2025-26 Project (Free & Khalil & Lewin)
Risk prediction models for adverse pregnancy outcomes amongst at risk pregnant women
SUPERVISORY TEAM
Supervisor
Professor Cari Free at LSHTM
Email: caroline.free@lshtm.ac.uk
Co-Supervisor
Professor Asma Khalil at City St George’s
Email: akhalil@sgul.ac.uk
Co-Supervisor
Dr Alex Lewin at LSHTM
Email: alex.lewin@lshtm.ac.uk
PROJECT SUMMARY
Project Summary
Predicting the risk of poor pregnancy outcomes amongst pregnant women using the Hampton blood pressure monitoring app.
Doctors need to know who is likely to have a poor outcome from their pregnancy to offer timely treatments and prevention. The Hampton digital app provides an opportunity to improve current risk prediction as more data at more time points, on a larger number of variables is collected than in current clinical notes. This PhD will develop risk prediction models, explore the best threshold for high blood pressure when its measured at home during pregnancy and look at differences in uptake and use of the app as mediators of poor outcomes across social and ethnic groups. This work could have important implications for how care is provided in the future.
Project Key Words
Blood pressure, pregnancy, home monitoring, preeclampsia
MRC LID Themes
- Health Data Science
- Translational and Implementation Research
Skills
MRC Core Skills
- Quantitative skills
- Interdisciplinary skills
Skills we expect a student to develop/acquire whilst pursuing this project
Quantitative Health Data analysis skills
Causal pathway analysis machine learning patient and public involvement in research
Routes
Which route/s are available with this project?
- 1+4 = Yes
- +4 = Yes
Possible Master’s programme options identified by supervisory team for 1+4 applicants:
- LSHTM – MSc Medical Statistics
Full-time/Part-time Study
Is this project available for full-time study? Yes
Is this project available for part-time study? Yes
Location & Travel
Students funded through MRC LID are expected to work on site at their primary institution, meeting – at the minimum – the institutional research degree regulations and expectations. Students may also be required to travel for conferences (up to 3 over the duration of the studentship), and for any required training (for research degree study). Other travel expectations and opportunities highlighted by the supervisory team are noted below.
Primary location for duration of this research degree: LSHTM, London
Travel requirements for this project: occasional travel to St George’s Hospital for supervisory meetings
Eligibility/Requirements
Particular prior educational requirements for a student undertaking this project
- Minimum LSHTM institutional eligibility criteria for doctoral study.
- MSc in Epidemiology, reproductive health, medical statistics, data science or similar related field
Other useful information
- Potential Industrial CASE (iCASE) conversion? = No
PROJECT IN MORE DETAIL
Scientific description of this research project
Background
There are approximately 600,000 births per year in England and Wales, and each patient is offered at least 7 antenatal appointments (NHS, 2023). Antenatal appointments have varied functions, including information and advice, screening tests, and monitoring of maternal and fetal health.
There is a move, expedited by the COVID-19 pandemic, towards home monitoring for some aspects of antenatal care. This potentially empowers patients to become more involved in their care and reduces unnecessary hospital visits (Wilson et al., 2022).
Home monitoring blood pressure in the antenatal and post-partum periods is safe, cost saving and effective. However, trials show it does not improve blood pressure control in women with known high blood pressure, diagnose at-risk women any earlier, or improve maternal and perinatal outcomes (Kalafat et al., 2020 (Xydopoulos et al., 2019, BUMP 2). (Tucker et al., 2022).
Some uncertainties about home blood pressure monitoring remain, such as the threshold for diagnosing and treating blood pressure when measured at home during pregnancy. Some studies suggest systolic and diastolic measurements are lower when measured at home in pregnant women than in the clinic, while others suggest measurements are similar (Kalafat et al., 2018) (Tucker et al., 2017, 2018).
The UDIP study, which looked at urinary protein self-monitoring by pregnant women, showed that patients could read the test strips with similar accuracy to both antenatal health professionals and automated colourimetric readers (Jakubowski et al., 2022). Self-assessment of urine test strips is widely used within the NHS.
The HaMpton pilot study 2017 trialled monitoring blood pressure at home, combined with a series of questions to assess symptoms and urinalysis. The idea behind this project was to empower women to have more control over their care, assess the safety of this monitoring, and determine whether home monitoring offers cost savings to the health service. Hampton telemedicine care has been rolled out in more than 30 hospitals in the UK.
Significantly more data than traditional monitoring is collected using home monitoring including several blood pressure measurements, urinalysis and symptoms in high-risk women. This can be assessed against those patients who went on to develop adverse pregnancy outcomes and those who didn’t.
OBJECTIVES
To develop risk prediction models for adverse pregnancy outcomes amongst at risk pregnant women asked to use the Hampton app.
To explore the impact of different thresholds for the home monitored blood pressure on adverse outcomes. To explore the role of uptake and compliance with the Hampton care pathway/app as a mediator for adverse outcomes across social and ethnic groups.
TECHNIQUES
(a) Risk prediction models, including machine learning approaches for large numbers of predictors
(b) Causal inference for analysis of inequities as risk factors for low uptake of intervention
(c) Causal mediation analysis for low uptake as mediator of adverse outcomes
DATA AVAILABILITY
Clean data from 800 patients with 30,000 measurements and information on 20 co-variates is available for St Georges hospital. The app is rolled out in 30 hospitals so additional data could be available.
RISK MITIGATION
Data from St George’s hospital is already available.
Further reading
Relevant preprints and/or open access articles:
(DOI = Digital Object Identifier)
Additional information from the supervisory team
The supervisory team has provided a recording for prospective applicants who are interested in their project. This recording should be watched before any discussions begin with the supervisory team.
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- To apply for a studentship: MRC LID How to Apply
- Full list of available projects: MRC LID Projects
- For more information about the DTP: MRC LID About Us