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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Modeling Ascending Vaginal Infection, Preterm Birth, and Neonatal Morbidity in Mice
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Developing a predictive model for perinatal morbidity among small for gestational age infants.

Nathan R Blue1, Amanda A Allshouse1, William A Grobman2

  • 1Department of Obstetrics and Gynecology, University of Utah Health, Salt Lake City, UT, USA.

The Journal of Maternal-Fetal & Neonatal Medicine : the Official Journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
|September 28, 2021
PubMed
Summary

Predicting perinatal morbidity in small for gestational age (SGA) neonates is crucial. New models using maternal factors identified in early and late pregnancy show moderate to good predictive accuracy for SGA infant complications.

Keywords:
Perinatal morbidityfetal growth restrictionintrauterine growth restrictionperinatal mortalityrisk predictionsmall for gestational age

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Area of Science:

  • Perinatal medicine
  • Neonatology
  • Obstetrics

Background:

  • Neonates with birth weight below the 10th percentile (small for gestational age, SGA) face increased risks.
  • Current methods cannot reliably distinguish high-risk SGA neonates from constitutionally small ones.
  • Improved risk stratification tools are needed for SGA neonates.

Purpose of the Study:

  • Identify factors associated with perinatal morbidity in SGA neonates.
  • Develop predictive models for perinatal morbidity in SGA neonates.
  • Assess model performance based on information availability during pregnancy and at delivery.

Main Methods:

  • Secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (NuMoM2B).
  • Nested case-control study including SGA neonates without major congenital anomalies or aneuploidy.
  • Developed two predictive models using maternal factors available at mid-pregnancy and delivery.

Main Results:

  • Model 1 (mid-pregnancy) included BMI, stress, diastolic blood pressure, narcotic use, and uterine artery pulsatility index.
  • Model 2 (delivery) added preterm delivery, preeclampsia, and suspected fetal growth restriction to Model 1.
  • Model 2 demonstrated better predictive performance (AUC 0.84) compared to Model 1 (AUC 0.72).

Conclusions:

  • Two predictive models for perinatal morbidity in SGA neonates were developed.
  • Models showed moderate to good predictive accuracy.
  • These models can aid in the risk stratification of SGA neonates.