2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing fetal heart development but they warrant revalidation and adjustment. 377 normal fetuses with healthy mothers, 98 normal fetuses with diabetic mothers, and 37 fetuses with cardiomyopathy and diabetic mothers had their cardiac structural dimensions, cardiothoracic ratio, valve flow velocities, and heart rates measured by fetal ECHO in a retrospective chart review. Cardiac features were fitted to linear functions, with respect to gestational age, femur length, head circumference, and biparietal diameter and z-scores were created to model normal fetal growth for all parameters. These z-scores were used to assess what metrics had no difference in means between the normal fetuses of both healthy and diabetic mothers but differed from those diagnosed with cardiomyopathy. It was found that functional metrics like mitral and tricuspid E wave and pulmonary velocity could be important predictors for cardiomyopathy when fitted by gestational age, femur length, head circumference, and biparietal diameter. Additionally, aortic and tricuspid annulus diameters when fitted to estimated gestational age showed potential to be predictors for fetal cardiomyopathy. While the metrics overlapped over their full range, combining them together may have the potential for predicting cardiomyopathy in utero. Future directions of this study will explore creating a classifier model that can predict cardiomyopathy using the metrics assessed in this study.

2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing fetal heart development but they warrant revalidation and adjustment. 377 normal fetuses with healthy mothers, 98 normal fetuses with diabetic mothers, and 37 fetuses with cardiomyopathy and diabetic mothers had their cardiac structural dimensions, cardiothoracic ratio, valve flow velocities, and heart rates measured by fetal ECHO in a retrospective chart review. Cardiac features were fitted to linear functions, with respect to gestational age, femur length, head circumference, and biparietal diameter and z-scores were created to model normal fetal growth for all parameters. These z-scores were used to assess what metrics had no difference in means between the normal fetuses of both healthy and diabetic mothers, but differed from those diagnosed with cardiomyopathy. It was found that functional metrics like mitral and tricuspid E wave and pulmonary velocity could be important predictors for cardiomyopathy when fitted by gestational age, femur length, head circumference, and biparietal diameter. Additionally, aortic and tricuspid annulus diameters when fitted to estimated gestational age showed potential to be predictors for fetal cardiomyopathy. While the metrics overlapped over their full range, combining them together may have the potential for predicting cardiomyopathy in utero. Future directions of this study will explore creating a classifier model that can predict cardiomyopathy using the metrics assessed in this study.

Young adults with type one diabetes mellitus (T1DM) face unique challenges in managing their chronic disease. While simultaneously navigating major life transitions and becoming fully responsible for their diabetes-self management behaviors (DSMB), social support can be an integral part of disease management. Many young adults enroll in college where student organizations are prevalent including diabetes related social groups on some campuses, which provide a rich source of social support for students with diabetes as they transition to greater independence in diabetes management. This study used descriptive analysis and personal network analysis (PNA) to investigate which aspects of being a part of a diabetes related social group and personal networks, in general, are pertinent to successful diabetes management, measured by a Diabetes Self-Management Questionnaire (DSMQ) among 52 young adults with T1DM. The majority of respondents indicated that since being a part of College Diabetes Network (CDN) or another diabetes-related social group, they increased time spent paying attention to, and felt more empowered to make changes to their diabetes management routine, and they were able to generally take better care of their diabetes. Half of respondents noticed their health improved since joining, and over half felt less burdened by their diabetes. Though no personal network measures were highly correlated with higher Diabetes Self-Management Scores, the degree to which health matters were discussed within their personal network was the most associated personal network measure. Our findings help contextualize the ways in which young adults’ DSMB are influenced by participation in diabetes- related social groups, as well as introduce the use of personal network analysis in gauging important aspects of social capital and support in young adults with chronic disease.