University of Strathclyde researchers have helped build a potentially lifesaving AI model for identifying maternal risk in pregnant women with pre-eclampsia, a blood pressure condition.

Pre-eclampsia occurs in between 2% and 4% of pregnancies, and is a leading global cause of maternal morbidity and mortality. It causes an estimated 46,000 maternal deaths, and half a million stillbirths and newborn deaths a year—nearly all occurring in low- and middle-income countries.

The majority of pregnant women who develop pre-eclampsia have mild disease which ends soon after they give birth. However, around one in 10 of these women in the UK experience life-threatening or life-changing complications, such as stroke.

The new preeclampsia risk-prediction model, which is based on machine learning, has been designed to be used internationally. Named PIERS-ML (Pre-eclampsia Integrated Estimate of Risk — Machine Learning), it consolidates two previous versions of the model. The research paper on it has been published in The Lancet Digital Health journal.

Now the researchers—who are from the University of Strathclyde, as well as King’s College London—aim to develop an app for determining an individual woman’s risk of suffering adverse outcomes of pre-eclampsia after they are diagnosed.

Tunde Csobán, a research assistant in Strathclyde’s Department of Mathematics and Statistics, and lead author of the paper, said: “Pre-eclampsia presents considerable, often fatal, risks to women and their children. There is an urgent need for an effective means of assessing these risks, so that they can be managed and support can be offered.”

Speaking on the model, Dr Kimberley Kavanagh, senior lecturer in Strathclyde’s Department of Mathematics and Statistics and a co-author of the paper, said: “The model we have developed has been rigorously tested and shown to deliver fast, precise predictions of the risks, in a way which can be adapted to the individual circumstances of women around the world.

“We hope to make it available on an app which can be used in clinical settings – and potentially save many lives.”

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The project’s principal investigator, Peter von Dadelszen, Professor of global women’s health at King’s College London, further added: “We started developing a model that would objectively measure the risks of pre-eclampsia in 2001. We have now taken the data we obtained from the previous versions, fullPIERS and miniPIERS, and came up with the machine learning approach that produced the best model.”

“One of the innovative things we have done with the modelling is to include the countries’ GDPs and their national maternal mortality ratios. Including these variables means that the model automatically adjusts according to where a woman is living and makes it a globally relevant model.”

The study for the model recruited over 8,800 women from 53 maternal units in 11 different low-income, middle-income, and high-income countries: Brazil; Fiji; Pakistan; South Africa; Uganda; Australia; Canada; Finland; New Zealand; the UK and the US. The maternal risk categories were defined as very low, low, moderate, high, or very high.

The records of a further 2,900 women from south-east England were used for an external validation exercise for the model, which confirmed the performance of the main study.