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Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers

Medicine and Health

Predictive Model for Preeclampsia Combining sFlt-1, PlGF, NT-proBNP, and Uric Acid as Biomarkers

G. C, C. M, et al.

Discover how a groundbreaking machine-learning model developed by Garrido-Giménez et al. enhances the prediction of preterm preeclampsia using key biomarkers like sFlt-1/PlGF ratio, NT-proBNP, and uric acid, demonstrating improved predictive capabilities as delivery approaches.

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Playback language: English
Abstract
This study externally validates a machine-learning model (MLM) for predicting preterm preeclampsia (PE) using sFlt-1/PlGF ratio, NT-proBNP, and uric acid. The MLM showed significantly better positive predictive value (PPV) and specificity than the sFlt-1/PlGF ratio alone, with slightly improved sensitivity and negative predictive value (NPV). The model's performance improved as the delivery date approached, particularly for early-preterm PE.
Publisher
J. Clin. Med.
Published On
Jan 05, 2023
Authors
Garrido-Giménez, C, Cruz-Lemini, M, Álvarez, F.V, Nan, M.N, Carretero, F, Fernández-Oliva, A, Mora, J, Sánchez-García, O, García-Osuna, Á, Alijotas-Reig, J
Tags
preterm preeclampsia
machine-learning model
sFlt-1/PlGF ratio
prediction
biomarkers
positive predictive value
specificity

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