title: Predicting risk for large-for-gestational age neonates at term: a population-based Bayesian theorem study.
abstract: OBJECTIVE
  To evaluate the prediction of large-for-gestational age (LGA) term neonates using the routine third-trimester ultrasound examination and to investigate whether the prediction could be further improved by adding information on maternal characteristics.
  
  
  METHODS
  Information on 56,792 singleton term pregnancies with a routine ultrasound examination at 32-34 weeks' gestation was retrieved from a population-based perinatal register. Estimated fetal weights (FW) were expressed as gestational age-specific standard deviation scores (Z-scores). The prediction of LGA was assessed by receiver-operating characteristics (ROC) curves, with LGA defined as birth weight Z-score > + 2. The data set with complete clinical information (n = 48,809) was divided into a development and a validation set. Using the development set, multiple logistic regression analysis was performed to identify maternal characteristics associated with LGA. The odds ratios obtained were converted into likelihood ratios. These were then applied to the validation set and the probability for LGA for each infant was estimated using the Bayesian theorem.
  
  
  RESULTS
  The FW Z-score showed a high predictive ability for LGA (area under the ROC curve (AUC) 0.89 (95% CI, 0.89-0.90)). Prediction was further improved by using the model that included both FW Z-scores and maternal variables (AUC 0.91 (95% CI, 0.90-0.92)) (P for difference < 10(-6) ). The corresponding AUC for a model including maternal characteristics only was 0.74 (95% CI, 0.73-0.76).
  
  
  CONCLUSIONS
  Routine third-trimester ultrasound FW estimation is effective in the prediction of LGA neonates at term. The prediction of LGA might be further improved by using a model including maternal characteristics.
authors.affiliation: Department of Obstetrics and Gynecology, Clinical Sciences Lund, Lund, Sweden. gun.lindell@med.lu.se
authors.forename: G
authors.initials: G
authors.lastname: Lindell
authors.forename: K
authors.initials: K
authors.lastname: Maršál
authors.forename: K
authors.initials: K
authors.lastname: Källén
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D005260:Female; D005320:Fetal Macrosomia / Q000000981:diagnostic imaging* / Q000453:epidemiology; D020567:Fetal Weight / Q000502:physiology; D006801:Humans; D007231:Infant, Newborn; D007232:Infant, Newborn, Diseases / Q000000981:diagnostic imaging* / Q000453:epidemiology; D011247:Pregnancy; D011263:Pregnancy Trimester, Third / Q000502:physiology*; D011379:Prognosis; D018570:Risk Assessment; D012307:Risk Factors; D013548:Sweden / Q000453:epidemiology; D016216:Ultrasonography, Prenatal