Twenty-four versus 48 hours of expectant management in the setting of premature rupture of membranes at term among women with a prior cesarean delivery.

OBJECTIVES: Compare two approaches of expectant management in the setting of term premature rupture of membranes (PROM) among women with prior cesarean delivery. METHODS: A retrospective study conducted in a tertiary care center during 2006 to 2017, including primiparous women with singleton pregnancy and a prior low-transverse cesarean delivery who presented with term PROM and requested trial of labor after cesarean (TOLAC). Outcomes were compared between the two campuses at our center: campus A enabled expectant management up to 48 hours following PROM and campus B enabled up to 24 hours after PROM. RESULTS: A total of 158 women met the inclusion criteria and were divided into two groups. Maternal characteristics of the two groups were similar. In campus B, the rate of oxytocin administration was significantly higher as compared with campus A (46.6% versus 26.0%, P = 0.01). The rate of successful TOLAC was similar between the groups (84.0% versus 84.5%, P = 0.96). Rates of chorioamnionitis, uterine rupture, postpartum hemorrhage, recurrent hospitalization, and Apg scores did not differ between the groups. CONCLUSION: Expectant management up to 48 hours in women with TOLAC presenting with term PROM was associated with a lower rate of induction of labor and similar maternal and neonatal outcomes.

Is TSH a Reliable Indicator of Thyroid Hormone Status in Pregnancy?

Thyroid screening is recommended during pregnancy with serum thyrotropin (TSH) as the primary test. However, since human chorionic gonadotropin, the serum hallmark of pregnancy, has TSH-like effects, the adequacy of TSH as a screening tool in this constellation requires further study. This study aimed to evaluate the relationship between TSH and thyroid hormones during pregnancy in order to determine if TSH is an adequate screening tool. This was a retrospective study utilizing the Clalit Health Service, Jerusalem district database between 2006-2017 in which we analyzed TSH, FT4 and FT3 measurements from 32430 pregnancies resulting in live birth. We grouped FT4 and FT3 levels by trimester and by the following TSH levels: (1) below 0.1/0.2/0.3 mIU/l, (2) 0.1-2.5/0.2-3.0/0.3-3.0 mIU/l, (3) 2.6-4.0/3.1-4.0 mIU/l, (4) 4.1-10.0 mIU/l and (5) above 10.0 mIU/l. In the first trimester, the most important for fetal brain development, FT3 was below normal, defined as below the 2.5th percentile for the population, in only 15.3% of tests with TSH over 10 mIU/l. FT4 was below normal in only 12.8% of such tests. Similar findings were noted for the second and third trimesters. As expected, there were far less abnormal tests when lower TSH cutoff levels were tested. In conclusion, TSH levels beyond the range accepted as normal do not, in most cases, reflect abnormal thyroid hormone levels during pregnancy. TSH is not a good screen for overt hypothyroidism in pregnancy. This may be due, at least in the first trimester, to thyrotropic effects of HCG.

Prediction of neonatal subgaleal hemorrhage using first stage of labor data: A machine-learning based model.

BACKGROUND: Subgaleal hemorrhage (SGH) is a rare neonatal condition, mainly associated with instrumental delivery, mainly vacuum extractor (VE). The aim of this study was to develop a machine learning model that would allow a personalized prediction algorithm for Subgaleal hemorrhage (SGH) following vacuum extraction (VE), based on maternal and fetal variables collected during the first stage of labor. MATERIALS AND METHODS: A retrospective cohort study on data from a university affiliated hospital, recorded between January 2013 and February 2017. Balanced random forest algorithm was used to develop a machine learning model to predict personalized risk of the neonate developing SGH, in the eventuality that vacuum extraction was used during delivery. RESULTS: During the study period, 35,552 term, singleton spontaneous or induced trials of labor deliveries were included in this study. Neonatal SGH following vacuum extraction (SGH-VE) occurred in 109 cases (0.3%). Two machine learning models were developed: a proof of concept model (model A), based on a cohort limited to the (n=2955) instances of vacuum extraction, and the clinical support model (model B), based on all spontaneous or induced trials of labor (n=35,552). The models stratified parturients into high- and low-risk groups for development of SGH-VE. Model A showed a 2-fold increase in the high-risk group of parturients compared to the low risk group (OR=2.76, CI 95% 1.85-4.11). In model B, a 4-fold increase in the odds of SGH was observed in the high-risk group of parturients compared to the low risk group (OR=4.2, CI 2.2-8.1), while identifying 90.8% (99/109) of the SGH cases. CONCLUSIONS: Our machine learning-based model stratified births to high or low risk for SGH, making it an applicable tool for personalized decision-making during labor regarding the application of VE. This model may contribute to improved neonatal outcomes.