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

Machine learning, advanced data analysis, and a role in pregnancy care? How can we help improve preeclampsia outcomes?

The value of machine learning capacity in maternal health, and in particular prediction of preeclampsia will only be realised when there are high quality clinical data provided, representative populations included, different health systems and models of care compared, and a culture of rapid use and application of real-time data and outcomes. This review has been undertaken to provide an overview of the language, and early results of machine learning in a pregnancy and preeclampsia context. Clinicians of all backgrounds are encouraged to learn the language of Machine Learning (ML) and Artificial intelligence (AI) to better understand their potential and utility to improve outcomes for women and their families. This review will outline some definitions and features of ML that will benefit clinician's knowledge in the preeclampsia discipline, and also outline some of the future possibilities for preeclampsia-focussed clinicians via understanding AI. It will further explore the criticality of defining the risk, and outcome being determined.

Biomarkers for Early Prediction and Management of Preeclampsia: A Comprehensive Review

Preeclampsia is a common complication of pregnancy. It is a multi-organ disorder that remains one of the main causes of maternal morbidity and mortality. Additionally, preeclampsia leads to many complications that can occur in the fetus or newborn. Preeclampsia occurs in about 1 in 20 pregnant women. This review focuses on the prediction of preeclampsia in women, using various biomarkers, in particular, a factor combining the use of soluble FMS-like tyrosinokinase-1 (sFlt-1) and placental growth factor (PlGF). A low value of the sFlt-1/PlGF ratio rules out the occurrence of preeclampsia within 4 weeks of the test result, and its high value predicts the occurrence of preeclampsia within even 1 week. The review also highlights other factors, such as pregnancy-associated plasma protein A, placental protein 13, disintegrin and metalloprotease 12, ß-human chorionic gonadotropin, inhibin-A, soluble endoglin, nitric oxide, and growth differentiation factor 15. Biomarker testing offers reliable and cost-effective screening methods for early detection, prognosis, and monitoring of preeclampsia. Early diagnosis in groups of women at high risk for preeclampsia allows for quick intervention, preventing the undesirable effects of preeclampsia. However, further research is needed to validate and optimize the use of biomarkers for more accurate prediction and diagnosis. This article aims to review the role of biomarkers, including the sFlt1/PlGF ratio, in the prognosis and management of preeclampsia.

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

Prediction of women at high risk of preeclampsia is important for prevention and increased surveillance of the disease. Current prediction models need improvement, particularly with regard to late-onset preeclampsia. Preeclampsia shares pathophysiological entities with cardiovascular disease; thus, cardiovascular biomarkers may contribute to improving prediction models. In this nested case-control study, we explored the predictive importance of mid-pregnancy cardiovascular biomarkers for subsequent preeclampsia. We included healthy women with singleton pregnancies who had donated blood in mid-pregnancy (~ 18 weeks' gestation). Cases were women with subsequent preeclampsia (n = 296, 10% of whom had early-onset preeclampsia [< 34 weeks]). Controls were women who had healthy pregnancies (n = 333). We collected data on maternal, pregnancy, and infant characteristics from medical records. We used the Olink cardiovascular II panel immunoassay to measure 92 biomarkers in the mid-pregnancy plasma samples. The Boruta algorithm was used to determine the predictive importance of the investigated biomarkers and first-trimester pregnancy characteristics for the development of preeclampsia. The following biomarkers had confirmed associations with early-onset preeclampsia (in descending order of importance): placental growth factor (PlGF), matrix metalloproteinase (MMP-12), lectin-like oxidized LDL receptor 1, carcinoembryonic antigen-related cell adhesion molecule 8, serine protease 27, pro-interleukin-16, and poly (ADP-ribose) polymerase 1. The biomarkers that were associated with late-onset preeclampsia were BNP, MMP-12, alpha-L-iduronidase (IDUA), PlGF, low-affinity immunoglobulin gamma Fc region receptor II-b, and T cell surface glycoprotein. Our results suggest that MMP-12 is a promising novel preeclampsia biomarker. Moreover, BNP and IDUA may be of value in enhancing prediction of late-onset preeclampsia.

Angiogenic Biomarkers in Preeclampsia

Preeclampsia contributes disproportionately to maternal and neonatal morbidity and mortality throughout the world. A critical driver of preeclampsia is angiogenic imbalance, which is often present weeks to months before overt disease. Two placenta-derived angiogenic biomarkers, soluble fms-like tyrosine kinase 1 (sFlt-1) and placental growth factor (PlGF), have proved useful as diagnostic and prognostic tests for preeclampsia. Recently, the U.S. Food and Drug Administration approved the sFlt-1/PlGF assay to aid in the prediction of preeclampsia with severe features among women with hypertensive disorders of pregnancy at 24-34 weeks of gestation. In this narrative review, we summarize the body of work leading to this approval and describe how the sFlt-1/PlGF ratio may be implemented in clinical practice as an adjunctive measure to help optimize care and to reduce adverse outcomes in preeclampsia.

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.

Artificial Intelligence in Early Diagnosis of Preeclampsia

BACKGROUND

Every day, 810 women die of preventable causes related to pregnancy and childbirth worldwide, and preeclampsia is among the top three causes of maternal deaths.

AIM

To develop a diagnostic system with artificial intelligence for the early diagnosis of preeclampsia.

METHODS

This retrospective study included pregnant women who were screened for the inclusion criteria on the hospital's database, and the sample consisted of the data of 1158 pregnant women diagnosed with preeclampsia and 9194 pregnant women who were not diagnosed with preeclampsia at Kahramanmaras Necip Fazıl City Hospital Gynecology and Pediatrics Additional Service Building, Kahramanmaras/Turkey. The statistical analysis was performed using the Statistical Package for social sciences (SPSS) version 22 for windows. Artificial intelligence models were created using Python, scikit-learn, and TensorFlow.

RESULTS

The model achieved 73.7% sensitivity (95% confidence interval (CI): 70.2%-77.1%) and 92.7% specificity (95% CI: 91.7%-93.6%) on the test set. Furthermore, the model had 90.6% accuracy (95% CI: 90.1% - 91.1%) and an area under the curve (AUC) value of 0.832 (95% CI: 0.818-0.846). The significant parameters in predicting preeclampsia in the model were hemoglobin (HGB), age, aspartate transaminase level (AST), alanine transferase level (ALT), and the blood group.

CONCLUSION

Artificial intelligence is effective in the prediction and diagnosis of preeclampsia.

Uterine arteries pulsatility index by Doppler ultrasound in the prediction of preeclampsia: an updated systematic review and meta-analysis

BACKGROUND

Preeclampsia is a common pregnancy complication with serious potential risks for maternal and neonatal health. Early prediction of preeclampsia is crucial for timely prevention, surveillance, and treatment to improve maternal and neonatal outcomes. This systematic review aimed to summarize the available evidence on the prediction of preeclampsia based on Doppler ultrasound of uterine arteries at different gestational ages.

METHODS

A systematic literature search and meta-analysis were conducted to evaluate the sensitivity and specificity of the pulsatility index of Doppler ultrasound of uterine arteries for predicting preeclampsia. The timing of ultrasound scans within and beyond 20 weeks of gestational age was compared to assess its effect on the sensitivity and specificity of the pulsatility index.

RESULTS

This meta-analysis included 27 studies and 81,673 subjects (3309 preeclampsia patients and 78,364 controls). The pulsatility index had moderate sensitivity (0.586) and high specificity for predicting preeclampsia (0.879) (summary point: sensitivity 0.59; 1-specificity 0.12). Subgroup analysis revealed that ultrasound scans performed within 20 weeks of gestational age did not significantly affect the sensitivity and specificity for predicting preeclampsia. The summary receiver operator characteristic curve showed the pulsatility index's optimal range of sensitivity and specificity.

CONCLUSIONS

The uterine arteries pulsatility index measured by Doppler ultrasound is useful and effective for predicting preeclampsia and should be implemented in the clinical practice. The timing of ultrasound scans at different gestational age ranges does not significantly affect the sensitivity and specificity.

Exploring the use of ChatGPT in OBGYN: a bibliometric analysis of the first ChatGPT-related publications

PURPOSE

Little is known about the scientific literature regarding the new revolutionary tool, ChatGPT. We aim to perform a bibliometric analysis to identify ChatGPT-related publications in obstetrics and gynecology (OBGYN).

STUDY DESIGN

A bibliometric study through PubMed database. We mined all ChatGPT-related publications using the search term "ChatGPT". Bibliometric data were obtained from the iCite database. We performed a descriptive analysis. We further compared IF among publications describing a study vs. other publications.

RESULTS

Overall, 42 ChatGPT-related publications were published across 26 different journals during 69 days. Most publications were editorials (52%) and news/briefing (22%), with only one (2%) research article identified. Five (12%) publications described a study performed. No ChatGPT-related publications in OBGYN were found. The leading journal by the number of publications was Nature (24%), followed by Lancet Digital Health and Radiology (7%, for both). The main subjects of publications were ChatGPT's scientific writing quality (26%) and a description of ChatGPT (26%) followed by tested performance of ChatGPT (14%), authorship and ethical issues (10% for both topics).In a comparison of publications describing a study performed (n = 5) vs. other publications (n = 37), mean IF was lower in the study-publications (mean 6.25 ± 0 vs. 25.4 ± 21.6, p < .001).

CONCLUSIONS

The study highlights main trends in ChatGPT-related publications. OBGYN is yet to be represented in this literature.