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

Autor:	Montgomery-Csobán T; Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK., Kavanagh K; Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK., Murray P; Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK., Robertson C; Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK., Barry SJE; Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK., Vivian Ukah U; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, QC, Canada., Payne BA; School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada., Nicolaides KH; Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, UK., Syngelaki A; Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, UK., Ionescu O; Harris Birthright Research Centre for Fetal Medicine, King's College Hospital, London, UK; Fetal Medicine Unit, Medway Maritime Hospital, Gillingham, UK., Akolekar R; Fetal Medicine Unit, Medway Maritime Hospital, Gillingham, UK; Institute of Medical Sciences, Canterbury Christ Church University, Chatham, UK., Hutcheon JA; Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC, Canada; Institute of Women and Children's Health, University of British Columbia, Vancouver, BC, Canada., Magee LA; Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC, Canada; Department of Women and Children's Health, School of Life Course and Population Sciences, King's College London, London UK., von Dadelszen P; Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC, Canada; Department of Women and Children's Health, School of Life Course and Population Sciences, King's College London, London UK. Electronic address: pvd@kcl.ac.uk.
Jazyk:	angličtina
Zdroj:	The Lancet. Digital health [Lancet Digit Health] 2024 Apr; Vol. 6 (4), pp. e238-e250.
DOI:	10.1016/S2589-7500(23)00267-4
Abstrakt:	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. Funding: University of Strathclyde Diversity in Data Linkage Centre for Doctoral Training, the Fetal Medicine Foundation, The Canadian Institutes of Health Research, and the Bill & Melinda Gates Foundation. Competing Interests: Declaration of interests TM-C, KK, PM, SJEB, LAM, and PvD acknowledge that the intellectual property related to the PIERS-ML model has been registered, and that the inventors have no financial benefit from the use of the model based on the transfer. TM-C was funded by the University of Strathclyde, through the STRADDLE (University of Strathclyde Diversity in Data Linkage) Centre for Doctoral Training. All other authors declare no competing interests. (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
Databáze:	MEDLINE
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