Integrated Instrumentation and Multivariate Index Assay System for Cardiovascular Health Assessment at the Point-of-Care

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dc.contributor.advisorMcDevitt, John Ten_US
dc.creatorMcRae, Michaelen_US
dc.date.accessioned2017-08-03T15:25:41Zen_US
dc.date.available2017-08-03T15:25:41Zen_US
dc.date.created2016-05en_US
dc.date.issued2016-04-20en_US
dc.date.submittedMay 2016en_US
dc.date.updated2017-08-03T15:25:41Zen_US
dc.description.abstractCardiovascular disease (CVD) is the leading cause of death in the United States and globally, accounting for approximately one in three deaths. Early detection and frequent monitoring of traditional risk factors and biomarkers is necessary to save lives and reduce unnecessary costs due to CVD morbidity and mortality. This dissertation aims to develop new tools and processes that can contribute more effective means to assist in prevention and early intervention at the point-of-care through integrated instrumentation and predictive models for CVD. The Programmable Bio-Nano-Chip (p-BNC) is a platform to digitize biology in which small quantities of patient sample generate immunofluorescent signal that is optically extracted and converted to antigen concentrations. A novel fluid delivery method was developed to provide accurate and repeatable flow rates via actuation of the p-BNC cartridge’s blister packs. A portable analyzer instrument was designed and fabricated so as to facilitate fluid delivery, optical detection, automated data analysis, and intuitive mobile health interfaces, representing a universal system for acquiring, processing, and managing clinical data streams and overcoming many of the challenges facing the widespread clinical adoption of lab-on-a-chip technologies. The p-BNC system is also a biosensor system that learns—a diagnostic device that implements machine-learning algorithms for diagnosis and prognosis for a variety of disease applications. In this capacity, a Cardiac ScoreCard system was developed to predict a spectrum of CVD using multiplexed biomarker measurements, symptoms, medical history, and demographics. The combination of a platform to digitize biology and predictive analytics has the potential to alter the trajectory of medicine, where the current linear thinking—mainly based on late-stage disease diagnosis using expensive and cumbersome tools—is replaced by a pathway to exponential medicine made possible through the introduction of scalable tools with the capacity to learn.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationMcRae, Michael. "Integrated Instrumentation and Multivariate Index Assay System for Cardiovascular Health Assessment at the Point-of-Care." (2016) Diss., Rice University. <a href="https://hdl.handle.net/1911/96539">https://hdl.handle.net/1911/96539</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/96539en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectProgrammable bio-nano-chip (p-BNC)en_US
dc.subjectmachine learningen_US
dc.subjectpoint-of-care diagnosticsen_US
dc.subjectlab on a chipen_US
dc.subjectCardiac ScoreCarden_US
dc.titleIntegrated Instrumentation and Multivariate Index Assay System for Cardiovascular Health Assessment at the Point-of-Careen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentBioengineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophyen_US
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