Browsing by Subject "Heart disease"
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Item Assessing the psychosocial risk factors for coronary artery disease: an investigation of predictive validity for the psychosocial inventory for cardiovascular illness(2009-08) Baker, Maria Kathryn; McCarthy, Christopher J.This dissertation investigated the psychometric properties and clinical applications of the Psychosocial Inventory for Cardiovascular Illness (PICI). The PICI is an inventory developed to measure the psychosocial risk factors for heart disease including anxiety, depression, stress, social isolation, and anger. The inventory was developed to measure the ways that each psychosocial risk factor contributes to the coronary artery disease process through the lifestyle behaviors and pathophysiological mechanisms with which they are associated. The primary purpose of the study was to examine predictive validity for the PICI. With support for predictive validity, the inventory may aid in early identification of individuals at increased risk for coronary artery disease (CAD) so that behavioral, psychosocial, and medical interventions can be implemented. Both healthy and cardiac samples were used in the inventory development and validation process. The PICI was administered in conjunction with similar inventories and physiological markers of CAD were collected including percent of coronary artery blockage and history of heart attacks. Item analysis and factor analysis were used to yield a 20-item PICI comprised of three subscales to include Negative Affect, Social Isolation, and Anger. It was hypothesized that the PICI subscales would predict group membership; whether or not a participant carried a diagnosis of CAD, and would be have a strong relationship to the physiological markers of CAD that were measured. Analysis revealed that the PICI was unable to predict diagnostic status and did not have a strong relationship with the physiological markers of CAD. Results suggest that the PICI has acceptable reliability and construct validity as demonstrated in the current sample, yet further investigation must be conducted to gain support for the instrument’s predictive abilities.Item Byheart : a personalized heart-health companion(2018-05) Barve, Ajinkya S.; Gorman, CarmaThe number of people living with some form of chronic cardiovascular disease is growing worldwide. Studies have shown that people often lack sufficient information about their heart condition. This issue can be addressed by improving heart health literacy and by helping those with a chronic heart disease to understand and manage their condition so that they are empowered to make informed decisions regarding their health. Although many existing online resources provide heart-health-related information, most fall short on providing actionable content—namely, nudges, prompts, reminders, and tracking features—that would help heart patients and their families make that information actionable in ways that would improve health outcomes. Byheart is a personalized heart health web and mobile companion that helps heart patients and their caregivers understand, track, and more effectively manage chronic heart conditions.Item Development of artificial intelligence methods for optical coherence tomography based medical diagnostic applications(2020-12-11) Baruah, Vikram Lal; Rylander, H. Grady (Henry Grady), 1948-; Milner, Thomas E.; Markey, Mia; Vargas, Gracie; Feldman, MarcPrimary challenges with early disease diagnosis are the lack of high-quality data and complexity of available data. Early diagnosis of both Atherosclerosis and AD, which effect hundreds of millions, is still limited by these two factors. Atherosclerosis is largely diagnosed decades after its onset with imaging lacking the resolution to directly identify critical disease components like vulnerable plaque with thin fibrous caps over a fibroatheroma (TCFA). Changes in the brain due to AD may begin 20 years before onset of disease symptoms and current diagnoses. Current diagnosis relies on low quality, late stage biomarkers like memory or motor function loss. In this work we have developed and used artificial intelligence methods to diagnose early onset of heart disease, cancer and Alzheimer’s with high sensitivity and specificity using Optical Coherence Tomography technology, surpassing the state of the art. Optical coherence tomography (OCT) can provide higher quality image data for Atherosclerosis and AD. OCT offers an axial resolution between 1-15 μm—one or two orders of magnitudes finer than conventional ultrasound, CT, and MRI (50-200 μm) while maintaining an imaging depth an order magnitude greater than microscopy. OCT can enable identification of key pathological features directly, like TCFA with a cap thickness of 65 μm. Despite these advantages, the complexity of OCT images makes interpretation difficult and volume of image data constrains real time analysis. Implementing Artificial Intelligence (AI) has the potential to make analysis of OCT images more accurate, consistent and rapid and reveal otherwise unseen features. AI can leverage machine learning and neural networks to learn optimal image features for each arterial tissue type. Furthermore, AI can be trained on ground truth guided by histology, the clinical gold standard, to identify commonly mistaken morphologies like TCFA and macrophages. While, various IVOCT plaque-classification approaches have been developed, they are poor medical diagnostics lacking in precision, reproducibility, and speed. In particular, the development of these approaches have been limited by a lack of comparison to histology (precision, reproducibility), or are not built using clinically standard IVOCT devices, require user-selected regions of interest (speed), or lack sensitivity to detect lipidic or calcified tissue (precision) (1,2,3,4). Expert human readers cannot reliably distinguish macrophages from TCFA and importantly are too slow for clinically relevant analysis of the hundreds of frames in an artery image. AI is well suited for medical diagnostics, as its training on gold standard data and optimized learning provides excellent precision. Lack of inter-operator variability with AI enhances reproducibility and GPU-powered AI provides a faster turn-around time compared to expert human readers, with near real-time results. In this dissertation I develop an automated histology validated plaque classification AI that identifies in IVOCT images three fundamental atherosclerotic tissue types: lipidic, fibrous, and calcific. Numerous studies (5) have demonstrated that neuropathology also presents in the retina, retinal OCT imaging is useful in characterizing and possibly providing early detection — the eye serving as a window to the brain. However, state-of-the-art retinal OCT analysis still focuses on macroscopic tissue features, like retinal nerve fiber layer thinning, which occur late in disease progression. A need is recognized to identify cellular biomarkers of neuropathology that occur early in AD. Work by Barsoum et al (5) have shown mitochondria as a promising early stage biomarker. Mitochondria are less tightly clustered and undergo fission during neurodegeneration. Light scattering theory (6) suggests that the angle distribution of back scattered light from mitochondria will differ between healthy and fission states. Thus, the dissertation evaluates whether a scattering angle resolved OCT (SAROCT) system can identify mitochondrial fission, a potential early stage AD biomarker. Studies by Gardner et al (6) have also demonstrated that SAROCT when combined with statistical Burr distribution fitting can identify retinal vascularity, another neuropathology biomarker. This dissertation extends this work by incorporating distribution fitting over a pixel panning window and convolutional neural networks to generate full angiography images from SAR OCT.Item Mathematical modeling of coupled drug and drug-encapsulated nanoparticle transport in patient-specific coronary artery walls(2009-12) Hossain, Shaolie Samira; Hughes, Thomas J. R.A vast majority of heart attacks occur due to rapid progression of plaque buildup in the coronary arteries that supply blood to the heart muscles. The diseased arteries can be treated with drugs delivered locally to vulnerable plaques—ones that may rupture and release emboli, resulting in the formation of thrombus, or blood clot that can cause blockage of the arterial lumen. In designing these local drug delivery devices, important issues regarding drug distribution and targeting need to be addressed to ensure device design optimization as physiological forces can cause the local concentration to be very different from mean drug tissue concentration estimated from in vitro experiments and animal studies. Therefore, the main objective of this work was to develop a computational tool-set to support the design of a catheter-based local drug delivery system that uses nanoparticles as drug carriers by simulating drug transport and quantifying local drug distribution in coronary artery walls. Toward this end, a three dimensional mathematical model of coupled transport of drug and drug-encapsulated nanoparticles was developed and solved numerically by applying finite element based isogeometric analysis that uses NURBS-based techniques to describe the artery wall geometry. To gain insight into the parametric sensitivity of drug distribution, a study of the effect of Damkohler number and Peclet number was carried out. The tool was then applied to a three-dimensional idealized multilayered model of the coronary artery wall under healthy and diseased condition. Preliminary results indicated that use of realistic geometry is essential in creating physiological flow features and transport forces necessary for developing catheter-based drug delivery design procedures. Hence, simulations were run on a patient-specific coronary artery wall segment with a typical atherosclerotic plaque characterized by a lipid pool encased by a thin fibrous cap. Results show that plaque heterogeneity and artery wall inhomogeneity have a considerable effect on drug distribution. The computational tool-set developed was able to successfully capture trends observed in local drug delivery by incorporating a multitude of relevant physiological phenomena, and thus demonstrated its potential utility in optimizing drug design parameters including delivery location, nanoparticle surface properties and drug release rate.Item Psychosocial stress, hypothalamic pituitary adrenal (HPA) axis function, and cardiometabolic health(2019-06-20) Lehrer, Henry Matthew; Steinhardt, Mary; Davis, Jaimie; Maslowsky, Julie; Beretvas, Susan; Bray, Molly; Bartholomew, JohnThis dissertation examines the contribution of various psychological and social stressors to indicators of cardiovascular and metabolic function and disease. It also investigates the role of altered hypothalamic pituitary adrenal (HPA) axis activity as a mechanism by which psychosocial stress may influence cardiometabolic health. Grounded in the framework of the allostatic load model, studies 1-3 build on each other to assess the interplay between various types of stress, HPA axis indicators, and cardiometabolic health outcomes among diverse populations. Study 1 examines the association of perceived everyday discrimination and hair cortisol concentration, a stable indicator of retrospective cortisol output indexed over several months. The analyses focus on racial group differences, finding that perceived discrimination scores were associated with elevated hair cortisol concentration for African American adults but not White adults. Given that both groups reported similar discrimination frequency scores, this finding suggests that more qualitative characteristics of discrimination may be particularly salient to HPA axis output among African Americans. Study 2 moves one step past the stress-HPA axis association by examining the role that elevated HPA axis activity plays in the association between perceived stress and metabolic syndrome severity. This study found that psychological resilience protected against the association of elevated perceived stress with increased metabolic syndrome severity via elevated hair cortisol concentration. Study 3 uses the same resilience-based framework as study 2, but does so in a national longitudinal cohort of U.S. adults. Using daily diary entries and salivary cortisol analysis, this study examines unique effects of daily stressor frequency and severity on cardiovascular and metabolic disease prevalence 5-8 years later. Study 3 also tests whether flattened diurnal salivary cortisol slopes mediator effects of stressor frequency and severity on cardiometabolic conditions, and examines a latent resilience resources variable as a potential buffer of the daily stress-cardiometabolic disease relationship. Findings indicate that greater perceived stressor severity and flattened diurnal cortisol slopes predict greater cardiometabolic disease prevalence later in life. Taken together, this collection of studies provides evidence supporting the contribution of greater psychosocial stress to impaired cardiovascular and metabolic health, and suggests that the HPA axis plays a significant role.