaging, are an emerging non-invasive tool for personalized treatment planning. However, current simulations contend with two related challenges - incomplete anatomies in image-based models due to the exclusion of arteries smaller than the imaging resolution, and the lack of personalized flow distributions informed by patient-specific imaging. We introduce a data-enabled, personalized and multi-scale flow simulation framework spanning large coronary arteries to myocardial microvasculature. It includes image-based coronary models combined with synthetic vasculature for arteries below the imaging resolution, myocardial blood flow simulated using Darcy models, and systemic circulation represented as lumped-parameter networks. Personalized flow distributions and model parameters are informed by clinical CT myocardial perfusion imaging and cardiac function using surrogate-based optimization. We reveal substantial differences in flow distributions and clinical diagnosis metrics between the proposed personalized framework and empirical methods based on anatomy; these errors cannot be predicted a priori. This suggests virtual treatment planning tools would benefit from increased personalization informed by emerging imaging methods.

  View details for DOI 10.1101/2023.08.17.23294242

  View details for PubMedID 37645850

• Rapid model-guided design of organ-scale synthetic vasculature for biomanufacturing. ArXiv Sexton, Z. A., Hudson, A. R., Herrmann, J. E., Shiwarski, D. J., Pham, J., Szafron, J. M., Wu, S. M., Skylar-Scott, M., Feinberg, A. W., Marsden, A. 2023

  Abstract

  Our ability to produce human-scale bio-manufactured organs is critically limited by the need for vascularization and perfusion. For tissues of variable size and shape, including arbitrarily complex geometries, designing and printing vasculature capable of adequate perfusion has posed a major hurdle. Here, we introduce a model-driven design pipeline combining accelerated optimization methods for fast synthetic vascular tree generation and computational hemodynamics models. We demonstrate rapid generation, simulation, and 3D printing of synthetic vasculature in complex geometries, from small tissue constructs to organ scale networks. We introduce key algorithmic advances that all together accelerate synthetic vascular generation by more than 230 -fold compared to standard methods and enable their use in arbitrarily complex shapes through localized implicit functions. Furthermore, we provide techniques for joining vascular trees into watertight networks suitable for hemodynamic CFD and 3D fabrication. We demonstrate that organ-scale vascular network models can be generated in silico within minutes and can be used to perfuse engineered and anatomic models including a bioreactor, annulus, bi-ventricular heart, and gyrus. We further show that this flexible pipeline can be applied to two common modes of bioprinting with free-form reversible embedding of suspended hydrogels and writing into soft matter. Our synthetic vascular tree generation pipeline enables rapid, scalable vascular model generation and fluid analysis for bio-manufactured tissues necessary for future scale up and production.

  View details for PubMedID 37645046

  View details for PubMedCentralID PMC10462165

• Time From Authorization by the US Food and Drug Administration to Medicare Coverage for Novel Technologies. JAMA health forum Sexton, Z. A., Perl, J. R., Saul, H. R., Trotsyuk, A. A., Pietzsch, J. B., Ruggles, S. W., Nikolov, M. C., Schulman, K. A., Makower, J. 2023; 4 (8): e232260

  Abstract

  A wide variety of novel medical diagnostics and devices are determined safe and effective by the US Food and Drug Administration (FDA) each year, but to our knowledge the literature lacks evidence documenting how long it takes to establish new Medicare coverage for these technologies.To measure time from FDA authorization to at least nominal Medicare coverage for technologies requiring a new reimbursement pathway.In this cross-sectional study, public databases were used to associate each technology to billing codes, determine the effective date of each code and Medicare coverage decisions, and stratify by the maturity of the Medicare coverage. At least nominal coverage was defined as achievement of explicit coverage milestones through a national coverage determination, local coverage determinations by Medicare administrative contractors, or by implicit coverage aligned to a new billing code. Characterization by product type (acute treatment, chronic or ongoing treatment, diagnostic assay, and diagnostic device), manufacturer size, and evidence level were assessed for association with coverage achievement. The study included new product applications authorized by the FDA through the premarket approval pathway, the de novo pathway, or with breakthrough designation in the 510(k) pathway from January 1, 2016, to December 31, 2019. Data analysis took place between May 1, 2022, and December 31, 2022.Time from FDA authorization to the first coverage milestone.Among 281 identified technologies in the total sample, 64 (23%) were deemed novel technologies based on the absence of coverage determinations and/or the use of temporary or miscellaneous billing codes. Twenty-eight of 64 technologies (44%) successfully achieved explicit or implicit coverage following FDA authorization. The median time to at least nominal coverage for the analysis cohort was 5.7 years (90% CI, 4.4-NA years). Analysis of time-to-coverage data highlighted company size (log-rank P<.001) and product type (log-rank P = .01) as significant covariates associated with coverage achievement. No association was observed for technologies with level 1 evidence at FDA authorization and subsequent coverage milestone achievement (log-rank P = .40).In this cross-sectional study of 64 novel technologies, only 28 (44%) achieved coverage milestones over the study timeline. The several-year period observed to establish at least nominal coverage suggests existing coverage processes may affect timely reimbursement of new technologies.

  View details for DOI 10.1001/jamahealthforum.2023.2260

  View details for PubMedID 37540524

• A matched-pair case control study identifying hemodynamic predictors of cerebral aneurysm growth using computational fluid dynamics. Frontiers in physiology Weiss, A. J., Panduro, A. O., Schwarz, E. L., Sexton, Z. A., Lan, I. S., Geisbush, T. R., Marsden, A. L., Telischak, N. A. 2023; 14: 1300754

  Abstract

  Introduction: Initiation and progression of cerebral aneurysms is known to be driven by complex interactions between biological and hemodynamic factors, but the hemodynamic mechanism which drives aneurysm growth is unclear. We employed robust modeling and computational methods, including temporal and spatial convergence studies, to study hemodynamic characteristics of cerebral aneurysms and identify differences in these characteristics between growing and stable aneurysms. Methods: Eleven pairs of growing and non-growing cerebral aneurysms, matched in both size and location, were modeled from MRA and CTA images, then simulated using computational fluid dynamics (CFD). Key hemodynamic characteristics, including wall shear stress (WSS), oscillatory shear index (OSI), and portion of the aneurysm under low shear, were evaluated. Statistical analysis was then performed using paired Wilcoxon rank sum tests. Results: The portion of the aneurysm dome under 70% of the parent artery mean wall shear stress was higher in growing aneurysms than in stable aneurysms and had the highest significance among the tested metrics (p = 0.08). Other metrics of area under low shear had similar levels of significance. Discussion: These results align with previously observed hemodynamic trends in cerebral aneurysms, indicating a promising direction for future study of low shear area and aneurysm growth. We also found that mesh resolution significantly affected simulated WSS in cerebral aneurysms. This establishes that robust computational modeling methods are necessary for high fidelity results. Together, this work demonstrates that complex hemodynamics are at play within cerebral aneurysms, and robust modeling and simulation methods are needed to further study this topic.

  View details for DOI 10.3389/fphys.2023.1300754

  View details for PubMedID 38162830

• In vitro comparison of everting vs. non-everting suture techniques for the implantation of a supra-annular biological heart valve. Journal of thoracic disease Puluca, N., Münsterer, A., Prinzing, A., Sexton, Z. A., Lange, R., Meyer-Saraei, R., Scharfschwerdt, M. 2020; 12 (5): 2443-2449

  Abstract

  The aim of this study was to evaluate the hemodynamic effect of different suturing techniques for aortic valve replacement (AVR) in vitro. Whether or not the applied suturing technique impacts the outflow tract diameter by narrowing the annulus diameter was examined.The commonly applied non-everting pledget forced suture technique (NE, n=13) was compared with an everting pledget forced suture (ET, n=13) for AVR using the 25 mm St. Jude Trifecta aortic valve. Hemodynamic parameters were obtained in a pulsatile flow simulator. A high speed camera captured the visual aspects of the suturing technique.Despite some kind of left ventricular outflow narrowing due to protruding pledgets using the NE suture technique, mean pressure gradients of both techniques were nearly similar (NE 5.88±2.7 mmHg, ET 5.23±1.31 mmHg, P=0.44). Closing volume (NE 3.16±0.48 mL; ET 3.51±0.68 mL; P=0.14) and the leakage volume (NE: 8.09±2.53 mL; ET: 8.35±3.65 mL; P=0.83) also showed no differences.AVR using either suturing techniques leads to a similar hemodynamic performance in vitro. The impact of the suturing technique may be higher in a smaller annulus. Therefore, further studies using smaller prostheses are necessary.

  View details for DOI 10.21037/jtd.2020.03.55

  View details for PubMedID 32642150

  View details for PubMedCentralID PMC7330396

• Connecting Theoretical Concepts to Physical Phenomena Using 3-D-printed Microfluidic Devices ASEE Annual Conference & Exposition Rooney, S. ., Sariano, P. A., Sexton, Z. A., Stewart, W. G., Guidry, K. R., Gleghorn, J. American Society for Engineering Education Peer. 2018: 17