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  1. KEEP
  2. Theses and Dissertations
  3. Barrett, The Honors College Thesis/Creative Project Collection
  4. Fetal Growth Models of Cardiac Size and Function, and Prediction of Congenital Cardiomyopathy in Fetuses with Diabetic Mothers
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Fetal Growth Models of Cardiac Size and Function, and Prediction of Congenital Cardiomyopathy in Fetuses with Diabetic Mothers

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Description

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.

Date Created
2021-05
Contributors
  • Mishra, Shambhavi (Co-author)
  • Numani, Asfia (Co-author)
  • Sweazea, Karen (Thesis director)
  • Plasencia, Jonathan (Committee member)
  • Economics Program in CLAS (Contributor)
  • School of Life Sciences (Contributor)
  • Barrett, The Honors College (Contributor)
Topical Subject
  • School of Life Sciences
  • Cardiology
  • Diabetes
Resource Type
Text
Extent
17 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Barrett, The Honors College Thesis/Creative Project Collection
Series
Academic Year 2020-2021
Handle
https://hdl.handle.net/2286/R.I.63465
Level of coding
minimal
Cataloging Standards
asu1
System Created
  • 2021-04-15 12:17:16
System Modified
  • 2021-08-11 04:09:57
  •     
  • 1 year 9 months ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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