Julianne R. Lauring

Identification of small for gestational age by population-based and customized growth charts in newborns of obese and normal-weight primiparous women

Abstract Objective: Our hypothesis was that newborns of obese mothers would be more likely to be classified as small for gestational age (SGA) by their customized growth curves than by the standard growth curves when compared to newborns of normal-weight mothers. Methods: This is a retrospective cohort of primiparous patients delivering between 1 July 2008 and 30 June 2012. Normal-weight was defined as BMI ≤25 kg/m2 and obese as BMI ≥ 30 kg/m2. Infant birth-weight was characterized as SGA or non-SGA from the Lubchenco curve, the Fenton Preterm Growth Chart, and the customized growth curve. Results: Infants were more likely to be classified as SGA on the customized curve compared with Lubchenco curve. Odds ratio was 2.8 (CI: 1.7–4.4; p = 0.001) for obese women and was 2.9 (CI: 1.7–5.1; p < 0.001) for normal-weight women. Infants were also more likely to be classified as SGA based on the customized curve compared with the Fenton Preterm Growth Curve. The odds ratio was 2.3 (CI: 1.4–3.8; p = 0.001) for obese women and was 1.5 (CI: 1.01–2.33; p = 0.04) for normal-weight women. Conclusions: Population-based curves may mask SGA in obese women. Our study demonstrates that customized growth curves identify more SGA than population-based growth curves in obese and normal-weight women.

Fetal Growth Restriction May Be Underestimated in Obese Patients

INTRODUCTION: Fetal growth restriction is associated with poor perinatal outcomes. Unfortunately, only approximately 25% of children who are born growth restricted are identified prenatally. This underidentification of fetal growth restriction by standard measures is thought to be even higher in obese patients. The low sensitivity of outdated standardized fetal growth curves has been addressed by the development of customized fetal growth curves. Customized curves define fetal growth potential based on specific maternal factors. The objective of our study is to compare the identification of fetal growth restriction using customized compared with standardized curves (Hadlock) in normal-weight and obese patients. METHODS: In this retrospective study, 150 normal-weight (body mass index [BMI] less than 25 kg/m2) and 150 obese (BMI greater than 30 kg/m2) women aged 18–50 years who gave birth between July 1, 2008, and December 31, 2012, were included. Inclusion criteria were primiparity and an estimated fetal weight from a third-trimester ultrasound scan. For each fetus, a customized fetal growth curve was created using a program from www.gestation.net. Each fetus was then classified as growth-restricted or nongrowth-restricted using the third-trimester ultrasound-estimated weight and plotting it on the Hadlock and customized curves at the same gestational age. RESULTS: Obese women were more likely to have a fetus classified as growth-restricted by a customized curve compared with standard Hadlocks curve (odds ratio [OR] 2.1, 95% confidence interval [CI] 1.4–3.2, P=.001). Normal-weight women had no difference in classification of growth restriction (OR 0.9, 95% CI 0.7–1.2, P=.41). There was a statistically significant difference between the ORs comparing BMI categories (P=.001). CONCLUSION: In obese women, customized fetal growth curves better predict fetal growth restriction compared with standard growth curves.

Management of gestational hypertension – the impact of HYPITATa

Abstract Aims: The objective of this study was to examine the impact of one trial (the HYPITAT trial) on management of gestational hypertension. Study design: This is a retrospective cohort study of 5077 patients delivered at our institution from 7/1/2008 to 6/15/2011. “Pre-HYPITAT” was defined as 7/1/2008–9/30/2009 and “Post-HYPITAT” as 10/1/2009–6/15/2011. The primary outcome is the rate of delivery intervention for gestational hypertension. Secondary maternal and neonatal outcomes were analyzed in patients with gestational hypertension only. Statistical analyses included the χ2-test, Fisher’s exact test, and the two-sample t-test. Results: The rate of delivery intervention Pre-HYPITAT was 1.9%, compared to 4% Post-HYPITAT (P<0.001). There was no significant change in secondary outcomes. Conclusion: There was a statistically significant increase in delivery intervention for gestational hypertension at our institution after the publication of the HYPITAT trial. There was no significant change in immediate maternal or neonatal outcomes for patients with gestational hypertension.

Comparison of healthcare utilization and outcomes by gestational diabetes diagnostic criteria

Abstract Objective: To compare healthcare utilization and outcomes using the Carpenter-Coustan (CC) criteria vs. the National Diabetes Data Group (NDDG) criteria for gestational diabetes mellitus (GDM). Methods: This is a retrospective cohort study. Prior to 8/21/2013, patients were classified as “GDM by CC” if they met criteria. After 8/21/2013, patients were classified as “GDM by NDDG” if they met criteria and “Meeting CC non-GDM” if they met CC, but failed to reach NDDG criteria. “Non-GDM” women did not meet any criteria for GDM. Records were reviewed after delivery. Results: There was a 41% reduction in GDM diagnosed using NDDG compared to CC (P=0.01). There was no significant difference in triage visits, ultrasounds for growth or hospital admissions. Women classified as “Meeting CC non-GDM” were more likely to have preeclampsia than “GDM by CC” women [OR 11.11 (2.7, 50.0), P=0.0006]. Newborns of mothers “Meeting CC non-GDM” were more likely to be admitted to neonatal intensive care units than “GDM by CC” [OR 6.25 (1.7, 33.3), P=0.006], “GDM by NDDG” [OR 5.56 (1.3, 33.3), P=0.018] and “Non-GDM” newborns [OR 6.47 (2.6, 14.8), P=0.0003]. Conclusion: Using the NDDG criteria may increase healthcare costs because while it decreases the number of patients being diagnosed with GDM, it may also increase maternal and neonatal complications without changing maternal healthcare utilization.