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Background <p><i>Preeclampsia with severe features</i> 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.</p> Objective <p>This study describes the protocol for the <i>PreSev</i> Study, which aims to develop and validate a machine-learning risk prediction model for <i>preeclampsia with severe features</i> among pregnant women in Lagos, Nigeria.</p> Methods <p>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 <i>n</i> = 750) and independent validation (<i>n</i> = 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.</p> Conclusion <p>The <i>PreSev</i> Study will generate a context-specific, clinically applicable machine-learning model to support early risk stratification for <i>preeclampsia with severe features</i>, with potential to enhance maternal outcomes in Nigeria and similar low-resource settings.</p>

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Development of antenatal risk prediction model for preeclampsia with severe features in Lagos, Nigeria (PreSev study): protocol of a prospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月20日

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  • Okusanya Babasola,
  • Okunade Kehinde,
  • Olumodeji Ayokunle,
  • Adeboje-Jimoh Fatimah,
  • Adenekan Muisi,
  • Ojo Oluwole,
  • Akinsanya Gbolahan,
  • Ojo Temitope,
  • Uthman Olalekan

摘要

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.