Heart transplantation is the final treatment option for end-stage heart failure. In the United States, 70 pediatric patients die annually on the waitlist while 800 well-functioning organs get discarded. Concern for potential size-mismatch is one source of allograft waste and high waitlist mortality. Clinicians use the donor-recipient body weight (DRBW) ratio, a standalone metric, to evaluate allograft size-match. However, this body weight metric is far removed from cardiac anatomy and neglects an individual’s anatomical variations. This thesis body of work developed a novel virtual heart transplant fit assessment tool and investigated the tool’s clinical utility to help clinicians safely expand patient donor pools.

The tool allowed surgeons to take an allograft reconstruction and fuse it to a patient’s CT or MR medical image for virtual fit assessment. The allograft is either a reconstruction of the donor’s actual heart (from CT or MR images) or an analogue from a health heart library. The analogue allograft geometry is identified from gross donor parameters using a regression model build herein. The need for the regression model is donor images may not exist or they may not become available within the time-window clinicians have to make a provisional acceptance of an offer.

The tool’s assessment suggested > 20% of upper DRBW listings could have been increased at Phoenix Children’s Hospital (PCH). Upper DRBW listings in the UNOS national database was statistically smaller than at PCH (p-values: < 0.001). Delayed sternal closure and surgeon perceived complication variables had an association (p-value: 0.000016) with 9 of the 11 cases that surgeons had perceived fit-related complications had delayed closures (p-value: 0.034809).

A tool to assess allograft size-match has been developed. Findings warrant future preclinical and clinical prospective studies to further assess the tool’s clinical utility.

A specific type of Congenital Heart Defect (CHD) known as Coarctation (narrowing) of the Aorta (CoA) prevails in 10% of all CHD patients resulting in life-threatening conditions. Treatments involve limited medical therapy (i.e PGE1 therapy), but in majority of CoA cases, planned surgical treatments are very common. The surgical approach is dictated by the severity of the coarctation, by which the method of treatments is divided between minimally invasive and extensive invasive procedures. Modern diagnostic procedures allude to many disadvantages making it difficult for clinical practices to properly deliver an optimal form of care. Computational Fluid Dynamics (CFD) technique addresses these issues by providing new forms of diagnostic measures that is non-invasive, inexpensive, and more accurate compared to other evaluative devices. To explore further using the CFD based alternative diagnostic measure, this project aims to validate CFD techniques through in vitro studies that capture the fluid flow in anatomically accurate aortic structures. These studies combine particle image velocimetry and catheterization experimental techniques in order to provide a significant knowledge towards validation of fluid flow simulations.

The advent of medical imaging has enabled significant advances in pre-procedural planning, allowing cardiovascular anatomy to be visualized noninvasively before a procedure. However, absolute scale and tactile information are not conveyed in traditional pre-procedural planning based on images alone. This information deficit fails to completely prepare clinicians for complex heart repair, where surgeons must consider the varied presentations of cardiac morphology and malformations. Three-dimensional (3D) visualization and 3D printing provide a mechanism to construct patient-specific, scale models of cardiovascular anatomy that surgeons and interventionalists can examine prior to a procedure. In addition, the same patient-specific models provide a valuable resource for educating future medical professionals. Instead of looking at idealized images on a computer screen or pages from medical textbooks, medical students can review a life-like model of patient anatomy.



In cases where surgical repair is insufficient to return the heart to normal function, a patient may proceed to advanced heart failure, and a heart transplant may be required. Unfortunately, a finite number of available donor hearts are available. A mechanical circulatory support (MCS) device can be used to bridge the time between heart failure and reception of a donor heart. These MCS devices are typically constructed for the adult population. Accordingly, the size associated to the device is a limiting factor for small adults or pediatric patients who often have smaller thoracic measurements. While current eligibility criteria are based on correlative measurements, the aforementioned 3D visualization capabilities can be leveraged to accomplish patient-specific fit analysis.

The main objectives of the work presented in this dissertation were 1) to develop and evaluate an optimized process for 3D printing cardiovascular anatomy for surgical planning and medical education and 2) to develop and evaluate computational tools to assess MCS device fit in specific patients. The evaluations for objectives 1 and 2 were completed with a collection of qualitative and quantitative validations. These validations include case studies to illustrate meaningful, qualitative results as well as quantitative results from surgical outcomes. The latter results present the first quantitative supporting evidence, beyond anecdotal case studies, regarding the efficacy of 3D printing for pre-procedural planning; this data is suitable as pilot data for clinical trials. The products of this work were used to plan 200 cardiovascular procedures (including 79 cardiothoracic surgeries at Phoenix Children's Hospital), via 3D printed heart models and assess MCS device fit in 29 patients across 6 countries.