Performance of the Pre-Eclampsia Integrated Estimate of Risk–Machine Learning (PIERS-ML) model in a Kenyan cohort of women with pre-eclampsia- a retrospective test derivation validation study

OBJECTIVE: Pre-eclampsia is a cause of significant maternal morbidity and mortality, with delivery initiating resolution. The Pre-eclampsia Integrated Estimate of RiSk-machine learning (PIERS-ML) tool provides individualized risk estimates to guide joint decision-making for women with pre-eclampsia. While it has been externally validated in the UK, our objective was to test PIERS-ML performance in Kenya. DESIGN: Retrospective cohort validation study. SETTING: Two tertiary hospitals in Nairobi, Kenya. POPULATION: Women admitted with pre-eclampsia who had not experienced any element of the main outcome measure. METHODS: Test performance was assessed by stratification capacity, area under the receiver-operator curve (AUROC), area under the precision-recall curve (AUPRC), and decision curve analysis. MAIN OUTCOME MEASURES: Any component of the PIERS primary outcome of maternal death or major maternal organ dysfunction within 48 h of admission. RESULTS: Among 2,002 women with pre-eclampsia, 408 (20.4%) experienced an adverse maternal outcome within 48 h of admission (including 4 deaths) and a further 74 (3.7%) between 3-7 days. Missingness was substantial for most laboratory variables, particularly at the public hospital. Despite this, individual level imputation enabled model assessment. PIERS-ML demonstrated good discrimination (AUROC 0.68; AUPRC 0.40) and clinically meaningful stratification: high-risk women had doubled outcome rates and the single very high-risk woman experienced an event. Decision curve analysis showed greater net benefit than treating all or none. Patterns of missingness and more severe outcomes suggested a higher risk Kenyan case mix. CONCLUSION: In a high-morbidity Kenyan cohort, the PIERS-ML tool accurately identified personalised risk in women admitted with pre-eclampsia.

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Journal
BMC Pregnancy and Childbirth
Published
2026-07-01
DOI
https://doi.org/10.1186/s12884-026-09585-1
Primary Topic
Pregnancy and preeclampsia studies
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article
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article

Performance of the Pre-Eclampsia Integrated Estimate of Risk–Machine Learning (PIERS-ML) model in a Kenyan cohort of women with pre-eclampsia- a retrospective test derivation validation study

Beth A. Payne, Paul Murray, Keith R. Walley, F. Haniff et al.
BMC Pregnancy and Childbirth
Pregnancy and preeclampsia studies
article

Performance of the Pre-Eclampsia Integrated Estimate of Risk–Machine Learning (PIERS-ML) model in a Kenyan cohort of women with pre-eclampsia- a retrospective test derivation validation study

Beth A. Payne, Paul Murray, Keith R. Walley, F. Haniff, Erika van Papendorp, Mukaindo Mwaniki, Graeme N. Smith, Felix Nyagaka, Shakila Thangaratinam, Sarah Barry, Manu Vatish, Garth Cloete, Sarka Lisonkova, Sayrin Lalji, Eleni Tsigas, Dorothy Shaw, Anne‐Marie Côté, Paula Lott, Amanda Noovao, Christopher W.G. Redman, M. Peter Moore, LJ Tang, Andrée Gruslin, Kimberley Kavanagh, Christine Biryabarema, Marshall D. Lindheimer, Dustin Dunsmuir, Kypros H. Nicolaides, Mariana Widmer, Jian Zhou, PHILLIPA M. KYLE, Mario Merialdi, K.S. Joseph, Eija Kortelainen, Hanna Karjalainend, David R. Hall, Swati Mahajan, Ziguang Qu, Gregory K. Davis, J. Wessel Ganzevoort, Jennifer A. Hutcheon, Jean‐Marie Moutquin, Claire Parker, F. Broughton Pipkin, Rozina Sikandar, S. C. Robson, Brenda Wagner, Guy A. Dumont, Felix Oindi, J. Mark Ansermino, Annettee Nakimuli, Laura A. Magee, Peter von Dadelszen, Diane Sawchuck, Joanne Lim, Rahat N. Qureshi, M. Joanne Douglas, D. Wilhelm Steyn, Baha M. Sibai, Nelson Sass, Hannele Laivuori, Tünde Montgomery-Csobán, Annie B. Ouellet, John Allotey, Kenneth I. Lim, Shereen Z. Bhutta, Pamela Loughna, Lynne Palmer, Tunde Csoban, Tao Duan, Mark A. Brown, Ranjit Akolekar, Douglas A. Woelkers, Jing Li, Alexandra L. Millman, George Tawagi, Florence Mirembe, Ingrid Gichere, Jennifer M. Menzies, D. Keith Still, Argyro Syngelaki, James J. Walker, Barbra Pullar, James A. Russell, Barry N. Walters, Olivia Ionescu, U. Vivian Ukah, Dany Hugo, Shoo K. Lee, Vivien Cao, Chris Robertson, William A. Grobman, Zulfiqar A. Bhutta, Geoffrey W. Cundiff, Henk Groen, Michael de Swiet
article en

Abstract

OBJECTIVE: Pre-eclampsia is a cause of significant maternal morbidity and mortality, with delivery initiating resolution. The Pre-eclampsia Integrated Estimate of RiSk-machine learning (PIERS-ML) tool provides individualized risk estimates to guide joint decision-making for women with pre-eclampsia. While it has been externally validated in the UK, our objective was to test PIERS-ML performance in Kenya. DESIGN: Retrospective cohort validation study. SETTING: Two tertiary hospitals in Nairobi, Kenya. POPULATION: Women admitted with pre-eclampsia who had not experienced any element of the main outcome measure. METHODS: Test performance was assessed by stratification capacity, area under the receiver-operator curve (AUROC), area under the precision-recall curve (AUPRC), and decision curve analysis. MAIN OUTCOME MEASURES: Any component of the PIERS primary outcome of maternal death or major maternal organ dysfunction within 48 h of admission. RESULTS: Among 2,002 women with pre-eclampsia, 408 (20.4%) experienced an adverse maternal outcome within 48 h of admission (including 4 deaths) and a further 74 (3.7%) between 3-7 days. Missingness was substantial for most laboratory variables, particularly at the public hospital. Despite this, individual level imputation enabled model assessment. PIERS-ML demonstrated good discrimination (AUROC 0.68; AUPRC 0.40) and clinically meaningful stratification: high-risk women had doubled outcome rates and the single very high-risk woman experienced an event. Decision curve analysis showed greater net benefit than treating all or none. Patterns of missingness and more severe outcomes suggested a higher risk Kenyan case mix. CONCLUSION: In a high-morbidity Kenyan cohort, the PIERS-ML tool accurately identified personalised risk in women admitted with pre-eclampsia.

BMC Pregnancy and Childbirth
University of Strathclyde (GB), King's College London (GB), Aga Khan University Nairobi (KE)
Gender equality
Openalex Percentile: Top 6%
Pregnancy and preeclampsia studies
DOIOpen Access PDFOpenAlex
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