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

Abstract

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.

Angiogenic Biomarkers in Preeclampsia

“…66 Additional work will be needed to determine how angiogenic biomarkers can be used in conjunction with remote monitoring devices and machine learning techniques to further identify high-risk patients and to decrease preeclampsia-related adverse outcomes. 67 Use of the sFlt-1/PlGF ratio is not without limitations and does not serve as a substitute for being attentive to the standard diagnostic criteria for preeclampsia with severe features (Box 1). It was approved by the FDA for use in singleton pregnancies with hypertensive disorders at 24-34 weeks of gestation, and it is not yet indicated in those with normal blood pressure, gestational age outside this window, or multifetal gestation.…”

“…66 Additional work will be needed to determine how angiogenic biomarkers can be used in conjunction with remote monitoring devices and machine learning techniques to further identify high-risk patients and to decrease preeclampsia-related adverse outcomes. 67…”

Novel Associations Between Mid-Pregnancy Cardiovascular Biomarkers and Preeclampsia: An Explorative Nested Case-Control Study

“…Due to the complex nature of preeclampsia, machine learning algorithms, trained by large data sets to recognize and predict complex patterns, may be applied when exploring the predictive performance of multiple biomarkers [ 24 , 25 ]. Therefore, we explored the individual predictive value of 92 cardiovascular biomarkers in mid-pregnancy plasma to detect subsequent preeclampsia using proteomic profiling and machine learning.…”

AI-based preeclampsia detection and prediction with electrocardiogram data

“…Gene variants associated with cardiomyopathy are also associated with preeclampsia, and prolonged QT interval, altered p -wave duration, and LV strain are more common among females with preeclampsia compared to healthy pregnancies ( 10 , 11 ). Infants with births complicated by preeclampsia are more likely to be premature, have intrauterine growth restriction and have an increased risk of death, resulting in up to 900,000 infant deaths per year ( 9 , 12 , 13 ). Identifying pregnant females at elevated risk for preeclampsia using low-cost tools may facilitate closer monitoring and timely interventions to reduce preeclampsia-related adverse events in both babies and mothers.…”