Study of feto-maternal outcome in patients with hepatitis E infection during pregnancy

Background: Hepatitis E is considered as a common cause of high maternal morbidity and mortality particularly in third trimester and also high perinatal morbidity and mortality. Thus, this study is conducted to evaluate the feto-maternal outcome in patients infected with hepatitis E during pregnancy.Methods: It is a retrospective observational study conducted in department of obstetrics and gynecology at L. G. hospital. Fifty pregnant women with clinical hepatitis in third trimester of pregnancy were included in this study and thorough investigation were carried out. Patients were monitored till postpartum period and fetal monitoring data were collected from neonatal ICU.Results: In this study, majority of pregnant patients with hepatitis B were admitted during monsoon season suggests that HEV outbreaks are more common during monsoon months. Majority of the patients (70%) were emergency cases. Majority of these patients (82%) were belonged to lower socio-economic class. Co-infection with HAV was in 2% and with HBV in 4%. S. bilirubin >15 mg/dl in 16% of patients. PT and APTT were raised in 28% of patients. FDP was raised in 70% of patients. 76% were delivered vaginally and 22% were delivered by LSCS. Most common complication in HEV infected pregnant women was disseminated intravascular coagulation (DIC) (26%). Maternal mortality rate is 14%. Out of 50 patients, 88% delivered live baby, out of which 72% needed NICU admission. Perinatal mortality rate was as high as 28%.Conclusions: Hepatitis E infection and pregnancy is a deadly and fatal combination. Specifically, in 3rd trimester of pregnancy, acute hepatitis E has a grave prognosis with high maternal morbidity and mortality. Prevention is the mainstay of controlling HEV especially in developing countries.

Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters

Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.

Umbilical Cord Thrombosis: A Rare but Life-threatening Occurrence

Background: Thrombosis of umbilical vessels is a rare but life-threatening pregnancy complication. The correct diagnosis and clinical management of umbilical cord thrombosis remain a challenge. This study aimed to evaluate the meaningful clinical manifestations and features of umbilical cord thrombosis and its optimal management options.Methods: This retrospective study analyzed umbilical cord thrombosis cases enrolled from January 1, 2011, to May 31, 2021. Data were collected from medical archives where the diagnoses of all patients were established by histopathology.Results: A total of 66 patients with umbilical cord thrombosis were enrolled, including 20 patients with intrauterine fetal death and 6 with fatal labor induction in the second trimester. The overall incidence of umbilical cord thrombosis was 0.05% (66/123,746), and the incidence increased significantly in the last 2 years, reaching 0.19% in 2021. The chief complaint was decreased fetal movement (25/60) or nonreactive nonstress test (NST) (19/40). In the 20 patients with intrauterine stillbirth, 8 patients had ignored fetal movement and were referred to the hospital because of ultrasound findings of intrauterine stillbirth. Five patients were misdiagnosed with a single umbilical artery on prenatal ultrasound. Twenty patients underwent emergency cesarean section due to decreased fetal movement and unresponsive fetal monitoring; all neonates were alive. The gross examination of the placenta and cord revealed umbilical cord abnormalities in 26 patients (39.4%, 26/66). The pathological examination revealed venous, venous and arterial, and arterial thrombosis in 74.2%, 12.1%, and 13.6% of patients, respectively.Conclusions: The main manifestation of umbilical cord thrombosis was decreased or disappeared fetal movement. The importance of self-counting fetal movement should be emphasized to patients. The abnormalities in the umbilical cord are the main cause of this phenomenon. Focus on counting fetal movements, electronic fetal monitoring, and specific signs during a prenatal ultrasound can help early identification of umbilical cord thrombi. Emergency cesarean section is recommended to reduce the risk of interrupting the umbilical cord blood flow.

Pregnancy and COVID-19

Pregnancy is a physiological state that alters the body’s response to infections. COVID-19 has been found to cause severe disease in pregnancy with morbidity and mortality that is higher than in non-pregnant adults. There is risk of transmission of SARS-CoV2 infection to fetus during ante-natal period, intra-partum and post-delivery from an infected mother. It is necessary to provide an un-interrupted ante-natal care and delivery services to pregnant women during the pandemic. Tele-consultation is important modality to reduce the physical exposure of pregnant women to the hospital environment and should be utilised. Screening, isolation, testing and treatment for SARS-CoV2 infection in pregnant women should follow the local guidelines and remain essentially the same as in non-pregnant adults. Admission, if required, should be in a facility that can provide obstetric maternal and fetal monitoring in addition to care for COVID-19 illness. Use of nitrous oxide and inhalational oxygen for fetal indication should be avoided during labor. Second stage of labor is considered an aerosol generating procedure and should be managed with adequate precautions. Mode of delivery should be as per obstetric indications. Regional anaesthesia should be preferred during caesarean. COVID-19 is not a contra-indication to breast feeding. For antenatal women, COVID-19 vaccination can be considered after shared decision making.

Detection of Preventable Fetal Distress During Labor From Scanned Cardiotocogram Tracings Using Deep Learning

Despite broad application during labor and delivery, there remains considerable debate about the value of electronic fetal monitoring (EFM). EFM includes the surveillance of fetal heart rate (FHR) patterns in conjunction with the mother's uterine contractions, providing a wealth of data about fetal behavior and the threat of diminished oxygenation and cerebral perfusion. Adverse outcomes universally associate a fetal injury with the failure to timely respond to FHR pattern information. Historically, the EFM data, stored digitally, are available only as rasterized pdf images for contemporary or historical discussion and examination. In reality, however, they are rarely reviewed systematically or purposefully. Using a unique archive of EFM collected over 50 years of practice in conjunction with adverse outcomes, we present a deep learning framework for training and detection of incipient or past fetal injury. We report 94% accuracy in identifying early, preventable fetal injury intrapartum. This framework is suited for automating an early warning and decision support system for maintaining fetal well-being during the stresses of labor. Ultimately, such a system could enable obstetrical care providers to timely respond during labor and prevent both urgent intervention and adverse outcomes. When adverse outcomes cannot be avoided, they can provide guidance to the early neuroprotective treatment of the newborn.