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Objective: Our main objective was to study the influence on birth and ultrasound fetal weight of traditional factors in combination with non-traditionally explored predictors such as paternal height to provide a new customized in utero growth model. We also have compared it in our population with other customized and non-customized models. Methods: We collected 5243 cases of singleton pregnancies. An integrated study of the different variables was performed in a multivariate model to predict the fetus birthweight and customized growth curves were created following the Gardosi procedure. Results: Gestational age (P<0.001), parity (P<0.001), maternal age (P<0.001), maternal body mass index (P<0.001), maternal height (P<0.001), parental height (P<0.001), pregnancy-associated plasma protein A (PAPP-A) (P<0.001), free-beta human chorionic gonadotropin (FBHCG) (P<0.013), single umbilical artery (SUA) (P<0.009), region of origin (P<0.001), fetal sex (P<0.001), smoking (P<0.001) and pre-gestational diabetes (P<0.001) showed statistical significance. We created two growth customized models (simple and advance) that have shown good performance in predicting fetal weight at delivery and estimated by ultrasounds. The percentage of small for gestational age (SGA) cases (P10) predicted by the two models at birth were 9.9% and 9%, and for large gestational ages (LGA) (P90) we obtained values of 90.1% and 90.3%. Also, using the fetal weights measured by ultrasounds, we obtained P10 adjusted predictions, 9.2% and 9.4%, for the simpler and advance models, respectively, which were more adjusted than the 0.4, 4.6 and 10.6 obtained using the other compared models. For an easy use of models an app and a nomogram is provided. Conclusion: Using new predictor variables we implemented new growth in utero model, with predictions more adjusted to our population than Spanish customized or Intergrowth 21st models with better performance for birth and ultrasound fetal weights. We propose using a prediction model that includes parental height.

Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.

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. Exclusion criteria were monochorionic pregnancies and indication for cesarean delivery. The sample was dichotomized by intertwin interval (<10 and ≥10 minutes). Neonatal outcomes including Apgar scores and umbilical cord pH were evaluated. Results Overall, 323 twin deliveries were included. Intertwin interval was less than 10 minutes in 277 (85.6%) cases, and 10 minutes or longer in 46 (14.2%). There were no differences in maternal or obstetric characteristics between the groups. Incidence of instrumental delivery (P<0.001) and internal podalic version (P<0.001) for the second twin was higher in the longer interval group. A longer interval was associated with higher frequencies of 1‐minute Apgar score below 4 (P=0.009), 5‐minute Apgar score below 7 (P<0.001), and umbilical cord pH below 7.15 (P<0.001). Conclusion Second twins with an intertwin interval of 10 minutes or longer are more likely to have poorer Apgar scores and arterial blood pH below 7.15.

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