RT Journal Article
T1 Machine Learning Algorithms Combining Slope Deceleration and Fetal Heart Rate Features to Predict Acidemia
A1 Esteban, Luis Mariano
A1 Castán, Berta
A1 Esteban Escaño, Javier
A1 Sanz Enguita, Gerardo
A1 Laliena, Antonio R.
A1 Lou Mercadé, Ana Cristina
A1 Chóliz Ezquerro, Marta
A1 Castán, Sergio
A1 Savirón Cornudella, Ricardo
AB Electronic fetal monitoring (EFM) is widely used in intrapartum care as the standard method for monitoring fetal well-being. Our objective was to employ machine learning algorithms to predict acidemia by analyzing specific features extracted from the fetal heart signal within a 30 min window, with a focus on the last deceleration occurring closest to delivery. To achieve this, we conducted a case–control study involving 502 infants born at Miguel Servet University Hospital in Spain, maintaining a 1:1 ratio between cases and controls. Neonatal acidemia was defined as a pH level below 7.10 in the umbilical arterial blood. We constructed logistic regression, classification trees, random forest, and neural network models by combining EFM features to predict acidemia. Model validation included assessments of discrimination, calibration, and clinical utility. Our findings revealed that the random forest model achieved the highest area under the receiver characteristic curve (AUC) of 0.971, but logistic regression had the best specificity, 0.879, for a sensitivity of 0.95. In terms of clinical utility, implementing a cutoff point of 31% in the logistic regression model would prevent unnecessary cesarean sections in 51% of cases while missing only 5% of acidotic cases. By combining the extracted variables from EFM recordings, we provide a practical tool to assist in avoiding unnecessary cesarean sections.
PB MDPI
SN 2076-3417
YR 2023
FD 2023-06-25
LK https://hdl.handle.net/20.500.14352/110753
UL https://hdl.handle.net/20.500.14352/110753
LA eng
NO Esteban, L.M.; Castán, B.; Esteban-Escaño, J.; Sanz-Enguita, G.; Laliena, A.R.; Lou-Mercadé, A.C.; Chóliz-Ezquerro, M.; Castán, S.; Savirón-Cornudella, R. Machine Learning Algorithms Combining Slope Deceleration and Fetal Heart Rate Features to Predict Acidemia. Appl. Sci. 2023, 13, 7478. https://doi.org/10.3390/app13137478
NO 2023 Descuento MDPI
NO Ministerio de Ciencia e Innovación (España)
NO Gobierno de Aragón
DS Docta Complutense
RD 9 abr 2025