New advances in prediction and surveillance of preeclampsia: role of machine learning approaches and remote monitoring.

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.

A machine-learning-based algorithm improves prediction of preeclampsia-associated adverse outcomes.

BACKGROUND: Preeclampsia presents a highly prevalent burden on pregnant women with an estimated incidence of 2% to 5%. Preeclampsia increases the maternal risk of death 20-fold and is one of the main causes of perinatal morbidity and mortality. Novel biomarkers, such as soluble fms-like tyrosine kinase-1 and placental growth factor in addition to a wide span of conventional clinical data (medical history, physical symptoms, laboratory parameters, etc.), present an excellent basis for the application of early-detection machine-learning models. OBJECTIVE: This study aimed to develop, train, and test an automated machine-learning model for the prediction of adverse outcomes in patients with suspected preeclampsia. STUDY DESIGN: Our real-world dataset of 1647 (2472 samples) women was retrospectively recruited from women who presented to the Department of Obstetrics at the Charité - Universitätsmedizin Berlin, Berlin, Germany, between July 2010 and March 2019. After standardization and data cleaning, we calculated additional features regarding the biomarkers soluble fms-like tyrosine kinase-1 and placental growth factor and sonography data (umbilical artery pulsatility index, middle cerebral artery pulsatility index, mean uterine artery pulsatility index), resulting in a total of 114 features. The target metric was the occurrence of adverse outcomes throughout the remaining pregnancy and 2 weeks after delivery. We trained 2 different models, a gradient-boosted tree and a random forest classifier. Hyperparameter training was performed using a grid search approach. All results were evaluated via a 10 × 10-fold cross-validation regimen. RESULTS: We obtained metrics for the 2 naive machine-learning models. A gradient-boosted tree model was performed with a positive predictive value of 88%±6%, a negative predictive value of 89%±3%, a sensitivity of 66%±5%, a specificity of 97%±2%, an overall accuracy of 89%±3%, an area under the receiver operating characteristic curve of 0.82±0.03, an F1 score of 0.76±0.04, and a threat score of 0.61±0.05. The random forest classifier returned an equal positive predictive value (88%±6%) and specificity (97%±1%) while performing slightly inferior on the other available metrics. Applying differential cutoffs instead of a naive cutoff for positive prediction at ≥0.5 for the classifier's results yielded additional increases in performance. CONCLUSION: Machine-learning techniques were a valid approach to improve the prediction of adverse outcomes in pregnant women at high risk of preeclampsia vs current clinical standard techniques. Furthermore, we presented an automated system that did not rely on manual tuning or adjustments.

Fetal and maternal outcome after hyperimmunoglobulin administration for prevention of maternal-fetal transmission of cytomegalovirus during pregnancy: retrospective cohort analysis.

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.