Application of the Electronic Nose in Predicting Preeclampsia in High-risk Pregnancies. Pilot Study

Background. Preeclampsia is a syndrome that affects 2-8 % of pregnancies worldwide and is the leading cause of maternal death. Therefore, early detection is crucial to identify women who require clinical monitoring during pregnancy and to evaluate new preventive therapies before clinical symptoms occur. Methods. The chemical fingerprints of the urine from three study groups pregnant with Preeclampsia, Healthy Pregnant (HP) and pregnant at High Risk of Preeclampsia (HRP) were evaluated using an electronic nose and the data obtained were subjected to principal component analysis (PCA), Canonical Analysis of Principal Coordinates (CAP), Partial Least Squares - Discriminant Analysis (PLS-DA) and ROC curves to determine the diagnostic power of the test. Results. A separation was found between the patients with preeclampsia and HP explaining 99% of the variability of the data. Subsequently, a CAP was obtained with a correct classification of 100%, and the PLS-DA was obtained an accuracy of 88%. With the results of axis CAP1, a ROC curve was performed resulting in a sensitivity of 100% and a specificity of 95.5%. Based on the CAP model it was found that 36% (n=9) of the HRP patients would develop preeclampsia based on the metabolites found in urine. Conclusion. metabolomics can be used as a tool for early detection of preeclampsia in high-risk pregnant women, using portable olfactory technology. (C) 2021 Instituto Mexicano del Seguro Social (IMSS). Published by Elsevier Inc. All rights reserved.

Automated summary

The study utilized electronic nose technology to identify chemical fingerprints in urine samples, effectively distinguishing patients with preeclampsia from healthy pregnant women, showing promise for early diagnosis of preeclampsia. Analysis of metabolites provided scientific basis for prevention and monitoring of high-risk pregnant women.