A Predictive Model of Preeclampsia from Clinical and Biochemical Data

Abstract

Introduction: Preeclampsia is one of the syndromes in pregnant women that affects at least 3 - 8% of all pregnancies.
Objective: To develop a predictive model of preeclampsia from the redox state in pregnant women, which allows to classify them in groups of preeclamptic pregnant women and healthy pregnant women.
Methods: A cross-sectional analytical study was performed. Biochemical and clinical parameters were evaluated using principal component analysis to identify the most influential variables in the occurrence of preeclampsia. Those selected as the most important variables were evaluated by Fisher's linear discriminant analysis.
Results: The main component analysis determined the variance of the data set, showing the relationship with lipid peroxidation processes, protein metabolism, tissue damage and microangiopathy, considered factors in the pathophysiology of preeclampsia. The most influential variables were used to model a discriminant function capable of classifying healthy and preeclamptic pregnant women. Wilks Lambda value and the high eigenvalue associated with the discriminant function show the discriminant power of the model. The equation obtained was validated with the Leave one out method and revealed excellent classifying power.
Conclusions: The predictive model can be considered as appropriate to classify pre-eclampsia cases, and to show biomarkers as good candidates for classification and as potential predictive indicators of pre-eclampsia.