No abstract

Purpose To determine the frequency of fetal infection as well as adverse pregnancy outcomes following antenatal hyperimmunoglobulin (HIG) treatment for primary cytomegalovirus (CMV) infection in pregnancy. Methods In our observational cohort study, data from 46 women with a primary CMV infection during pregnancy were evaluated. Primary CMV infection was defined by seroconversion or the presence of CMV-IgM and low CMV-IgG avidity. All women received at least two or more infusions of HIG treatment (200 IU/kg). Congenital CMV infection (cCMV) was diagnosed by detection of CMV in amniotic fluid and/or neonatal urine. We compared the rate of maternal–fetal transmission from our cohort to data without treatment in the literature. The frequency of adverse pregnancy outcomes was compared to those of live-born infants delivered in our clinic. Results We detected 11 intrauterine infections in our cohort, which correlates to a transmission rate of 23.9%. Compared to the transmission rate found in cases without treatment (39.9%), this is a significant reduction (P = 0.026). There were no adverse pregnancy outcomes in our cohort. The mean gestational age at delivery was 39 weeks gestation in treatment and control group. Conclusion The administration of HIG for prevention of maternal–fetal CMV transmission during pregnancy seems safe and effective.

Preeclampsia, a multisystem disorder in pregnancy, is still one of the main causes of maternal morbidity and mortality. Due to a lack of a causative therapy, an accurate prediction of women at risk for the disease and its associated adverse outcomes is of utmost importance to tailor care. In the past two decades, there have been successful improvements in screening as well as in the prediction of the disease in high-risk women. This is due to, among other things, the introduction of biomarkers such as the sFlt-1/PlGF ratio. Recently, the traditional definition of preeclampsia has been expanded based on new insights into the pathophysiology and conclusive evidence on the ability of angiogenic biomarkers to improve detection of preeclampsia-associated maternal and fetal adverse events.However, with the widespread availability of digital solutions, such as decision support algorithms and remote monitoring devices, a chance for a further improvement of care arises. Two lines of research and application are promising: First, on the patient side, home monitoring has the potential to transform the traditional care pathway. The importance of the ability to input and access data remotely is a key learning from the COVID-19 pandemic. Second, on the physician side, machine-learning-based decision support algorithms have been shown to improve precision in clinical decision-making. The integration of signals from patient-side remote monitoring devices into predictive algorithms that power physician-side decision support tools offers a chance to further improve care.The purpose of this review is to summarize the recent advances in prediction, diagnosis and monitoring of preeclampsia and its associated adverse outcomes. We will review the potential impact of the ability to access to clinical data via remote monitoring. In the combination of advanced, machine learning-based risk calculation and remote monitoring lies an unused potential that allows for a truly patient-centered care.