Performance of a machine learning approach for the prediction of pre‐eclampsia in a middle‐income country

Abstract

AbstractObjectivePreeclampsia (PE) is a serious complication of pregnancy associated with maternal and fetal morbidity and mortality. Early‐onset and preterm preeclampsia have long‐term health implications for both mothers and infants. Current prediction models have limitations and may not be applicable in resource‐limited settings. Machine learning (ML) algorithms offer a potential solution for developing accurate and efficient prediction models.MethodsWe conducted a prospective cohort study in Mexico City to develop a first‐trimester prediction model for preterm preeclampsia (pPE) using ML. Maternal characteristics and locally developed multiples of the median (MoMs) for mean arterial pressure (MAP), uterine artery pulsatility index (UtA‐PI), and serum placental growth factor (PlGF) were used for variable selection. The dataset was split into training, validation, and test sets. An elastic net method was employed for predictor selection, and model performance was evaluated using area under the curve (AUC) and detection rates (DR) at 10% false positive rates (FPR).ResultsThe final analysis included 3,050 pregnant women, of whom 124 (4.07%) developed PE. The ML model showed good performance, with AUCs of 0.897, 0.963, and 0.778 for pPE, early‐onset preeclampsia (ePE), and any type of preeclampsia (all‐PE), respectively. The DRs at 10% FPR were 76.5%, 88.2%, and 50.1% for pPE, ePE, and all‐PE, respectively. The ML model outperformed previous prediction models and showed better performance than external validations of existing algorithms.ConclusionsOur ML model demonstrated high accuracy in predicting pPE and ePE using first‐trimester maternal characteristics and locally developed MoMs. The model could provide an efficient and accessible tool for early prediction of preeclampsia, facilitating timely interventions and improved maternal and fetal outcomes.This article is protected by copyright. All rights reserved.