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
Year: 2026
Type: article
Study design: Observational Study
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.
Source: BMC Pregnancy and Childbirth
Authors: Felix Nyagaka, Tünde Montgomery-Csobán, Ingrid Gichere, Mukaindo Mwaniki, Kimberley Kavanagh, Laura A. Magee, Peter von Dadelszen, Felix Oindi, Mark A. Brown, Gregory K. Davis, Claire Parker, Barry N. Walters, Nelson Sass, J. Mark Ansermino, Vivien Cao, Geoffrey W. Cundiff, M. Joanne Douglas, Guy A. Dumont, Dustin T. Dunsmuir, Jennifer A. Hutcheon, K. S. Joseph, Sayrin Lalji, Tang Lee, Jing Li, Joanne Lim, Kenneth I. Lim, Sarka Lisonkova, Paula Lott, Jennifer M. Menzies, Alexandra L. Millman, Lynne Palmer, Beth A. Payne, Ziguang Qu, James A. Russell, Diane Sawchuck, Dorothy Shaw, D. Keith Still, U. Vivian Ukah, Brenda Wagner, Keith R. Walley, Dany Hugo, Andrée Gruslin, George Tawagi, Graeme N. Smith, Anne-Marie Côté, Jean-Marie Moutquin, Annie B. Ouellet, Shoo K. Lee, Tao Duan, Jian Zhou, Farizah Haniff, Swati Mahajan, Amanda Noovao, Hanna Karjalainend, Eija Kortelainen, Hannele Laivuori, J. Wessel Ganzevoort, Henk Groen, Phillipa M. Kyle, M. Peter Moore, Barbra Pullar, Zulfiqar A. Bhutta, Rahat N. Qureshi, Rozina Sikandar, Shereen Z. Bhutta, Garth Cloete, David R. Hall, Erika van Papendorp, D. Wilhelm Steyn, Christine Biryabarema, Florence Mirembe, Annettee Nakimuli, John Allotey, Shakila Thangaratinam, Kypros H. Nicolaides, Olivia Ionescu, Argyro Syngelaki, Michael de Swiet, Ranjit Akolekar, James J. Walker, Stephen C. Robson, Fiona Broughton Pipkin, Pamela Loughna, Manu Vatish, Christopher W. G. Redman, Sarah J. E. Barry, Tunde Csoban, Paul Murray, Chris Robertson, Eleni Z. Tsigas, Douglas A. Woelkers, Marshall D. Lindheimer, William A. Grobman, Baha M. Sibai, Mario Merialdi, Mariana Widmer
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,290
OpenAlex ID: W7166875130
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