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The use of ultrasound and biochemical markers in prediction of prgnancy outcome

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posted on 15.12.2014, 10:30 by Claire Janine Elson
The aims of this thesis were to develop novel approaches to the management of early pregnancy complications and to identify a subgroup of women who develop various pregnancy complications in the second and third trimester. The thesis is based on four studies of women in the first trimester of pregnancy. These studies examine the value of ultrasound and serum biochemistry in the prediction of outcome in women with an anembryonic gestational sac (Chapter 4), with ectopic pregnancies (Chapter 5), with failing pregnancies (Chapter 6) and women developing later complications (Chapter 7). Statistical models comprising of logistic regression or decision tree analysis were developed for each study. In women with anembryonic gestation sacs the probability of a viable pregnancy can be calculated using a logistic regression model, decision tree analysis or progesterone alone with equal accuracy. These models were validated prospectively. Decision tree analysis was used to develop a model for the prediction of successful expectant management of ectopic pregnancy, based on both a single serum hCG measurement or using serial measurement. These models were also validated prospectively. A novel approach to the prediction of the success of expectant management of miscarriages was developed using decision tree analysis and IGFBP-1 and inhibin A. A raised serum value of 17 OH progesterone appeared to identify those women at risk of developing hypertensive disorders later in pregnancy. This thesis has developed algorithms that can be used in clinical practise to predict the success of expectant management in women with failing pregnancies. It has identified that novel biochemical markers may have a role to play in the prediction of successful expectant management of women with miscarriages and the prediction of pregnancy complications developing in the second and third trimesters.


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University of Leicester

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