BMC pregnancy and childbirth · 2026
Development of antenatal risk prediction model for preeclampsia with severe features in Lagos, Nigeria (PreSev study): protocol of a prospective cohort study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Okusanya Babasola, Okunade Kehinde, Olumodeji Ayokunle, Adeboje-Jimoh Fatimah, Adenekan Muisi, Ojo Oluwole, Akinsanya Gbolahan, Ojo Temitope, Uthman Olalekan
Аннотация
BACKGROUND: Preeclampsia with severe features is a major contributor to maternal and perinatal morbidity and mortality, particularly in low- and middle-income countries (LMICs). Despite improvements in obstetric care, early identification of women at highest risk remains limited, and existing prediction tools lack contextual specificity for African populations.
OBJECTIVE: This study describes the protocol for the PreSev Study, which aims to develop and validate a machine-learning risk prediction model for preeclampsia with severe features among pregnant women in Lagos, Nigeria.
METHODS: This multicentre prospective cohort study will recruit at-risk pregnant women aged ≥ 18 years between 14 and 24 weeks' gestation from four major hospitals in Lagos State between September 2025 and December 2026. A total of 938 women will be enrolled for model development (training set n = 750) and independent validation (n = 486). Data on epidemiologic and clinical predictors of preeclampsia will be collected using REDCap. Five machine-learning models, random forest, extreme gradient boosting (XGBoost), multilayer perceptron (MLP), LightGBM, and glmnet, will be trained using 10-fold cross-validation and hyperparameter optimisation. Model performance will be assessed using area under the curve (AUC), calibration, decision curves, and clinical performance metrics. SHAP values will be used to evaluate feature importance. The best-performing model will be converted into an accessible online risk-prediction tool.
CONCLUSION: The PreSev Study will generate a context-specific, clinically applicable machine-learning model to support early risk stratification for preeclampsia with severe features, with potential to enhance maternal outcomes in Nigeria and similar low-resource settings.
OBJECTIVE: This study describes the protocol for the PreSev Study, which aims to develop and validate a machine-learning risk prediction model for preeclampsia with severe features among pregnant women in Lagos, Nigeria.
METHODS: This multicentre prospective cohort study will recruit at-risk pregnant women aged ≥ 18 years between 14 and 24 weeks' gestation from four major hospitals in Lagos State between September 2025 and December 2026. A total of 938 women will be enrolled for model development (training set n = 750) and independent validation (n = 486). Data on epidemiologic and clinical predictors of preeclampsia will be collected using REDCap. Five machine-learning models, random forest, extreme gradient boosting (XGBoost), multilayer perceptron (MLP), LightGBM, and glmnet, will be trained using 10-fold cross-validation and hyperparameter optimisation. Model performance will be assessed using area under the curve (AUC), calibration, decision curves, and clinical performance metrics. SHAP values will be used to evaluate feature importance. The best-performing model will be converted into an accessible online risk-prediction tool.
CONCLUSION: The PreSev Study will generate a context-specific, clinically applicable machine-learning model to support early risk stratification for preeclampsia with severe features, with potential to enhance maternal outcomes in Nigeria and similar low-resource settings.