scholarly journals A Risk Calculator to Predict the Need for Maternal or Neonatal Hospital-Based Peripartum Intervention: Modelling National Surveillance Data

Author(s):  
George Zhang ◽  
Frances Wang ◽  
Ha Vi Nguyen ◽  
Jessica Bienstock ◽  
Marielle Gross

Objective: Given growing interest in alternatives to hospital birth, particularly given the COVID-19 pandemic, we developed a peripartum intervention risk calculator (PIRC) to estimate maternal and neonatal risk of requiring hospital-based peripartum intervention. Design: National cohort study. Setting: United States. Sample: Hospital births captured by the Pregnancy Risk Assessment Monitoring System from 2009-2018. Methods: The cohort was stratified by receipt of hospital-based interventions, defined as: 1) operative vaginal delivery (forceps or vacuum), 2) cesarean delivery, or 3) requiring neonatal intensive care unit admission. Gravidas with prior cesarean delivery or fetal malformation were excluded. Main Outcome Measures: Risk of requiring hospital-based intervention. Results: A total of 63,234 births were evaluated (72.6% full-term, 48.5% nulliparous) including 37.9% who received one or more hospital–based interventions. Gestational age was the most predictive factor of requiring hospital-based intervention, with lowest odds at 400/7-406/7 weeks. Previous live births (Ref: none; 1, OR 0.41; 2, OR 0.35; ≥3, OR 0.29; p<0.05 for all) were protective. Other predictors included advanced maternal age, high pre-pregnancy body mass index, maternal diabetes, maternal hypertension, and not exercising during pregnancy. The resulting seven-factor model demonstrated strong discrimination (optimism corrected C-statistic=0.776) and calibration (mean absolute error=0.009). Conclusions: We developed and validated the PIRC for predicting individualized risk for hospital-based intervention among gravidas based on seven readily accessible prenatal factors. This calculator can support personalized counseling regarding planned birth setting, helping to close a critical gap in current clinical guidance and providing an evidence-based risk assessment for those contemplating alternatives to hospital birth.

2019 ◽  
Vol 86 (2-3) ◽  
pp. 225-230
Author(s):  
Jennifer J. Barr ◽  
Lindsey Marugg

Marriage has been associated with improved pregnancy outcomes. However, as Americans become increasingly accepting of pregnancy and childbearing outside of marriage, many believe the father can support the mother without the parents being married. Some question whether the present normalization of childbearing outside of marriage will negate the protective effect of marriage on pregnancy outcomes. Data from the Centers for Disease Control and Prevention Pregnancy Risk Assessment Monitoring System were used to obtain data from a sample of 138,118 live singleton deliveries from 2012 to 2014. Odds ratios were compared between married and unmarried mothers for outcomes of preterm delivery, a small for gestational age infant, neonatal intensive care unit admission, vaginal delivery, and breastfeeding initiation. Logistic regression analyses were used to adjust for maternal age, maternal and paternal race, maternal medical comorbidities, maternal smoking status, and receipt of Medicaid. Adjusted odds ratios (AOR) showed married women had a lower risk of preterm delivery (AOR = .877, 95% confidence interval [CI; .811–.948]), a small for gestational age baby (AOR = .838, 95% CI [.726–.967]), and a neonatal intensive care admission (AOR = .808, 95% CI [.754–.866]). Women who were married were more likely to have a vaginal delivery (AOR = 1.144, 95% CI [1.085–1.211]) and to initiate breastfeeding (AOR = 1.601, 95% CI [1.490–1.719]). These data demonstrate that despite a normalization in society of childbearing outside of marriage, there continues to be an association of marriage with improved birth outcomes. Summary: Marriage is associated with a lower risk of preterm delivery, small for gestational age infants, and neonatal intensive care unit admission. These differences persist even after correcting for potentially confounding socioeconomic factors.


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