Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.640012
Title: Gestational age estimation in resource poor settings
Author: Kemp, Bryn
ISNI:       0000 0004 5366 5786
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2014
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Abstract:
Background and objectives: The incidence of preterm birth (PTB), and the extent to which it results in perinatal mortality in sub-Saharan Africa (sSA) is unclear, partly because reliable estimates of gestational age (GA) at birth are lacking. This research: 1) Describes how clinical and ultrasound (US) estimates of gestational age (GA) influence PTB rates and perinatal mortality amongst a population in Kilifi, Kenya; 2) Implements a novel PTB classification system as proof of concept that such systems are feasible in low-income settings, and 3) Presents two novel approaches for estimating GA for women presenting >24 weeks’ gestation. Methods: Objectives 1) and 2) used a perinatal surveillance platform developed at the KEMRI/Wellcome Trust Research Programme in Kilifi, Kenya. Ultrasound (US) was offered for GA estimation in women ≤24 weeks’ gestation clinically. To achieve objective 3), two candidate US dating equations were derived by combining a machine learning algorithm with polynomial regression analyses. Lastly, an entirely automated model with the capacity to estimate GA using computational image analysis of the fetal cerebral cortex was developed and tested. Results: 1) Between November 2011 and July 2013, 3630 women presented for antenatal care, 1107 women had US and data were available for 950 (86%) of these. The PTB rate by US (US-GA) was 10.0% compared to 17.1% by a best clinical estimate of GA (C-GA), although the number of perinatal deaths that were preterm by US and C-GA were similar; 2) Implementation of a novel PTB classification system is feasible, and 3) New dating equations and an automated model provide estimates in the 3rd trimester with a prediction error at 34 weeks of 12.4 and 14.2 days, respectively. Conclusion: Clinical estimates of GA significantly overestimate the rate of PTBs. Despite this, the proportion of perinatal deaths in those identified as preterm by clinical and US methods was similar, suggesting that US may be a better predictor of PTB and its associated mortality. Novel dating methods can estimate GA at 34 weeks’ gestation with an error equivalent to that provided by routine clinical methods at 22 weeks’. This has important implications and may extend capacity to provide GA estimates amongst a large group of women whose birth phenotypes remain poorly described.
Supervisor: Kennedy, Stephen; Papageorghiou, Aris Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.640012  DOI: Not available
Keywords: Medical Sciences ; Epidemiology ; Obstetrics ; Gestational Age ; Fetal Medicine ; Resource-poor Setting ; Machine Learning ; Image Processing
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