Preeclampsia and Related Problems

Health Impacts of Pre-eclampsia: A Comprehensive Analysis of Maternal and Neonatal Outcomes

Background and Objectives: Hypertensive disorders, particularly pre-eclampsia, pose significant risks during pregnancy, affecting both maternal and neonatal health. The study aims to analyze short- and long-term health implications for mothers and their children, comparing those with pre-eclampsia to those without, to improve understanding of risk factors, diagnostic markers, and outcomes. Materials and Methods: This retrospective observational study involved 235 patients, 98 with pre-eclampsia and 137 without, monitored from 2015 to 2018 at the Obstetrics and Gynecology Department of the "Pius Brînzeu" Emergency County Clinical Hospital in Timișoara, Romania. Results: Women with pre-eclampsia were older, had higher BMIs, and more frequently had a family history of pre-eclampsia, hypertension, and diabetes. They also had lower educational and socioeconomic levels and fewer prenatal visits. Biochemical markers such as higher proteinuria, elevated sFlt-1, and lower PlGF were significant in diagnosing pre-eclampsia. Short-term maternal complications like eclampsia, HELLP syndrome, and acute kidney injury were more prevalent in the pre-eclampsia group. Neonatal outcomes included higher rates of preterm birth, low birth weight, and NICU admissions. Long-term mothers with a history of pre-eclampsia had higher incidences of chronic hypertension, cardiovascular disease, kidney problems, diabetes, and mental health disorders. Their children faced increased risks of neuropsychological delays, chronic respiratory issues, behavioral disorders, learning difficulties, and frequent infections. Conclusions: The study highlights the significant short- and long-term health impacts of pre-eclampsia on both mothers and their children. Early monitoring, intervention, and comprehensive management are crucial in mitigating these risks. These findings underscore the need for personalized care strategies to improve health outcomes for affected individuals.

AI-based preeclampsia detection and prediction with electrocardiogram data

Introduction

More than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings.

Methods

Ten-second 12-lead ECG data was obtained from two large health care settings: University of Tennessee Health Science Center (UTHSC) and Atrium Health Wake Forest Baptist (AHWFB). UTHSC data was split into 80% training and 20% holdout data. The model used a modified ResNet convolutional neural network, taking one-dimensional raw ECG signals comprising 12 channels as an input, to predict risk of preeclampsia. Sub-analyses were performed to assess the predictive accuracy for preeclampsia prediction within 30, 60, or 90 days before diagnosis.

Results

The UTHSC cohort included 904 ECGs from 759 females (78.8% African American) with a mean ± sd age of 27.3 ± 5.0 years. The AHWFB cohort included 817 ECGs from 141 females (45.4 African American) with a mean ± sd age of 27.4 ± 5.9 years. The cross-validated ECG-AI model yielded an AUC (95% CI) of 0.85 (0.77-0.93) on UTHSC holdout data, and an AUC (95% CI) of 0.81 (0.77-0.84) on AHWFB data. The sub-analysis of different time windows before preeclampsia prediction resulted in AUCs (95% CI) of 0.92 (0.84-1.00), 0.89 (0.81-0.98) and 0.90 (0.81-0.98) when tested on ECGs 30 days, 60 days and 90 days, respectively, before diagnosis. When assessed on early onset preeclampsia (preeclampsia diagnosed at <34 weeks of pregnancy), the model's AUC (95% CI) was 0.98 (0.89-1.00).

Discussion

We conclude that preeclampsia can be identified with high accuracy via application of AI models to ECG data.