Publications by authors named "Sergio Castan"
Article Synopsis
- Electronic fetal monitoring (EFM) is the standard method used to monitor fetal health during labor, and this study aimed to use machine learning to predict fetal acidemia based on EFM data.
- A case-control study involved 378 infants, using models such as logistic regression, random forest, and neural networks to analyze the data, with the random forest model showing the best performance (AUC of 0.865).
- The findings suggest that the predictive model effectively identifies acidemia and could reduce unnecessary cesarean sections while maintaining good accuracy.
Objective: To investigate the association between pre-gestational body mass index (BMI), total gestational weight gain (GWG), and/or trimester-specific weight gain (GWGT) with adverse maternal or perinatal outcomes (AMPOs).
Materials And Methods: Maternal clinical characteristics and pregnancy and perinatal outcomes were used to predict AMPOs. The predictive ability of BMI, GWG, or GWGT for AMPOs was analyzed using the area under the curve (AUC).
Objective: To examine the effect of intertwin interval on umbilical cord pH and Apgar scores of the second twin after vaginal delivery.
Methods: A retrospective study of twin deliveries at a university hospital in Spain between August 2012 and September 2017. Inclusion criteria were vaginal delivery of both twins at 32 gestational weeks or more.