Comparison of fetal weight distribution improved by paternal height by Spanish standard versus Intergrowth 21st standard

Abstract

Abstract 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.