Objective To evaluate the application of artificial intelligence (AI), i.e. deep learning and other machine‐learning techniques, to amniotic fluid (AF) metabolomics and proteomics, alone and in combination with sonographic, clinical and demographic factors, in the prediction of perinatal outcome in asymptomatic pregnant women with short cervical length (CL). Methods AF samples, which had been obtained in the second trimester from asymptomatic women with short CL (< 15 mm) identified on transvaginal ultrasound, were analyzed. CL, funneling and the presence of AF ‘sludge’ were assessed in all cases close to the time of amniocentesis. A combination of liquid chromatography coupled with mass spectrometry and proton nuclear magnetic resonance spectroscopy‐based metabolomics, as well as targeted proteomics analysis, including chemokines, cytokines and growth factors, was performed on the AF samples. To determine the robustness of the markers, we used six different machine‐learning techniques, including deep learning, to predict preterm delivery < 34 weeks, latency period prior to delivery < 28 days after amniocentesis and requirement for admission to a neonatal intensive care unit (NICU). Omics biomarkers were evaluated alone and in combination with standard sonographic, clinical and demographic factors to predict outcome. Predictive accuracy was assessed using the area under the receiver–operating characteristics curve (AUC) with 95% CI, sensitivity and specificity. Results Of the 32 patients included in the study, complete omics, demographic and clinical data and outcome information were available for 26. Of these, 11 (42.3%) patients delivered ≥ 34 weeks, while 15 (57.7%) delivered < 34 weeks. There was no statistically significant difference in CL between these two groups (mean ± SD, 11.2 ± 4.4 mm vs 8.9 ± 5.3 mm, P = 0.31). Using combined omics, demographic and clinical data, deep learning displayed good to excellent performance, with an AUC (95% CI) of 0.890 (0.810–0.970) for delivery < 34 weeks' gestation, 0.890 (0.790–0.990) for delivery < 28 days post‐amniocentesis and 0.792 (0.689–0.894) for NICU admission. These values were higher overall than for the other five machine‐learning methods, although each individual machine‐learning technique yielded statistically significant prediction of the different perinatal outcomes. Conclusions This is the first study to report use of AI with AF proteomics and metabolomics and ultrasound assessment in pregnancy. Machine learning, particularly deep learning, achieved good to excellent prediction of perinatal outcome in asymptomatic pregnant women with short CL in the second trimester. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

Objective  To determine independent perinatal associations of anxiety and depression in women who were and were not treated with psychotropic drugs in comparison to unaffected pregnancies. Study Design  From 2013 to 2014, 978 (6.3%) cases of anxiety/depression, of which 35% used psychotropic drugs, were compared with 14,514 (93.7%) unaffected pregnancies using logistic regression. Results  Subjects were more likely to be Non-Hispanic Whites, use tobacco and illegal substances, be unmarried, use public insurance, and have medical complications of pregnancy. For independent maternal outcomes, untreated anxiety/depression was associated with labor induction (adjusted odds ratio [aOR] = 2.02), cesarean deliveries (aOR = 1.69), longer length of stay (aOR = 1.96), readmission (aOR = 2.40), fever (aOR = 2.03), magnesium exposure (aOR = 1.82), and postpartum hemorrhage (aOR = 2.57), whereas treated cases were associated with increased blood transfusion (aOR = 4.81), severe perineal lacerations (aOR = 2.93), and postpartum hemorrhage (aOR = 3.85), but decreased risk of cesarean deliveries (aOR = 0.59). Independent neonatal outcomes included small for gestational age (aOR = 3.04), meconium-stained fluid (aOR = 1.85; 2.61), respiratory failure (aOR = 5.84), neonatal adaptation syndrome (aOR = 11; 10.2), and neonatal seizures (aOR = 12.3) in treated cases, whereas untreated cases were associated with hypoxia (aOR = 2.83), low Apgar score (aOR = 3.82), and encephalopathy (aOR = 18.3). Exposure to multiple psychotropic medications independently increased the risk of neonatal adaptation syndrome, neonatal length of stay, and hypoglycemia. Conclusion  Untreated cases were associated with increased maternal adverse outcomes, whereas treated cases were associated with more adverse neonatal outcomes when compared with unaffected pregnancies.

Objectives Fetal growth evaluation is an essential component of pregnancy surveillance. There have been several methods used to construct growth charts. The conventional charts used in current daily practice are based on small numbers and traditional statistical methods. The purpose of this study was to improve fetal biometric charts based on a much larger number of observations with an alternative statistical method: quantile regression analysis. A comparison between the charts is presented. Methods During the 12 years of study, 17,708 sonographic examinations of pregnant women from the north of Israel, between 12 and 42 weeks of pregnancy, were performed. Fetal measurements were obtained by several operators using various equipment and included head circumference, abdominal circumference, and femur length. Results Growth charts were established based on these measurements. Conclusions In this study, we constructed biometric growth charts using a large cohort of pregnant women. These charts offer the advantages of specific estimated regression parameters for each specified percentile, thus better defining the normal range. We suggest using these new charts in routine daily obstetric practice.

Carbon monoxide poisoning requires a high degree of suspicion. Diagnosis is made based on initial history and physical evaluation and assessment of environmental CO levels; presenting carboxyhemoglobin levels may be poor indicators of severity of disease. Oxygen therapy should be initiated promptly in all possible cases with consideration of hyperbaric oxygen therapy in cases of significant maternal exposure. Treatment requires a longer duration for fetal CO elimination than in the nonpregnant patients. Importantly, practitioners should educate pregnant patients on prevention.

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Diabetes was protective of neonatal hypoglycemia, which may be explained by optimum maternal glucose management; nevertheless macrosomia was independently predictive of neonatal hypoglycemia. Cesarean section and decreasing gestational age were the most consistent independent risk factors followed by treatment for chorioamnionitis and SGA. Further studies to evaluate these observations and develop preventive strategies are warranted.

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