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- Genre: Doctoral Dissertation
Description
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.
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.
ContributorsRyan, Justin Robert (Author) / Frakes, David (Thesis advisor) / Collins, Daniel (Committee member) / LaBelle, Jeffrey (Committee member) / Pizziconi, Vincent (Committee member) / Pophal, Stephen (Committee member) / Arizona State University (Publisher)
Created2015
Description
Over the past fifty years, the development of sensors for biological applications has increased dramatically. This rapid growth can be attributed in part to the reduction in feature size, which the electronics industry has pioneered over the same period. The decrease in feature size has led to the production of microscale sensors that are used for sensing applications, ranging from whole-body monitoring down to molecular sensing. Unfortunately, sensors are often developed without regard to how they will be integrated into biological systems. The complexities of integration are underappreciated. Integration involves more than simply making electrical connections. Interfacing microscale sensors with biological environments requires numerous considerations with respect to the creation of compatible packaging, the management of biological reagents, and the act of combining technologies with different dimensions and material properties. Recent advances in microfluidics, especially the proliferation of soft lithography manufacturing methods, have established the groundwork for creating systems that may solve many of the problems inherent to sensor-fluidic interaction. The adaptation of microelectronics manufacturing methods, such as Complementary Metal-Oxide-Semiconductor (CMOS) and Microelectromechanical Systems (MEMS) processes, allows the creation of a complete biological sensing system with integrated sensors and readout circuits. Combining these technologies is an obstacle to forming complete sensor systems. This dissertation presents new approaches for the design, fabrication, and integration of microscale sensors and microelectronics with microfluidics. The work addresses specific challenges, such as combining commercial manufacturing processes into biological systems and developing microscale sensors in these processes. This work is exemplified through a feedback-controlled microfluidic pH system to demonstrate the integration capabilities of microscale sensors for autonomous microenvironment control.
ContributorsWelch, David (Author) / Blain Christen, Jennifer (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Frakes, David (Committee member) / LaBelle, Jeffrey (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2012
Description
Statistical process control (SPC) and predictive analytics have been used in industrial manufacturing and design, but up until now have not been applied to threshold data of vital sign monitoring in remote care settings. In this study of 20 elders with COPD and/or CHF, extended months of peak flow monitoring (FEV1) using telemedicine are examined to determine when an earlier or later clinical intervention may have been advised. This study demonstrated that SPC may bring less than a 2.0% increase in clinician workload while providing more robust statistically-derived thresholds than clinician-derived thresholds. Using a random K-fold model, FEV1 output was predictably validated to .80 Generalized R-square, demonstrating the adequate learning of a threshold classifier. Disease severity also impacted the model. Forecasting future FEV1 data points is possible with a complex ARIMA (45, 0, 49), but variation and sources of error require tight control. Validation was above average and encouraging for clinician acceptance. These statistical algorithms provide for the patient's own data to drive reduction in variability and, potentially increase clinician efficiency, improve patient outcome, and cost burden to the health care ecosystem.
ContributorsFralick, Celeste (Author) / Muthuswamy, Jitendran (Thesis advisor) / O'Shea, Terrance (Thesis advisor) / LaBelle, Jeffrey (Committee member) / Pizziconi, Vincent (Committee member) / Shea, Kimberly (Committee member) / Arizona State University (Publisher)
Created2013