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The world is currently affected by the invasion of a human to human highly transmissible novel corona virus classified as SARS-CoV-2. It causes a severe acute lower respiratory tract syndrome named corona virus disease . The virus is detected primarily by RT-PCR. The reproduction number (Ro) has been reported between 2.28 and 5.27]. It is beyond our objective to provide an in-depth discussion of the virus characteristics and its distinct viral clades and pathogenic behavior. On 30 January 2020 the World Health Organization (WHO) declared this outbreak a Public Health Emergency of International Concern, (PHEIC) and on 11 March 2020 WHO declared it a pandemic. There is limited information on the effect of CoVid-19 in pregnancy and the new born. We describe the details of the hospital course of the first 16 cases involving pregnant women, admitted to an urban-suburban community general hospital in Wayne County Michigan, from 26 March to 10 April 2020. At the time of this writing the Covid-19 pandemic has affected 35,291 persons in the state of Michigan (0.37%) making it the third most affected state in the USA (MDHHS). Pregnant women are believed to be at higher risk of Covid-19 infection in association with the known physiologic changes of the immune, cardiorespiratory and metabolic systems during pregnancy.

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Obesity is currently recognized as a health epidemic worldwide. Its prevalence has doubled in the last three decades. Obesity is a complex clinical picture associated with physical, physiologic, hormonal, genetic, cultural, socioeconomic and environmental factors. The rate of obesity is also increasing in the pregnant women population. Maternal obesity is associated with less than optimal obstetrical, fetal and neonatal outcomes. It is also associated with significant adverse long-term effects on both obese parturients and the infants born from obese women. A number of guidelines have been published to educate health care workers and the general population in an attempt to develop effective interventions on a large scale to prevent obesity. These guidelines are multiple, confusing and inconsistent. There are no standard recommendations regarding gestational weight gaining goals, nutrients and additional elements necessary for certain obese women who have been treated with bariatric surgical procedures, screening for metabolic diseases such as diabetes, additional preventive health care services indicated for obese women in the pregnancy planning stages, during prenatal care, in the immediate post-partum period and as a long-term approach for health preservation. In 2013, the American Medical Association supported by several US national medical specialty organizations published Resolution 420 (A-13) recognizing obesity as a disease state with multiple pathophysiological aspects requiring a range of interventions to improve its prevention and treatment. The goal of this decision was to encourage a broader spectrum of health care benefits insurance coverage for the prevention and treatment of obesity. There are a number of myths and misconceptions associated with obesity. These perspectives present our views and clinical experience with a partial review of recent bibliography addressing the associations between obese reproductive age women and their risks during pregnancy.

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Objectives To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (1/1/2018-4/7/2022). HIPPS combines: 1) an extension of a previously published pregnancy episode algorithm, 2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and 3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. Results We identified 628,165 pregnant persons with 816,471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within one week of precision for 475,433 (58.2%) episodes. 62,540 (7.7%) episodes had incident COVID-19 during pregnancy. Discussion HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. Conclusion We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data. Lay Summary The National COVID Cohort Collaborative (N3C) provides researchers a unique opportunity to use electronic health record data from more than 12 million individuals from over seventy healthcare systems across the U.S. to study the impact of COVID-19 on pregnancy and women’s health. However, doing research with electronic health record data from different sources can be challenging as data can often be reported in many ways and formats. To address this challenge, we developed an approach known as Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS) that can 1) find the start and end of a pregnancy, 2) infer whether the pregnancy resulted in a live birth or pregnancy loss, and 3) determine the gestational age at the end of pregnancy. We observed from a subset of data that our approach had high agreement with how clinicians would collect this information from electronic health records. When applying our approach on all the data in N3C, we identified 816K pregnancies from 628K individuals. Of these individuals, 62K had COVID-19 during pregnancy. Our research demonstrates that our HIPPS approach can enable COVID-19-related research in pregnancy with electronic health record data.

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