Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model) : a modelling study

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Csoban, Tunde and Kavanagh, Kimberley and Murray, Paul and Robertson, Chris and Barry, Sarah J.E. and Ukah, U Vivian and Payne, Beth A. and Nicolaides, Kypros H. and Syngelaki, Argyro and Ionescu, Olivia and Akolekar, Ranjit and Hutcheon, Jennifer A. and Magee, Laura A. and von Dadelszen, Peter (2024) Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model) : a modelling study. The Lancet Digital Health, 6 (4). E238-E250. ISSN 2589-7500 (https://doi.org/10.1016/S2589-7500(23)00267-4)

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Abstract

Background Affecting 2–4% of pregnancies, pre-eclampsia is a leading cause of maternal death and morbidity worldwide. Using routinely available data, we aimed to develop and validate a novel machine learning-based and clinical setting-responsive time-of-disease model to rule out and rule in adverse maternal outcomes in women presenting with pre-eclampsia. Methods We used health system, demographic, and clinical data from the day of first assessment with pre-eclampsia to predict a Delphi-derived composite outcome of maternal mortality or severe morbidity within 2 days. Machine learning methods, multiple imputation, and ten-fold cross-validation were used to fit models on a development dataset (75% of combined published data of 8843 patients from 11 low-income, middle-income, and high-income countries). Validation was undertaken on the unseen 25%, and an additional external validation was performed in 2901 inpatient women admitted with pre-eclampsia to two hospitals in south-east England. Predictive risk accuracy was determined by area-under-the-receiver-operator characteristic (AUROC), and risk categories were data-driven and defined by negative (–LR) and positive (+LR) likelihood ratios. Findings Of 8843 participants, 590 (6·7%) developed the composite adverse maternal outcome within 2 days, 813 (9·2%) within 7 days, and 1083 (12·2%) at any time. An 18-variable random forest-based prediction model, PIERS-ML, was accurate (AUROC 0·80 [95% CI 0·76–0·84] vs the currently used logistic regression model, fullPIERS: AUROC 0·68 [0·63–0·74]) and categorised women into very low risk (–LR <0·1; eight [0·7%] of 1103 women), low risk (–LR 0·1 to 0·2; 321 [29·1%] women), moderate risk (–LR >0·2 and +LR <5·0; 676 [61·3%] women), high risk (+LR 5·0 to 10·0, 87 [7·9%] women), and very high risk (+LR >10·0; 11 [1·0%] women). Adverse maternal event rates were 0% for very low risk, 2% for low risk, 5% for moderate risk, 26% for high risk, and 91% for very high risk within 48 h. The 2901 women in the external validation dataset were accurately classified as being at very low risk (0% with outcomes), low risk (1%), moderate risk (4%), high risk (33%), or very high risk (67%). Interpretation The PIERS-ML model improves identification of women with pre-eclampsia who are at lowest and greatest risk of severe adverse maternal outcomes within 2 days of assessment, and can support provision of accurate guidance to women, their families, and their maternity care providers.

ORCID iDs

Csoban, Tunde, Kavanagh, Kimberley ORCID logoORCID: https://orcid.org/0000-0002-2679-5409, Murray, Paul ORCID logoORCID: https://orcid.org/0000-0002-6980-9276, Robertson, Chris, Barry, Sarah J.E. ORCID logoORCID: https://orcid.org/0000-0003-3039-8729, Ukah, U Vivian, Payne, Beth A., Nicolaides, Kypros H., Syngelaki, Argyro, Ionescu, Olivia, Akolekar, Ranjit, Hutcheon, Jennifer A., Magee, Laura A. and von Dadelszen, Peter;
CORE (COnnecting REpositories)