Advances in preeclampsia testing

Miller, Jessica J.; Higgins, Victoria; Ren, Annie; Logan, Samantha; Yip, Paul M.; Fu, Lei

doi:10.1016/bs.acc.2023.08.004

Cited by 2 publications

(4 citation statements)

References 128 publications

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“…Substantial research has been undertaken to identify females at high risk for preeclampsia, preferably with low-cost tools that can be used widely. Groups that have evaluated the utility of clinical and laboratory biomarkers to assess risk of preeclampsia (21)(22)(23)(24) have reported moderate-high results (AUC between 0.80 and 0.90) when using such data within machine learning or neural network algorithms as shown by Jhee et al, Marić et al,Neocleous et al and Li et al (8,9,20,25) At the same time, there has been considerable interest and research into the role of PlGF and sFlt-1 in preeclampsia testing (26). Approaches based on models using PlGF alone or the ratio of sFlt-1/PlGF have shown good performance and have been implemented for short term preeclampsia risk prediction and for assisting preeclampsia diagnosis in the second and third trimesters.…”

Section: Discussionmentioning

confidence: 99%

AI-based preeclampsia detection and prediction with electrocardiogram data

Butler,

Gunturkun,

Chinthala

et al. 2024

Front. Cardiovasc. Med.

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IntroductionMore 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.MethodsTen-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.ResultsThe 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).DiscussionWe conclude that preeclampsia can be identified with high accuracy via application of AI models to ECG data.

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Section: Discussionmentioning

confidence: 99%

AI-based preeclampsia detection and prediction with electrocardiogram data

Butler,

Gunturkun,

Chinthala

et al. 2024

Front. Cardiovasc. Med.

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show abstract

Section: Introductionmentioning

confidence: 99%

“…Preeclampsia, a significant hypertensive disorder occurring during pregnancy, poses considerable risks to both maternal and fetal health globally [ 1 ]. Perinatal mortality constitutes about 15%, which includes the intra-uterine demise of the fetus (8.6%), stillbirths (2.15%), and neonatal deaths (4.3%) [ 1 ]. It is characterized by the onset of hypertension (systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg) and proteinuria (≥ 300 mg/24 hours) after the 20 th week of gestation in previously normotensive women.…”

Section: Introductionmentioning

confidence: 99%

“…It is characterized by the onset of hypertension (systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg) and proteinuria (≥ 300 mg/24 hours) after the 20 th week of gestation in previously normotensive women. Despite extensive research, the exact cause of preeclampsia remains elusive, underscoring the complexity of its pathophysiology [ 1 ]. Recent studies have highlighted the potential importance of specific serum biomarkers, notably lactate dehydrogenase (LDH) and uric acid, in understanding and managing preeclampsia.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Review on Serum Lactate Dehydrogenase (LDH) and Uric Acid in Preeclampsia: Implications for Maternal Health and Disease Severity

Deeksha,

Pajai,

Reddy Eleti

et al. 2024

Cureus

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Preeclampsia, a hypertensive disorder unique to pregnancy, remains a significant cause of maternal and fetal morbidity and mortality worldwide. Serum lactate dehydrogenase (LDH) and uric acid have garnered attention as potential biomarkers in understanding preeclampsia's pathophysiology and clinical management. Elevated LDH and uric acid levels have been associated with disease severity and adverse outcomes, highlighting their potential utility in risk stratification and guiding management strategies. This comprehensive review explores the roles of LDH and uric acid in preeclampsia, summarizing current evidence regarding their diagnostic, prognostic, and therapeutic implications. Future research directions are also discussed, including understanding and validation studies. Integrating LDH and uric acid measurements into routine clinical practice may facilitate early detection and intervention, ultimately improving outcomes for preeclamptic pregnancies. This review underscores the importance of serum biomarkers in enhancing our understanding and managing preeclampsia, aiming to optimize maternal and fetal health.

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Advances in preeclampsia testing

Cited by 2 publications

References 128 publications

AI-based preeclampsia detection and prediction with electrocardiogram data

AI-based preeclampsia detection and prediction with electrocardiogram data

A Comprehensive Review on Serum Lactate Dehydrogenase (LDH) and Uric Acid in Preeclampsia: Implications for Maternal Health and Disease Severity

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