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

Tools

Nyagaka, Felix and Csoban, Tunde and Gichere, Ingrid and Mwaniki, Mukaindo and Kavanagh, Kimberley and Magee, Laura A. and von Dadelszen, Peter and Oindi, Felix (2026) 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. BMC Pregnancy and Childbirth. ISSN 1471-2393 (https://doi.org/10.1186/s12884-026-09585-1)

[thumbnail of s12884-026-09585-1_reference] Text. Filename: s12884-026-09585-1_reference.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 1 January 2099.
Download (861kB) | Request a copy

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.

ORCID iDs

Nyagaka, Felix, Csoban, Tunde ORCID logoORCID: https://orcid.org/0000-0002-0498-3732, Gichere, Ingrid, Mwaniki, Mukaindo, Kavanagh, Kimberley ORCID logoORCID: https://orcid.org/0000-0002-2679-5409, Magee, Laura A., von Dadelszen, Peter and Oindi, Felix;
CORE (COnnecting REpositories)