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Background: Mobile medical devices for self-patient use are a rapidly evolving section of telehealth. We examined the INSTINCT® ultrasound system, a portable, self-operated ultrasound device attach...

Background The COVID-19 pandemic is an ongoing global healthcare crisis that negatively affects pregnant women. Although patients with an acute infection during pregnancy have been widely studied, information regarding labor and delivery while infected is sparse. The aim of the study was to ascertain maternal, obstetrical, and perinatal outcomes of women who gave birth while infected with SARS-CoV-2. Methods Patients diagnosed with COVID-19 during pregnancy at a tertiary medical center in 4/20–2/21 were identified by a retrospective database search. Those with an active intrapartum SARS-CoV-2 infection were compared with those who recovered at least 10 days before labor and delivery. Results Of the 176 women included in the study, 84 had a SARS-CoV-2 infection at the time of delivery and 92 had recovered from the infection. There was no statistically significant between-group difference in mean gestational age at delivery (39 weeks for both, p = 0.71) and overall rate of cesarean delivery (26.2% vs 17.4%, respectively, p = 0.35) or non-elective cesarean delivery (10.71% vs 4.34%, respectively, p = 0.48). In the active-infection group, the rate of severe disease was 2.4%, and of critical disease (with intensive care unit admission, mechanical ventilation, and ECMO), 3.6%, compared to zero for both in the recovered group. No differences were found between the groups in adverse perinatal outcomes. Conclusion Delivery is safe and feasible in women with active SARS-CoV-2 infection. Nevertheless, we found a non-significant trend for more severe disease and for cesarean delivery and urgent cesarean delivery (for COVID-19-related indications) in women with an intrapartum SARS-CoV-2 infection.

BACKGROUND External cephalic version (ECV) is a common procedure for women carrying a breech-presenting fetus, in an effort to avoid a cesarean birth. The benefit of immediate labor induction after ECV vs. expectant management is undetermined. We aimed to evaluate whether induction of labor immediately after a successful ECV improves the chances of a vaginal delivery compared with expectant management. METHODS Retrospective analysis of 296 women who underwent successful ECV performed in term singleton gestations (2007-2018) in the Rabin Medical Center. Mode of delivery and other obstetrical and neonatal outcomes were compared between women undergoing immediate labor induction after ECV and those managed expectantly. RESULTS Of 296 women after successful ECVs, 54 (18.2%) underwent immediate labor induction and 242 (81.8%) were managed expectantly. Immediate induction was associated with higher parity (2.4 vs 1.7, P = .03) and lower mean birthweight (3128 vs 3346 g, P

Objective To evaluate body mass index (BMI) acceleration patterns in children and to develop a prediction model targeted to identify children at high risk for obesity before the critical time window in which the largest increase in BMI percentile occurs. Study design We analyzed electronic health records of children from Israel's largest healthcare provider from 2002 to 2018. Data included demographics, anthropometric measurements, medications, diagnoses, and laboratory tests of children and their families. Obesity was defined as BMI ≥95th percentile for age and sex. To identify the time window in which the largest annual increases in BMI z score occurs during early childhood, we first analyzed childhood BMI acceleration patterns among 417 915 adolescents. Next, we devised a model targeted to identify children at high risk before this time window, predicting obesity at 5-6 years of age based on data from the first 2 years of life of 132 262 children. Results Retrospective BMI analysis revealed that among adolescents with obesity, the greatest acceleration in BMI z score occurred between 2 and 4 years of age. Our model, validated temporally and geographically, accurately predicted obesity at 5-6 years old (area under the receiver operating characteristic curve of 0.803). Discrimination results on subpopulations demonstrated its robustness across the pediatric population. The model's most influential predictors included anthropometric measurements of the child and family. Other impactful predictors included ancestry and pregnancy glucose. Conclusions Rapid rise in the prevalence of childhood obesity warrant the development of better prevention strategies. Our model may allow an accurate identification of children at high risk of obesity.

Rapid rise in childhood obesity prevalence worldwide and its major implications for health warrant the development of better prevention strategies. Here, we analyzed electronic health records of children from Israel’s largest healthcare provider from 2002 to 2018 and developed a model for predicting children’s obesity. Data included demographics, anthropometric measurements, medications, diagnoses, and laboratory tests of children and their families. Obesity was defined as body mass index (BMI) ≥95th percentile for age and sex. To identify the most critical time-window in which the largest annual increases in BMI percentile occurs during early childhood, we first analyzed childhood BMI trajectories among 382,132 adolescents. Next, we devised a prediction model targeted to identify children at high risk of obesity prior to this time-window, predicting obesity at 5-6 years of age based on data from the first 2 years of life of 136,196 children. Retrospective BMI analysis revealed that among obese adolescents, the greatest acceleration in BMI percentiles occured between 2-4 years of age. Our model, validated temporally and geographically, accurately predicted obesity at 5-6 years old (auROC of 0.804). Discrimination results on subpopulations demonstrated its robustness across the pediatric population. The model’s most influential features included anthropometric measurements of the child and family. Other impactful features included ancestry and pregnancy glucose. Antibiotics exposure in utero and during the first 2 years of life had no observed impact on obesity prediction. Our model, targeted to identify children prior to the most critical time-window of BMI acceleration, may allow a more accurate identification and implementation of early prevention strategies for children at high risk of obesity and can be readily embedded into healthcare systems.

Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring1–4. GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes5,6. Here we used a machine-learning approach to predict GDM on retrospective data of 588,622 pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC) = 0.85), substantially outperforming a baseline risk score (auROC = 0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC = 0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model. Leveraging the availability of nationwide electronic health records from over 500,000 pregnancies in Israel, a machine-learning approach offers an alternative means of predicting gestational diabetes at high accuracy in the early stages of pregnancy.