Application of Novel Nonlinear Data Modelling and Analysis to the Study of Cervical Impedance Spectroscopy for Preterm Birth Prediction

Lead Research Organisation: University of Sheffield
Department Name: Automatic Control and Systems Eng

Abstract

Every year, globally, about 15 million babies are born before 37 weeks. Preterm birth (PTB) complications are the leading cause of death of children under 5 years, causing 1 million deaths annually globally. PTB costs the UK NHS more than £1 billion annually, 10-fold higher than for term babies. As PTB is a major public health problem with profound implications on society, being able to identify women at risk of PTB during the course of their pregnancy is crucially important, so that care measures can be employed to delay birth to reduce potential long-term disability and impairment. However, accurate identification of women at risk for prevention and mitigation remain illusory. The fundamental problem with current screening approaches is that they are unable to assess and quantify cervical tissue composition, neither are they able to discriminate the various clinical conditions that are associated with PTB.

For PTB to occur, the cervix must soften and dilate through a series of remodelling events at a molecular level. Based on this observation, a team of doctors and scientists led by Prof Dilly OC Anumba at Sheffield have revealed that women who are at high risk of PTB have lower resistance in their cervix in mid-pregnancy than women who deliver at term. This discovery motivated the MRC ECCLIPPxTM project where the Sheffield Mark V Electrical Impedance Spectroscopy (EIS) device was successfully investigated to quantify cervical remodelling remote from birth to predict PTB in a group of about 500 pregnant patients. The study has shown that EIS measured at 20-22 weeks predicts PTB (birth before 37 weeks gestation) with a sensitivity of ~70% and a specificity of ~80%. This promising performance was based only on a single derived parameter - tissue resistance over some discrete frequencies - and employed linear logistic and conventional statistical analysis. However, cervical tissue electrical properties are literally represented by the tissue impedance over wide range of frequencies, which has both a real and an imaginary part known as resistance and reactance, respectively, and can also involve the effects of nonlinearities. Therefore, comprehensively correlating all EIS screening data parameters with PTB and taking into account possibly more complicated nonlinear associations could further improve the accuracy of PTB prediction.

Motivated by these considerations, Profs Dilly OC Anumba and ZQ Lang established a collaboration which has preliminarily employed data modelling and model analysis techniques developed by Prof ZQ Lang's team to analyse the tissue impedance data over a wider range of frequencies. They have then produced a nonlinear logistic regression model which uses a nonlinear combination of the amplitude and phase of EIS impedance to predict PTB. The application of this model to a subset of the EIS data from 33 patients has achieved better PTB prediction with a sensitivity of 82% and a specificity of 85%. The encouraging outcome of this early scoping study informs this proposal.

In this project, we propose to employ advanced data processing, modelling, and analysis uniquely developed in the Department of Automatic Control and Systems Engineering at the University of Sheffield to enhance the extraction of PTB related features enabled by the pioneering Sheffield cervical impedance spectroscopy-based PTB screening devices. We will develop a novel nonlinear logistic analysis to integrate the cervical impedance spectroscopy features, demographic data, and other clinically available observations for a more informed, clinically explainable, and significantly improved PTB prediction. The achievements will be demonstrated by prediction of PTB for 700 women who will have been studied by cervical impedance spectroscopy-based PTB screening at the Sheffield Teaching Hospitals (STH) NHS Foundation Trust.

Planned Impact

Premature delivery poses substantial societal burdens on women, their families and their communities. In England and Wales 60,000 premature babies are born annually at excess costs that exceed £1bn annually. Severely preterm babies sometimes face a life time of disability and ill-heath, many parents sometimes having to give up work to care for an affected child. Improved identification of women at risk, which is expected to be achieved by the project, will enable more timely interventions to prevent or delay PTB. Such interventions include drug therapy (such as progesterone), surgical treatment (such as cervical cerclage for cervical insufficiency) and better referral pathways for better preterm birth care. The interventions will prolong the duration of pregnancy and, consequently, avoid or minimise intensive neonatal care requirements, the risk of neurological handicap, lung dysfunction, and the retinopathy of prematurity associated with prolonged oxygen administration for extreme preterm birth, making a very significant impact on realising the ambitions of "Healthy Nation" in "Improving prevention and public health" and "Optimising care through effective diagnosis, patient-specific prediction and evidence-based treatment planning". The UK scientists including the researchers at Sheffield are leading the research areas of nonlinear system modelling and frequency analysis in the world. The project will apply some unique approaches proposed by UK scientists in this niche area to resolve challenging data processing and analysis issues with PTB prediction. The achievements would offer significant added value over current healthcare solution and demonstrate the values of UK scientists' research findings in an important healthcare application. The project covers many EPSRC strategic research areas for the Healthcare Technology updated recently by EPSRC Balancing Capability activities including statistics (grow), artificial intelligence (maintain) , nonlinear systems(maintain), sensors and instrumentations (maintain), and digital signal processing (maintain), and fits perfectly with EPSRC's portfolio and strategy in these areas.

Publications

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Description We have found several effective algorithms which have potential to be applied to process the Electrical Impedance Spectroscopic (EIS) data for preterm birth prediction.
Exploitation Route The data analysis algorithms will be implemented by computer codes which will have user friendly interface and are expected to help clinicians to predict and manage preterm birth.
Sectors Healthcare

 
Description Knowledge Transfer Partnership between the University of Sheffield and Zilico Limited
Amount £190,816 (GBP)
Funding ID KTP 11443 
Organisation Innovate UK 
Sector Public
Country United Kingdom
Start 05/2019 
End 05/2021