ArticleBMC pregnancy and childbirth2026
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.
Article in BMC pregnancy and childbirth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivePre-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.
designRetrospective cohort validation study.
settingTwo tertiary hospitals in Nairobi, Kenya. POPULATION: Women admitted with pre-eclampsia who had not experienced any element of the main outcome measure.
methodsTest 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 measuresAny component of the PIERS primary outcome of maternal death or major maternal organ dysfunction within 48 h of admission.
resultsAmong 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.
conclusionIn a high-morbidity Kenyan cohort, the PIERS-ML tool accurately identified personalised risk in women admitted with pre-eclampsia.
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