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  Individual risk prediction modelling in pregnancy; the development and validation of algorithms for the future use in clinical practice

   Faculty of Biology, Medicine and Health

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  Dr Victoria Palin, Dr J Myers, Dr Glen Martin  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Clinical prediction models (CPMs) are mathematical models/algorithms that take what we know about a person and predict the probability of experiencing a subsequent outcome using a regression model or algorithm. CPMs are becoming widely adopted in clinical practice to better optimise care to help improve health outcomes. For example, the Fetal Medicine Foundation (FMF) algorithm uses a combination of maternal factors, first trimester blood tests and uterine artery dopplers to predict the risk of developing preterm pre-eclampsia. The FMF algorithm successfully identifies over 70% of cases [1] and dramatically reduces the rate of preterm pre-eclampsia with a 21-month relative effect reduction of 80% (P = 0.025) [2]. National adoption of this algorithm will have significant impact on health outcomes for mothers and baby.

Traditionally, CPMs are often developed in isolation, considering a single outcome. However, there are many medical applications where two or more outcomes are of interest. There is a need for CPMs that can accurately estimate the joint risk of multiple pregnancy outcomes simultaneously.

The overall aim of this PhD is to develop CPMs for future use in a clinical trial. The appointed PhD candidate will expand on the current risk prediction models to:

·        Develop and validate algorithms for predicting the risk of pregnancy complication and poor outcomes in isolation, incorporating maternal risk factors, observational tests results and ultrasound measurements (similar to the MFM algorithm). This work will expand on the known risk factors and biological markers [4].

·        Develop and apply methodologies to allow users to predict the (joint) risk of multiple correlated or related outcomes (e.g., composite model of poor pregnancy outcomes using multinomial logistic regression, or alternative methods) [5].

The analysis would utilise multiple data sources to allow for model development, internal and external validation of algorithms. For this PhD we are seeking a student with background in mathematics, statistics, epidemiology or computer science. This PhD will use cutting edge analytics of large-scale complex maternity data to derive impact to clinical care. The development of innovative algorithms will allow for future intervention trials to adopt first-trimester screening and risk stratification of care, providing medical professionals a means to further target pregnancies at higher risk of complication and reduce the rate of poor outcomes in high risk groups. 

Entry Requirements

Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a related area / subject. Candidates with experience in data analytics using electronic health records and/or developmental biology are encouraged to apply. Applicants interested in this project should make direct contact with the Primary Supervisor to arrange to discuss the project further as soon as possible.

How To Apply

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website ( Informal enquiries may be made directly to the primary supervisor. On the online application form select the appropriate subject title.

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website”

Biological Sciences (4) Mathematics (25) Medicine (26)

Funding Notes

Applications are invited from self-funded students. This project has a Band 2 fee. Details of our different fee bands can be found on our website


1) DOI: 10.1080/10641955.2021.1921791
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