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

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Montgomery-Csobán, Tünde 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

Montgomery-Csobán, Tünde ORCID logoORCID: https://orcid.org/0000-0002-0498-3732, 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)